· Board of Th e Egyptian Journal of Bronchology (EJB) Editor-in-Chief Tarek Safwat Ain Shams University, Egypt Associate Editors Amr Shoukri Ain Shams University, Egypt Ashraf Madko

Board of Th e Egyptian Journal of Bronchology (EJB)

Editor-in-Chief

Tarek SafwatAin Shams University, Egypt

Associate Editors

Amr ShoukriAin Shams University, Egypt

Ashraf MadkourAin Shams University, Egypt

EDITORIAL BOARD MEMBERS

INTERNATIONAL ADVISORY BOARD

Ahmed Boseila

Westfälische Wilhelms-University, Münster,

Germany

Alaa El Gendy

Florida, USA

Ali Musani

Interventional Pulmonology and

Bronchoscopy Service, USA

Atul Mehta

Cleveland, Ohio, USA

Grigoris Stratakos

Athens, Greece.

Heinrich D. Becker

Th oraxklinik at Heidelberg University, Germany

Henri G. Colt

University of California, Irvine, Orange,

CA, USA

Majdy M. Idrees

Prince Sultan Riyadh Military Medical

City, Riyadh, Saudi Arabia

Nikos Koufos

University de Bellvitge, Greece

Peter Pohunek

University Hospital Motol, Prague, Chez

Republic

Richard W. Light

Nashville, Tennessee, USA

Roland M. du Bois

Imperial College, London, UK

Semra Bilaceroglu

TRS. President, EABIP, Turkey

Spasoje Popevic

Clinical Center of, Belgrade, Serbia

Tudor Toma (PhD (Lon), FRCP)

London, UK

http://www.ejbronchology.eg.net/

Editorial Coordinator

Dr Riham HazemAin shams University, Egypt

Adel Kattab

Ain Shams University, Egypt

Ahmed Al Halfawy

Cairo University, Egypt

Ahmed El Noury


Asem Elesawy

Alfayoum University

Ashraf Hatem


Emad Koraa


Essam Gouda

Alexandria University, Egypt

Gamal El Khouly

Tanta University, Egypt

Gamal Rabie Agmy

Assuit University, Egypt

Hesham Tarraf


Hisham H. Raefat

Assistant Professor of Pulmonary Medicine,


Iman H. Galal Eldin

Professor of Pulmonary Medicine, Ain Shams

University, Egypt

Khaled Wagih


Malak Shaheen


Mamdouh Mahfouz


Medhat Abdel Khalek


Mohamed A. Tag Eldin


Mohamed Khairy

Mansoura University, Egypt

Moustafa Elshazly


Maysa Sharaf El Din


Nader Fasseeh

Alexandria University, Egypt

Yasser Mostafa


Walid El Sorougy

Helwan University, Egypt

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Th e Egyptian Journal of BronchologyThe Offi cial Journal of the Egyptian Scientifi c Society of Bronchology

Vol. (13), No. (4), October-December, 2019

Airway Diseases

Original article443 A study of plasma copeptin level as a predictor of

severity during acute exacerbation of bronchial asthma

Ahmed G. El Gazzar, Khaled M. Belal, Tarek S. Essawy,

Neveen M. Abd-Elfattah

Original article452 Impulse oscillometry usefulness in small-airway

dysfunction in asthmatics and its utility in asthma control

Ragia S. Sharshar

Original article459 Correlation between ventricular function as

assessed by echocardiography and six-minute walk test as a surrogate of functional capacity in patients with chronic obstructive pulmonary disease

Magdy M. Khalil, Hala M. Salem,

Hossam-Eldin M. Abdil-Hamid, Muhammad Y. Zakaria

Bronchosocopy and Interventional Pulmonology

Original article469 Diagnostic yield of ul trasound-guided

transthoracic biopsy in peripheral lung lesions Fayed H. Kawshty, Ahmed A. Abd Elradi, Ahmed M. Ahmed

Critical Cares

Original article477 Sonographic measurement of lung aeration

versus rapid shallow breathing index as a predictor of successful weaning from mechanical ventilation

Nabila I. Laz, Mohammad F. Mohammad,

Sahar M. Abdelsalam, Radwa M. Abdelwahab

Original article484 Clinical outcome of weaning in mechanically

ventilated patients with chronic obstructive pulmonary disease

Suzan Salama Sayed, Khalid Hussein Ahmed,

Sayed Abdelsabour Kinawy, Islam Galal Sayed

Original article489 Diaphragm and weaning from mechanical

ventilation: anticipation and outcome Rasha M. Abdelhafeez, Ahmed M. Abumossalam,

Eman O. Arram, Mohsen M. Elshafey,

Mohammed E. Abushehata

Original article498 Usefulness of different prognostic scores for

AECOPD: APACHE II, BAP65, 2008, and CAPS scores

Rania A. Sweed, Mostafa Abd El Mageed Shaheen,

Esraa A. El Gendy

Original article505 Post ICU syndrome among survivors from

respiratory critical illness. A prospective study Ahmad Abbas, Niveen E. Zayed, Samah M. Lutfy

Pulmonary functions

Original article510 Effect of different classes of obesity on the

pulmonary functions among adult Egyptians: a cross-sectional study

Ashraf M. Hatem, Mohamed S. Ismail,

Yasmine H. El-Hinnawy

Original article516 A study of the relationship between pulmonary

function tests and both fasting plasma glucose and glycated hemoglobin levels among asymptomatic cigarette smokers

Magdy M. Khalil, Rehab M. Mohammed, Omnia H.S. Hassan

Pulmonary infections

Original article523 Delay in the diagnosis and management of

tuberculosis among patients in the Suez Canal Area

Noha M. Abu Bakr Elsaid, Amany H. Refaat,

Lamiaa A. Fiala, Eman R. Hamed

Original article531 Effect of adding inhalation of sodium bicarbonate

8.4% to the usual treatment on smear-positive pulmonary tuberculosis: a prospective controlled study

Mohammad K. El-Badrawy, Eman O. Arram,

Dina A. Abdalla, Dina Al-Sagheer, Alaa Zahran,

Mohammad A. AboElEla, Adel El-Badrawyb, Wagdy Amin

Original article539 Role of lactate dehydrogenase and other

biomarkers in predicting prognosis of community-acquired pneumonia

Rasha M. Hendy, Mona A. Elawady,

Heba M. Abd EL Kareem

Original article

545 Study of the prevalence and pattern of fungal pneumonias in respiratory intensive care units

Mona M. Ahmed, Ayman A. Farghaly, Riham H. Raafat,

Waleed M. Abd Elsattar

Original article551 The impact of admission blood glucose level on

patients with community-acquired pneumonia Tamer M. Ali, Hala M. Salem, Dina R. Sultan

Sleep Medicine

Original article556 Prepolysomnography evaluation can predict

obstructive sleep apnea and is correlated to its severity

Hend M. Esmaeel, Hamdy A. Mohammadien,

Abd-Elbaset M. Saleh, Fatma H. Mohamed

Original article563 Study of serum cystatin C levels in patients with

obstructive sleep apnea Eman Elfeky, Ayman Abd El-Zaher, Amal Elbendary,

Salwa Ganna

Case Report570 Mediastinal abscess complicating esophageal

dilatation: a case report Doaa M. Magdy, Shereen Farghaly, Ahmed Metwally

Original article 443

A study of plasma copeptin level as a predictor of severityduring acute exacerbation of bronchial asthmaAhmed G. El Gazzara, Khaled M. Belalb, Tarek S. Essawya,Neveen M. Abd-Elfattaha

Background An exacerbation of asthma is an episode,characterized by a progressive increase in one ormore typicalasthma symptoms (shortness of breath, wheezing, cough,and chest tightness).Copeptin is a 39-amino acid glycopeptide that is derived fromthe c-terminal part of the pre–pro-hormone of argininevasopressin.

Aim The aim of our study was to evaluate the role of copeptinin asthmatic patients and its relationship to disease severity.

Patients and methods This was a prospective observationalstudy carried out on 45 patients during acute exacerbation ofbronchial asthma (15 mild, 15 moderate, and 15 severecases) and 15 healthy participants.

Results Our study showed no significant difference in age,sex, and BMI between case and control groups. There was astatistical highly significant differences in pulmonary functiontests, partial pressure of oxygen in arterial blood, partialpressure of carbon dioxide in arterial blood, and oxygensaturation among mild, moderate, and severe cases, andsignificant increase in total leukocytic count and hospital stayin severe cases than mild and moderate cases. There was ahighly significant increase of plasma copeptin in moderateand severe cases than mild cases and control groups. Therewere nonsignificant correlations between copeptin andpulmonary function tests in mild cases; a significant negative

© 2019 Egyptian Journal of Bronchology | Published by Wolters Kluwer -

correlation between copeptin and forced expiratory volume in1 s (FEV1) actual in moderate cases; significant negativecorrelations between copeptin, FEV1 actual, FEV1%predicted, forced vital capacity% predicted, and peakexpiratory flow% predicted in severe cases; and highlysignificant negative correlations between copeptin and partialpressure of oxygen in arterial blood and oxygen saturation inall cases (P<0.001). Partial pressure of carbon dioxide inarterial blood exhibited a nonsignificant positive correlationwith copeptin (P<0.05).

Conclusion Copeptin is proven to be a novel biomarker andis increased in patients with asthma as compared with healthycontrols.Egypt J Bronchol 2019 13:443–451© 2019 Egyptian Journal of Bronchology

Egyptian Journal of Bronchology 2019 13:443–451

Keywords: acute exacerbation, asthma, copeptin

aDepartment of Chest Diseases, bDepartment of Clinical and Chemical

Pathology, Benha University, Benha, Egypt

Correspondence to Neveen M. Abd-Elfattah, MSc, Benha, Egypt. Tel: +20

100 027 1189;

e-mail: [email protected]

Received 8 August 2018 Accepted 2 February 2019

This is an open access journal, and articles are distributed under the terms

of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0

License, which allows others to remix, tweak, and build upon the work

non-commercially, as long as appropriate credit is given and the new

creations are licensed under the identical terms.

IntroductionAsthma is defined as a common lung inflammatorydisorder of the airways that causes the bronchi to swell;this results in difficulties of breathing, chest tightness,cough, and wheezing. Severe exacerbation of bronchialasthma was defined as a life-threatening condition thatshould be managed as an emergency, and patients needhospital admission owing to worsening asthma, needfor systemic corticosteroids, or morning peak flowdecrease more than 25% of baseline in twosuccessive days [1].

Copeptin, a 39-amino acid glycopeptide, is a carboxy-terminal part of the precursor (pre–pro-vasopressin).Vasopressin has an antidiuretic action on kidney so it istermed antidiuretic hormone. It is involved in renal andcardiovascular functions [2].

Xue et al. [3] evaluated the prognosis of copeptin utilityin 525 patients who have acute dyspnea owing toasthma. They found that copeptin was a significantindependent predictor of prolonged hospital stay andmortality in patients who have acute dyspnea owing tobronchial asthma.

AimThe aim of this study is to measure the level of plasmacopeptin during acute exacerbation of bronchial asthmato determine if there is a change in its level thatcorrelates with changes in the ventilatory functions.

Patients and methodsPatientsThis was a prospective observational study carried outon 60 patients at Benha University Hospital ChestDepartment. They were classified into four groups:control group comprised 15 apparently healthynonsmoker patients, and asthma group comprised 45patients (15 mild cases, 15 moderate cases, and 15severe cases), classified according to Global Initiativefor Asthma 2016 guidelines [4].

Medknow DOI: 10.4103/ejb.ejb_61_18

mailto:[email protected]

444 Egyptian Journal of Bronchology, Vol. 13 No. 4, October-December 2019

Inclusion criteriaPatients during acute exacerbation of bronchialasthma, admitted to inpatient Chest Department,Benha University Hospital, were included.

Exclusion criteriaIn our study, we excluded patients who have renalimpairment, patients of chronic obstructivepulmonary disease (COPD), patients of cardiacasthma, and pregnant female.

MethodsAll participants were subjected to the following:

(1)
Full clinical history: some features strongly supportthe diagnosis of asthma such as nocturnal attack,periodicity of symptoms, and diurnal and seasonalvariations. Symptoms resulting owing to exertion,allergen exposure, and presence of atop in thepatient or his/her family also support thediagnosis. Patients were also asked aboutduration of the disease, previous hospitaladmission, the therapy needed to control thesymptoms, the family history, and history ofother allergies such as skin or nasal allergy.
(2)
Clinical examination (general and local chest)revealed signs of airway obstruction.
(3)
Plain chest radiography lateral and posteroanteriorviews to exclude any associated radiologicalabnormality.
(4)
Complete blood count for determination of totaland differential leukocytic counts.
(5)
Pulmonary function tests using JAEFER MS-PFT by Care Fusion (Germany). Wasperformed after administration ofbronchodilators, 6–8 h after stability of patient’sacute asthma exacerbation.
(6)
Arterial blood gases analysis using Sensa CoreMedical Instrumentation Pvt Ltd (India).
(7)
Measurement of copeptin levels: ∼2.5–5ml ofblood samples was taken from patients in testtubes containing EDTA as an anticoagulant.Centrifugation of the samples for 15min at1000 g within 30min of collection was done,and samples were stored in aliquots at −20to−80°C until the time of measurement. Todetermine plasma copeptin concentrationsamples, a new sandwich immuneluminometricassay was used. In brief, the EDTA plasmasamples were incubated with antibodies dilutedin 10–20ml of standard assay buffer underagitation (170–300 rpm) for 2 h at roomtemperature (18–24°C). The polyclonalantibodies used were directed against the amino
acid sequence 132–164 of pre–pro-vasopressin.Then the test tubes were washed four timeswith 1ml of LUMI test wash solution, andbound chemoluminescence was measured for 1 sper tube with an LB952T Luminometer(Berthold, Wildbad, Germany).

Patients gave written informed consent for theirparticipation. Ethics committee approved the study.

Statistical analysisThe data were analyzed with SPSS software (version20; SPSS Inc., Chicago, Illinois, USA). Therelationship between patients’ characteristics andmortality was tested using a χ2 test in the univariateanalysis. A P value of less than 0.05 was considered tobe statistically significant.

ResultsA total of 60 patients were classified into four groups:control group comprised 15 apparently healthynonsmoker patients, and asthma group comprised45 patients with acute exacerbation of bronchialasthma (15 mild cases, 15 moderate cases, and 15severe cases).

In the comparison between the cases and controlgroup regarding age of the patient, it is apparent thatmean±SD of the age was 44.27±9.92 years in casegroup and years and 41.67±2.69 years in controlgroup, with nonsignificant difference (P>0.05).Regarding sex, males represented 17.8% of thecase group and 23.3% of the control group, andfemales represented 82.2% of the case group and86.7% of the control group, with no significantdifference between both the groups (P>0.05).Mean±SD of BMI was 30.44±5.6 in cases and29.63±3.53 in the control group. Student t testshowed nonsignificant difference in the BMI(P>0.05) (Table 1, Fig. 1).

There was a statistically highly significant increase inpulmonary function tests in mild cases than moderateand severe cases (P<0.001) (Table 2).

There were statistically highly significantdifferences in partial pressure of oxygen inarterial blood (PaO2) and saturated oxygen(SaO2) among mild, moderate, and severe cases(P<0.001) (Table 3, Fig. 2).

There was a statistically significant increase in totalleukocytic count and hospital stay in severe cases than

Table 1 Comparison between case and control groups regarding personal data

Case group (45) Control group (15) t test P value

Age

Mean±SD 44.27±9.92 41.67±2.69 0.998 0.323

Sex

Male 8 (17.8) 2 (13.3) FET=0.0 1.0

Female 37 (82.2) 13 (86.7)

BMI

Mean±SD 30.44±5.6 29.63±3.53 0.53 0.60

Data are presented as mean±SD and n (%). FET, Fisher exact test. P value obtained from analysis of variance test. Significanceconsidered when P value less than 0.05. Nonsignificant difference (P<0.05).

Table 2 Comparison between the studied groups accordingto pulmonary function tests

Mildcases(15)

Moderatecases (15)

Severecases (15)

Ftest

Pvalue

FEV1actual

2.11±0.66

1.61±0.53 0.94±0.36 18.61 0.001(HS)

FEV1%predicted

66.6±11.91

58.13±8.86 35.8±12.71

29.83 0.001(HS)

FVC%predicted

79.67±9.95

68.87±8.35 46.93±17.39

26.58 0.001(HS)

PEF%predicted

85.96±4.68

66.96±3.82 32.18±9.37

269.4 0.001(HS)

FEV1, forced expiratory volume in 1 s; FVC, forced vital capacity;HS, highly significant; PEF, peak expiratory flow. Significanceconsidered when P value less than 0.05.

Figure 1

Comparison between case and control groups regarding age and BMI.

Table 3 Comparison between the studied groups accordingto partial pressure of oxygen in arterial blood, partialpressure of carbon dioxide in arterial blood, and oxygensaturation

Mildcases(15)

Moderatecases (15)

Severecases (15)

Ftest

Pvalue

PaO2 90.8±2.26

65.99±3.8 54.56±2.71 574.8 0.001**

PaCO2 40.13±1.85

41.79±7.07 42.97±7.7 0.82 0.45

SaO2 97.69±0.79

92.9±1.09 86.54±1.75 288.5 0.001**

PaCO2, partial pressure of carbon dioxide in arterial blood; PaO2,partial pressure of oxygen in arterial blood; SaO2, oxygensaturation. Significance considered when P value less than 0.05.**Highly significant.

A study of plasma copeptin level El Gazzar et al. 445

mild and moderate cases (P<0.05) (Table 4, Fig. 3).The means±SD of plasma copeptin are 17.47±5.42,24.33±6.61, 29.15±9.11, and 6.31±1.46 in mild cases,moderate case, severe cases, and controls. F test showshighly significant increase of plasma copeptin inmoderate and severe cases than mild cases andcontrol group (P<0.001) (Tables 5).

There was a significant negative correlation betweenforced expiratory volume in 1 s (FEV1) actual andcopeptin in moderate cases (P<0.05) (Table 7).

There were significant negative correlations betweencopeptin and FEV1 actual, FEV1% predicted, forcedvital capacity% predicted, and peak expiratory flow %predicted in severe cases (P<0.05) (Table 8).

There were nonsignificant differences in copeptinlevels between males and females in all groups(P>0.05) (Table 9).

There were high significant negative correlationsbetween copeptin and PaO2 and SaO2 in all cases

Figure 2

Comparison between the studied groups according to PaO2, PaCO2, and SaO2. PaCO2, partial pressure of carbon dioxide in arterial blood;PaO2, partial pressure of oxygen in arterial blood; SaO2, oxygen saturation.

Table 4 Comparison between the studied groups accordingto total leukocytic count and hospital stay

Mildcases(15)

Moderatecases (15)

Severecases(15)

F test Pvalue

TLC 6786.67±2453.24

8053.33±1453.99

9133.33±2107.02

4.94 0.012(S)

Hospitalstay

0.4±0.74 1.13±1.19 1.93±1.03 χ2=12.98 0.002(S)

S, significant; TLC, total leukocyte count.


(P<0.001), but PaCO2 exhibited a nonsignificantpositive correlation with copeptin (Figs 4–7).(P<0.05) (Table 10, Figs 8 and 9).

Copeptin level of 12.5 pg/ml predicts goodprognosis and survival among patients withsensitivity of 97.8%, specificity of 100%, positivepredictive value (PPV) of 100% and negativepredictive value (NPV) of 93.8% with an accuracyof 98.3% and area under the curve (AUC) of 1(P<0.001) (Table 11).

Copeptin level of 12.5 pg/ml predicts good prognosisand survival among mild patients with sensitivity of93.3%, specificity of 100%, PPV of 100%, and NPV of93.8%, with an accuracy of 96.7% and AUC of 1(P<0.001) (Table 12).

Copeptin level of 16.6 pg/ml predicts good prognosisand survival among moderate patients with sensitivityof 86.7%, specificity of 100%, PPV of 100%, and NPVof 88.2%, with an accuracy of 93.3% and AUC of 1(P<0.001) (Table 13).

Copeptin level of 18.65 pg/ml predicts good prognosisand survival among severe patients with sensitivity of86.7%, specificity of 100%, PPV of 100%, and NPV of

88.2%, with an accuracy of 93.3% and AUC of 1(P<0.001) (Table 14).

DiscussionArginine vasopressin is a posterior pituitary hormonethat is synthesized in the hypothalamic periventricularand suprapotic nuclei and then is stored and releasedfrom the posterior pituitary gland as a result of certainstimuli, such as hypoxia, infections, hypotension,acidosis, and hyperosmolarity, and it is claimed to be asensitive marker in these situations. Short half-life andinstability of arginine vasopressin (AVP) causedlimitations in its measurement. However, copeptin ismore stable in plasma and serum. So it mirrors argininevasopressin concentrations in individual stress response.Copeptin level has a marked increase when diseaseseverity is increased, and in critically ill patients [5].

To evaluate patient’s need for hospitalization andinitiate a specific treatment, we need to knowfactors that predict a worse outcome in asthma.The use of biomarkers helps to estimate thepresence of infections, their severity, and responseto treatment. Copeptin can reflect both theinflammatory cytokine responses, which correlatewith the severity of asthma, and the individualstress responses, and also the presence ofhemodynamic and osmoregulatory disturbances. So,the aim of our study was to evaluate the role ofcopeptin in asthmatic patients and its relationshipto disease severity.

Our study was carried out on 45 patients during acuteexacerbation of bronchial asthma (15 mild cases, 15moderate cases, and 15 severe cases) and 15 healthyparticipants.

Figure 3

Comparison between the studied groups according to total leukocytic count and hospital stay.

Figure 4

Correlation between copeptin and FEV1 actual among group of cases. FEV1, forced expiratory volume in 1 s.

Figure 5

Correlation between copeptin and FVC% predicted among group of cases. FVC, forced vital capacity.


Figure 6

Correlation between copeptin and FEV1% predicted among group of cases. FEV1, forced expiratory volume in 1 s.

Figure 7

Correlation between copeptin and PEF% predicted among group of cases. PEF, peak expiratory flow.

Table 7 Correlation between copeptin and pulmonaryfunction tests in moderate disease group

Copeptin Moderate cases (15)

r P

FEV1 actual −0.54 0.036 (S)

FEV1% predicted 0.09 0.76 (NS)

FVC% predicted −0.23 0.41 (NS)

PEF% predicted 0.35 0.20 (NS)

FEV1, forced expiratory volume in 1 s; FVC, forced vital capacity;NS, nonsignificant; PEF, peak expiratory flow; S, significant.Significance considered when P value less than 0.05.

Table 6 Correlation between copeptin and pulmonaryfunction tests in group of mild disease

Copeptin Mild cases (15)

r P

FEV1 actual −0.40 0.09 (NS)

FEV1% predicted −0.18 0.53 (NS)

FVC% predicted −0.16 0.56 (NS)

PEF% predicted 0.31 0.27 (NS)

FEV1, forced expiratory volume in 1 s; FVC, forced vital capacity;NS, nonsignificant; PEF, peak expiratory flow. Significanceconsidered when P value less than 0.05.

Table 5 Comparison between the studied groups accordingto copeptin value

Mildcases(15)

Moderatecases(15)

Severecases(15)

Controlgroup(15)

Ftest

Pvalue

Copeptin 17.47±5.42

24.33±6.61

29.15±9.11

6.31±1.46

37.21 0.001*

There was a nonsignificant correlation between copeptin andpulmonary function tests in mild cases (P<0.05) (Table 6).


In the present study, mean±SD of the age was 44.27±9.92 years in case group and 41.67±2.69 years in controlgroup, with nonsignificant difference (P>0.05).

Our results are in agreement with Morgenthaler et al.[6]. Their research assay measured copeptin level inserum and plasma of healthy individuals and patients toevaluate its clinical importance in a variety of


pathologies in which arginine vasopressin secretion isreportedly disturbed, and they revealed that there wasno major difference in median copeptin concentrationsafter stratification according to age groups.

Regarding sex, males represented 17.8% of case groupand 23.3% of control group and females represented82.2%of patient group and 86.7%of control group, withno significant difference between both groups (P>0.05).

This study showed nonsignificant differences incopeptin levels between males and females in allgroups (P>0.05).

Table 8 Correlation between copeptin and pulmonaryfunction tests in severe disease group

Copeptin Severe cases (15)

r P

FEV1 actual −0.41 0.06 (NS)

FEV1% predicted −0.65 0.009 (S)

FVC% predicted −0.65 0.009 (S)

PEF% predicted −0.57 0.03 (S)

FEV1, forced expiratory volume in 1 s; FVC, forced vital capacity;NS, nonsignificant; PEF, peak expiratory flow. Significanceconsidered when P value less than 0.05.

Table 9 Comparison of copeptin level between male andfemale in all groups

Sex(copeptinlevels)

Mildcases (15)(mean±SD)

Moderatecases (15)(mean±SD)

Severecases (15)(mean±SD)

Controlgroup (15)(mean±SD)

Male 14.46±0.90

30.17±1.5 32.27±13.10

8.2±0.85

Female 18.98±6.15

22.87±6.6 28.38±8.44 6.02±1.31

t test 1.61 1.85 0.65 2.24

P value 0.133 0.09 0.53 0.083

Significance considered when P value less than 0.05. Highlysignificance considered when P value less than 0.001.

Figure 8

Correlation between copeptin and PaO2. PaO2, partial pressure of oxyg

Our results were contrary to Bhandari et al. [7] whofound that copeptin levels were significantly higher inhealthy males than females (P<0.001).

Pulmonary function tests showed highly statisticallysignificant decrease in moderate and severe cases thanmild cases (P<0.001).

Ian and Fred [8] explained that airway inflammationoccurs in both allergic and nonallergic forms of asthmaand is a feature of all grades of asthma severity.

This study showed statistically highly significantdifferences in PaO2 and SaO2 among mild,moderate, and severe cases.

There was a significant increase in plasma copeptin,total leukocytic count, and hospital stay in severe andmoderate cases than mild cases.

These results are in agreement with Al Salahy et al. [9],who found that elevated plasma copeptin levels reflectdisease severity and predict long hospital and ICU stay.

Muller et al. [10] found that in patients withcommunity acquired pneumonia (CAP), acute

Table 10 Correlation between copeptin and partial pressureof oxygen in arterial blood, partial pressure of carbon dioxidein arterial blood, and oxygen saturation

Copeptin Total cases (45)

r P

PaO2 −0.62 0.001**

PaCO2 0.26 0.088

SaO2 −0.72 0.001**

PaCO2, partial pressure of carbon dioxide in arterial blood; PaO2,partial pressure of oxygen in arterial blood; SaO2, oxygensaturation. Significance considered when P value less than 0.05.Highly significance considered when P value less than 0.001.**Highly significan.

en in arterial blood.

Figure 9

Correlation between copeptin and SaO2. SaO2, oxygen saturation.

Table 11 Validity of copeptin as a predictor of diseaseseverity

Copeptin Case group Control group FET P value

≥12.05 44 (97.8) 0 (0.0) 50.11 0.001**

<12.05 1 (2.2) 15 (100)

AUC 1.0

Cutoff point 12.05

Sensitivity 97.8

Specificity 100

PPV 100

NPV 93.8

Accuracy 98.3

AUC, area under the curve; FET, Fisher exact test; NPV, negativepredictive value; PPV, positive predictive value. **Highly significan.

Table 12 Validity of copeptin as a predictor of diseaseseverity among patients with mild bronchial asthma

Copeptin Mild group(15)

Control group(15)

χ2 Pvalue

≥12.05 14 (93.3) 0 (0.0) 22.63 0.001**

<12.05 1 (6.7) 15 (100)

AUC 1.0

Cutoffpoint

12.05

Sensitivity 93.3

Specificity 100

PPV 100

NPV 93.8

Accuracy 96.7

AUC, and area under the curve; NPV, negative predictive value;PPV, positive predictive value. **Highly significan.

Table 13 Validity of copeptin as a predictor of disease amongpatients with moderate bronchial asthma

Copeptin Moderate group(15)

Control group(15)

χ2 Pvalue

≥16.6 13 (86.7) 0 (0.0) 22.94 0.001**

<16.6 2 (13.3) 15 (100)

AUC 1.0

Cutoffpoint

16.6

Sensitivity 86.7

Specificity 100

PPV 100

NPV 88.2

Accuracy 93.3


Table 14 Validity of copeptin as a predictor of disease amongpatients with severe bronchial asthma

Copeptin Severe group(15)

Control group(15)

χ2 Pvalue

≥18.65 13 (86.7) 0 (0.0) 22.94 0.001**

<18.65 2 (13.3) 15 (100)

AUC 1.0

Cutoffpoint

18.65

Sensitivity 86.7

Specificity 100

PPV 100

NPV 88.2

Accuracy 93.3



exacerbation of chronic obstructive pulmonary disease(AECOPD), and exacerbation of asthma, copeptinlevels were also significantly higher as comparedwith controls (P<0.001).

Xueetal.[3]evaluatedtheutilityofcopeptinasaprognosticmarker in 525 patients with acute dyspnea owing toasthma, COPD, pneumonia, bronchitis, and influenza.

Theyconcluded that copeptin is a significant independentpredictor of increased hospital stay and mortality inpatients with acute dyspnea of noncardiac origin.

There were highly significant negative correlationsbetween copeptin and PaO2 and SaO2 in all cases


(P<0.001), but PaCO2 exhibited a nonsignificanpositive correlation with copeptin (P<0.05).

These results are in agreement with Al Salahy et al. [9],who found that copeptin concentrations are stronglyrelated to hypoxia, as they increase markedly with lowblood oxygen concentration.

Many studies were conducted to reveal the correlationbetween hypoxia as a stress factor and serum copeptinlevels, which was statistically significant and positive inmost of cases.

In one of the early studies on the relation betweencopeptin and hypoxia, Akagi et al. [11] obtained thesame results by finding a relationship between thehormonal response to acute hypoxemia in fetal sheepand arterial blood gases values.

Our results are supported by those of Ostergaard et al.[12] on measuring plasma levels of copeptin ofSprague-Dawley rats under normoxic conditions andafter acute exposure to 10% oxygen for 5min. Theyshowed seven-fold increase in level of plasma copeptin.So, plasma copeptin is considered a sensitive, strongmarker on exposure to acute severe hypoxia.

Moreover, Schlapbach et al. [13] measured copeptinlevel in blood of umbilical cord of infants withchorioamnionitis, perinatal asphyxia, and early-onsetsepsis. They found that the highest copeptinconcentrations among all three stressor factors werein neonates who have asphyxia when compared withcontrols. These results were confirmed by multivariateanalysis adjusted for birth weight, gestational age,mode of delivery, and umbilical artery. RoC curveanalysis showed that concentrations of copeptin inblood cord have a strong association with asphyxia.

ConclusionFrom this study, it can be concluded that copeptinlevels can be a tool for the risk stratification in patients

with bronchial asthma as compared with healthycontrols. Copeptin is secreted in blood in anequimolar ratio to AVP and is more reliable toassay. Copeptin has been utilized as a surrogatemarker of AVP activity in recent investigations.

Financial support and sponsorshipNil.

Conflicts of interestThere are no conflicts of interest.

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11 Akagi K, Berdusco ET, Challis JR. Cortisol inhibits ACTH but not the AVPresponse to hypoxaemia in fetal lambs at days 123–128 of gestation. J DevPhysiol 1990; 14:319–324.

12 Ostergaard L, Rrudiger A, Wellmann S, Gammella E, Beck-schimmer B,Struck J, et al. Arginine-vasopressin marker copeptin is a sensitiveplasma surrogate of hypoxic exposure. Hypoxia (Auckl) 2014;2:143–151.

13 Schlapbach LJ, Frey S, Bigler S, Manh-Nhi C, Aebi C, Nelle M, et al.Copeptin concentration in cord blood in infants with early-onsetsepsis, chorioamnionitis and perinatal asphyxia. BMC Pediatrics2011; 11:38.

www.ginasthma.org


Impulse oscillometry usefulness in small-airway dysfunction inasthmatics and its utility in asthma controlRagia S. Sharshar

Background Small-airway affection and its relation to clinicalstatus in asthmatic patients became an increasing interestduring the last decade. Spirometry is a basic diagnostic toolfor measuring pulmonary function in asthmatics but not fullyillustrative especially in assessing small airways. Impulseoscillometry (IOS) can be considered a complementary andsometimes alternative technique to spirometry because it isused during quiet breathing and so gives more data aboutsmall-airways affection in asthmatic patients.

Aim To evaluate IOS usefulness in the detection of small-airways disease in asthma and its correlation to the level ofdisease control.

Patients and methods The study was conducted on 44asthmatic patients who were classified into two groups:controlled asthma and uncontrolled asthma by asthma controltest questionnaire (ACT score). Spirometry and IOS wereperformed on all patients.

Results Small-airway IOS values (R5–20, X5, and AX) werefound to be statistically significant between two groups.Moreover, they strongly correlated significantly with clinical


symptoms, assessed by ACT. There was high sensitivity andspecificity of (R5–20) 80 and 82%, (X5) 80 and 86%, and (AX)86 and 89%, while for spirometric data only forced expiratoryflow (FEF25–75%) showed a statistically significant differencebetween the two groups, and not FEV1% and there was poorcorrelation between ACT and FEF25–75%.

Conclusion IOS provides an easy and rapid tool to diagnoseand assess small-airways disease in adult, asthmatic patientsEgypt J Bronchol 2019 13:452–458© 2019 Egyptian Journal of Bronchology


Keywords: asthma, impulse oscillometry, small-airway dysfunction

Chest Department, Faculty of Medicine, Tanta University, Tanta, Egypt

Correspondence to Ragia S. Sharshar, MD, Chest Department, Faculty of

Medicine, Tanta University, Tanta, 1221, Egypt. Tel: +20 122 365 0568;


resented at ERS Congress 16-september-2018, poster oral discussion.

Paris, France.

Received 18 February 2019 Accepted 2 April 2019

IntroductionAsthma is a chronic inflammatory disorder that affectsthe entire tracheobronchial tree, including not onlycentral but also peripheral membranous bronchiolesthat represents small-airways affection. Remodelling ofsmall-airways affecting both clinical aspect with poorasthma control, more frequent exacerbations, as well asinfluence functional manifestations of asthma makingairflow limitation irreversible [1–3].

Functional evaluation of small-airways is still a matterof challenge, as the classical use of spirometryparameters is still not fully descriptive [4].

Impulse oscillometry (IOS), a technique first described60 years ago, was recently used successfully to evaluatelung function inhealthy individuals andasthmatics [5,6].

IOS can measure both proximal and peripheralresistance in both adults and pediatric asthmatics.The main advantage of IOS is it is simple,noninvasive, sensitive and moreover does not need aforced technique that affects the bronchial tone [7–9].






AimTo evaluate IOS usefulness in the detection of small-airways affection in asthmatic patients and itscorrelation to disease control level.

Patients and methodsThis prospective, cross-sectional study was done on 44asthmaticpatients, recruited fromtheChestDepartment,TantaUniversity, fromMay 2016 toFebruary 2017 thosewho fulfilled the ethics committee considerations.Exclusion criteria were smokers and ex-smokers,hospitalization in the last 1 month, respiratory tractinfection, and concomitant chest diseases.

After a written, informed consent has been taken,detailed medical history, thorough clinicalexamination and chest radiograph, spirometry[forced expiratory volume at first second (FEV1)/forced vital capacity (FVC), FEV1%, forcedexpiratory flow (FEF25–75%)] and IOS (R5, R5–20,X5, AX) measurements were done on all patients.

All patients were diagnosed with asthma based onmedical history, physical examination, and GINAguidelines [10].

The study patients were classified into two groups:controlled asthma and uncontrolled asthma according



IOS usefulness in small-airway dysfunction in asthmatics and its utility in asthma control Sharshar 453

to the asthma control test, which is a five-pointquestionnaire applied to evaluate asthma controlclinically. Each of the five questions of asthmacontrol test (ACT) was explained to patients beforecompletion of questionnaire, patients were consideredhaving controlled asthma if the ACT score is morethan 20 points and uncontrolled asthma if the ACTscore is 19 or less (Fig. 1) [11,12].

IOS maneuver was performed using Master Lab-IOSUnit (Master Screen IOS 2001, version 4.5; ErichJaeger GmbH, Hochberg, Germany), followingstandard recommendations [9].

The IOS device consists of measuring head, resistor, apneumotachograph, pressure and flowtransducers, andacomputer. The system was calibrated for volume beforedata collection using a 3-L syringe. The patient wasasked to breathe normally (tidal breathing) while seatedin a relaxed sitting position, the head held slightlyextended, with lips making a tight seal and tonguebelow a well-fitted mouthpiece. To avoid thecompliance of cheeks, place firmly the patient’s handsdirectly over them,with a nasal clip placed to occlude thenares. Impulses were applied for 30–45 s, IOS data were

Figure 1

Asthma control test.

reviewed, with rejecting segments affected by airflowleaks or swallowing artifacts. IOS used to assessrespiratory resistance at 5Hz (R5) indicates totalresistance. Respiratory reactance at 5Hz (X5) detectsperipheral elastic recoil of airways.Reactance area (Ax) isan integration index of reactance measure from X5 toFres [13–15].

R5–20 is defined by the difference between low-frequency total resistance (R5) and high-frequencycentral resistance (R20), and hence derives peripheralairway resistance. So peripheral airway obstruction isreflected by elevated R5–20 because pressure wavessignal passes into the distal lung, that is, R5,encounters more resistance than higher frequencymore proximal R20 impulse. Peripheral airwayobstruction leads to loss of elastic recoil expressed asless X5 and more AX. R5–20 is considered abnormal ifhigher than 0.03 kPa/l; X5 is considered normal if itequals X5 predicted 0.15 kPa/l; AX was considerednormal if it equals 0.33 kPa/l [15–17].

Statistical analysisStatistical analysis was done using SPSS (IBM Corp.Armonk, New York, USA) version (20). Continuous


data were expressed as mean±SD and categoricalvariables as percentages. Pearson’s linear correlationcoefficient was used for the correlation between ACTscores and lung function. P value of less than 0.05 wasconsidered significant.

Table 3 Correlation between spirometric, impulseoscillometry parameters, asthma control test in both groups

Asthma control test Controlled asthma Uncontrolled

ResultsA total of 44 asthmatics were included, their mean agewas 43.3±12.4 years with the percentage of women tomen being 72.7–27.3%. Basic demographic data ofpatients in both groups are illustrated in Table 1. Asfor ACT, themean value was 20.88±2.191, 29 out of 44(65.9%) cases had uncontrolled asthma while 15 out of44 (34.1%) was controlled (Table 2).

Spirometric parameters showed that the mean value ofFEV1%was 81.27±5.79 and 78.48±4.64 in groups I andII, while FEF25–75% was 62.93±4.03 and 44.17±3.55 ingroups I and II, respectively. A statistically significantdifference between FEF25–75% in two groups wasdetected, and not FEV1%. On correlation with ACT,there was poor correlation between ACT andFEF25–75%, while no correlation was detected betweenACT and FEV1 (Tables 1 and 3).

Small-airway IOS parameters were statisticallysignificant between controlled and uncontrolledasthma (P<0.05) Moreover, small-airways evaluatedby IOS indices, R5–20, X5, and AX values stronglycorrelated significantly with clinical symptoms, assessedby the ACT (Tables 1 and 3 and Figs 2–4). There washigh sensitivity and specificity of (R5–20) 80 and 82%,(X5) 80 and 86%, and (AX) 86 and 89% (Table 4).

Table 1 Level of control in the study groups, based onasthma control test

Level ofcontrol

Controlled asthma(group I)

Uncontrolled asthma(group II)

N 15/44 29/44

Percentage 34.1 65.9

Table 2 Basic demographic data of patients in both groups

Demographics Controlled asthma (group I)

N 15/44

Baseline spirometry

FEV1, %predicted 81.27±5.79

FEF25–75, %predicted 62.93±4.03

Baseline IOS

R5–20 0.68±0.31

X5 −0.85±0.19

AX 4.40±2.67

ACT 22.27±0.80

ACT, asthma control test; FEF, forced expiratory flow; IOS, impulse osc

DiscussionPoor evaluation of asthma control is a crucial elementof suboptimal asthma management, so the challengenow is to shift to a management approach based on thelevel of control [18].

Symptoms and lung function assessment considered thedifferent domains of asthma that correlate poorly overtime, so both clinical and functional assessment need to bemonitored by physicians to evaluate asthma control [19].

Although no comprehensive tool exists to defineasthma control sharply, many tools were used forthis purpose, one of these was a five-item self-administered asthma control test [11,12].

In our study according to the ACT score, 65.9%patients had uncontrolled asthma while 34.1%patients had controlled asthma. Similar findingswere reported by many previous authors, somereported 37% well-controlled asthma and anotherhospital-based study found only 28% well-controlledasthma. This was in contrast to other studies thatshowed controlled asthma was from 47% up to 80%in the studied patients [12,20–22].

Regarding spirometric values, we analyzed FEF25–75%,

the most commonly used indicator of small-airwaysaffection and FEV1%, where we found that FEF25–75%was statistically significant between the two groups

Uncontrolled asthma (group II) t-Test P value

29/44 – –

78.48±4.64 3.001 0.091

44.17±3.55 252.38 0.001*

1.68±0.29 156.99 0.001*

−1.40±0.21 70.989 0.001*

13.45±2.56 119.95 0.001*

15.48±1.40 297.77 0.001*

illometry. *P ≤0.05, statistically significant.

asthma

r P r P

FEV1% −0.147 0.214 −0.026 0.893

FEF25–75% 0.297 0.107 0.324 0.064

R5–20 −0.814 0.001* −0.789 0.001*

X5 0.828 0.001* 0.681 0.001*

AX −0.895 0.001* −0.658 0.001*

FEF, forced expiratory flow. *P ≤0.05, statistically significant.

Figure 2

Correlation between R5–20 and asthma control test in both groups.

Figure 3

Correlation between X5 and asthma control test in both groups.


Figure 4

Correlation between AX and asthma control test in both groups.

Table 4 Sensitivity and specificity of impulse oscillometryparameters

Cutoff Sensitivity Specificity PPV NPV Accuracy

R5–20 1.2 80 82 70 88 81

X5 −1.0 80 86 75 89 84

AX 10 86 89 81 92 88

NPV, negative predicted value; PPV, positive predicted value.


with no significant correlation between ACT andFEV1%. These results were highlighted by severalstudies, indicated only weak correlations betweenclinical symptoms, and airflow limitation evaluatedby FEV1 [23,24].

Otherprevious studiesby Johnbull et al. [20] showed thatthe correlation between the asthma control test andpulmonary function tests was not significant. This wasalso in accordance with the findings reported by Greenet al. [25], Reznik et al. [26], and Osborne et al. [27].

Unlike our study, Mendoza et al. [12], found acorrelation between FEV1 and ACT. This

significant correlation probably was due to a largerstudy and it was a prospective cohort study.Moreover, Chalise reported positive correlationsbetween FEV1 and ACT test [12,28].

The poor correlation between ACT and FEF25–75%

may be partly due to that asthma symptoms lackspecificity and also due to variations in magnitudeand time of response to therapy [29].

This poor correlation can be explained first by thepresence of marked measurement variability over agerange, second by the fact that forced expiratorymaneuver tends to exaggerate volume-dependentsmall-airway closure, which means FEF25–75 degreeof variability is affected by effort-dependent expirationfrom total lung capacity to residual volume. SoFEF25–75% is dependent on FVC, and if notadjusted it gives poor reproducibility; moreover, it isfrequently normal if the FEV1/FVC ratio is more than75%; lastly, there is poor correlation with other markersof small-airways such as FVC and residual volume


(RV)/total lung capacity (TLC) due to the alteration ofFVC with air trapping; therefore, there is much doubtabout the ability of FEF25–75% to clarify small-airwaysaffection [30–32].

As for IOS parameters, we found that small-airwayIOS parameters were statistically significant betweencontrolled and uncontrolled asthma (P<0.05) withhigh sensitivity and specificity. Also, these valuescorrelated significantly with clinical symptoms,assessed by ACT. Many previous studies haveshown obvious relationship between small-airwayassessed by IOS and uncontrolled asthma [33].

Takeda et al. [2] found that IOS correlated better withclinical symptoms and disease control in contrast tospirometry FEV1 that did not contribute to clinicalstatus or dyspnea. Another study by Alferini et al. [14]showedthatasthmaticswith increasedperipheral resistancehadpoorlycontrolledasthma.Moreover, theydidnotdifferfrom patients with normal values of peripheral resistancemeasured by spirometric FEV1 and FEV1/FVC.

ExplanationAsthma is considered a complex clinical syndrome, aheterogeneous group of phenotypes and endotypes thatshows different responses to therapy, rather thanspecific disease entity. Nowadays there is a movetoward personalizing asthma treatment according toeach phenotype [34–36].

So, asthmatic patients with poor control and moreexacerbations have persistent airways inflammation.More specifically, those patients show a ‘small-airways phenotype,’ where there is continuousunopposed small-airways inflammation that is notbeing targeted or controlled by current regulartherapies [37].

Small-airwaysmay be site of ventilatory heterogeneity inasthma that shows increases in peripheral airflowresistance even in patients who have normal FEV1 [30].

Three mechanical factors may explain more airwaynarrowing: first, more contractility of smoothmuscle; second, less of normal inhibiting factors sothe muscles never reach maximum force and degree ofshortening; third, decreased elastic load, provided bycartilage and the parenchyma. These three mechanismsare intensified in small-airways as they are withoutcartilage and in asthma they are a site of extensiveprocesses of inflammation and remodeling resulting indestabilization of airways, and so are more liable tobronchospasm [14,38,39].

Many studies suggest the presence of a ‘small-airwayasthma phenotype’ that may show normal parametersfor conventional pulmonary tests, that is, preservedFEV1 but poor asthma control and disproportionate,persistent, small-airway affection [40].

ConclusionIOS provides a useful tool as a marker of asthmacontrol in persistent asthmatic patients. It should beused as a complementary test with spirometry to clarifypatients with small-airway asthma phenotype. So, thiscan focus on recommendations on the importance of amultidimensional control-based strategy in asthmaapproach of personalized management.



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Correlation between ventricular function as assessed byechocardiography and six-minute walk test as a surrogate offunctional capacity in patients with chronic obstructivepulmonary diseaseMagdy M. Khalila, Hala M. Salema, Hossam-Eldin M. Abdil-Hamida,Muhammad Y. Zakariab

Background Exercise intolerance is common in patients withchronic obstructive pulmonary disease (COPD), which hasmultiple mechanisms underlying its progression. Ventriculardysfunction may play a role in the development of exerciseincapacity in patients with COPD.

Aim To investigate the possible contribution of left ventricular(LV) and right ventricular (RV) dysfunction (either systolic ordiastolic) in development of exercise intolerance in patientswith COPD.

Patients and methods A total of 60 patients with diagnosisof COPD were categorized according to GOLD spirometricstage into two groups (group 1: mild to moderate COPD, andgroup 2: severe to very severe COPD). Both groups wereevaluated by spirometry, ECG, chest radiography, routinelaboratory investigation, 6-min walk test, andechocardiography including tissue Doppler imaging.

Results The average age in the whole study group was 56.63±10.33 years. Male patients in the study were 46 (76.7%) andfemale patients were 14 (23.3%). Mean maximum walkdistance among the whole group was 342.75±54.85m. Therewas a significant correlation between 6-min walk distance andtranstricuspid E velocity, tricuspid E/A, and transtricuspiddeceleration time (P=0.011, 0.015, and 0.021, respectively).There was no significant correlation between 6-min walk


distance and parameters of LV diastolic dysfunction.Prevalence of ventricular dysfunction was as follow: LVsystolic dysfunction 3.3%, LV diastolic dysfunction 30%, RVsystolic dysfunction 21%, and RV diastolic dysfunction 46%.

Conclusion RV diastolic dysfunction may be a contributingfactor in the progression of exercise intolerance in COPD.Although LV diastolic dysfunction may not be associated withexercise intolerance, it is still prevalent in COPD and must beassessed and managed through the course of the diseaseand especially during exacerbation.Egypt J Bronchol 2019 13:459–468

© 2019 Egyptian Journal of Bronchology


Keywords: 6-min walk test, chronic obstructive pulmonary disease,echocardiography, exercise intolerance, ventricular dysfunction

aDepartment of Chest Diseases, Faculty of Medicine, Ain Shams University,

Cairo, bRespiratory ICU Department, Kafr El-Sheikh Chest Hospital, Ministry

of Health, Kafr El-Sheikh, Egypt

Correspondence to Muhammad Y. Zakaria, MSc, Borg El-Burrulos, Kafr El-

Sheikh, Egypt. Tel: +20 122 251 1538;


Received 29 May 2019 Accepted 9 September 2019






IntroductionChronic obstructive pulmonary disease (COPD) ison its way to be the third most common killer diseaseworldwide by 2020 [1,2]. Although being of primarypulmonary origin, it has a unique physiological andpathophysiological characteristics that may causeextrapulmonary effects and comorbidities [3–5].Comorbidities are associated with high mortality,reduced compliance to medications, and diminishedquality of life [6]. Cardiovascular comorbidities areamong the most frequently seen comorbiditiesassociated with COPD [4,7]. Many studies havedemonstrated a strong association between heartfailure and COPD [8–10].

Exercise intolerance is common in patients withCOPD, which has multiple mechanisms underlyingits progression. Increased ventilatory demand,associated with altered dynamic mechanics,abnormal gas exchange, airway limitation, andperipheral muscle dysfunction, is among the intrinsic

pulmonary mechanisms that are alleged for exerciseincapacity seen in such patients [11–15]. There isincreasing evidence that ventricular dysfunction mayplay a role in development of exercise intolerance inpatients with COPD.

In the present study, we tried to assess theleft ventricular (LV) and right ventricular (RV)function either systolic or diastolic inpatients with COPD. We tried to correlateechocardiographic parameters of ventricularfunction with 6-min walk test as a surrogate forexercise capacity to reveal any contribution ofventricular function on exercise intolerance foundin patients with COPD.




Patients and methods

The protocol of the study was approved by ResearchEthics Committee REC, Ain Shams University(FMASU MD 196/2016). The study included 60patients. Inclusion criteria entailed sure diagnosis ofCOPD (presence of risk factors, clinical symptoms andsigns, radiology, and spirometry) among patients whowere admitted to inpatient and outpatient clinic in KafrEl-Sheikh Chest Hospital in the period from December2016 to July 2018. Exclusion criteria were the presence ofsevere liver or kidney disease, presence of recent or oldischemic heart disease or other valvular heart disease,severe systemic hypertension, pulmonary disease otherthan COPD, acute venous thromboembolism,hemoglobin less than 10 g/dl, patients who cannotperform spirometry or 6-min walk test, and patientswith acute exacerbation of COPD. After taking awritten and informed consent from all patientsparticipating in the study, patients were categorizedaccording to GOLD spirometric criteria into group 1(mild tomoderateCOPD, forced expiratory volume in1 s(FEV1)/forced vital capacity <70%, and FEV1>50%)and group 2 (severe to very severe COPD, FEV1/forcedvital capacity <70%, and FEV1<50%). Routinelaboratory investigations to exclude severe liver orkidney diseases were performed. Chest radiography toensure hyperinflation and rule out any other chest diseasesand ECG to exclude ischemic changes or arrhythmiaswere done. Six-minwalk test was performed according toATS guidelines for all participants [16]. Patients wereinstructed to walk through a corridor of 30-m length asfast as they can for6min.Theywereencouragedbysimplewords throughout the test and were instructed to stopimmediately if they feel they cannot complete the test or ifany complications occurred. The outcome of the test wasrecordedas themaximumdistance thepatientwalked, thebasal and post-test heart rate, the basal and post-testoxygen saturation, and complication during the test (ifoccurred). Echocardiography was done for all patients inthe cardiology department, EL-Obour InsuranceHospital, using General Electric (GE) machine,VIVID S5, with a transducer (probe), 3S-RS1.5–3.6MHz. Images were acquired according to thelatest guidelines [17,18] (Fig. 1) and revised by twocardiologists in the department.

Linear LV dimension was taken from parasternal long-axis PLAX at the end of diastole, and ejection fraction(by Teicholtz method) and fractional shortening werederived. Transmitral flow was assessed using E, A, E/A, and deceleration time. Then tissue Doppler imaging(TDI) modality was used to measure e’, and E/e’ wascalculated.

RV linear dimensions were obtained in apical four-chamber view. Tricuspid annular plane excursion(TAPSE) was measured by 2D modality, and thentranstricuspid flow was assessed similar to transmitralflow using E, A, E/A, and deceleration time. TDI wasused to measure myocardial peak tricuspid annularvelocity (S’) and E’, and then E/E’ was calculated.

LV systolic dysfunction was present when EF is lessthan 50%. LV diastolic dysfunction was present whentwo of the following criteria are present [19]: (a) laterale’ velocity less than 10m/s (0.10m/s), (b) E/e’ morethan 14, and (c) tricuspid regurgitant velocity morethan 2.8m/s.

RV systolic dysfunction was present when TAPSE lessthan 1.6 cm or S’ less than 10 cm/s (0.10m/s). RVdiastolic dysfunction was present when one of thefollowing was present: (a) E/A<0.8, (b) E/A from0.8 to 2.1 and E/E’>6, and (c) E/A more than 2.1 anddeceleration time less than 120ms.

The collected data were revised, coded, tabulated, andintroduced to a PC using Statistical package for SocialScience (SPSS 25; IBM SPSS version 25, Chicago,Illinois, USA). Data were presented, and suitableanalysis was done according to the type of dataobtained for each parameter. Student t test andMann–Whitney test (U test) were used to assess thestatistical significance of the difference of parametricand nonparametric variables (respectively) between twostudy groups. χ2 test and Fisher’s exact test were used toexamine the relationship between two qualitativevariables according to their number. Correlationanalysis (using Pearson’s method) was used to assessthe strength of association between two quantitativevariables.

ResultsTable 1 summarizes patients data, comorbidities,laboratory investigations, arterial blood gases,spirometry, and 6-min walk test. Male patients inthe study were 46 (76.7%) and female patients were14 (23.3%). Both groups included 23 (76.67%) malesand seven (23.33%) females. Dyspnea was the mostfrequent complaint. Most patients were currentsmokers (46.7%) or ex-smokers (30.0%). Systemicarterial hypertension was the most commoncomorbidity (21%). One patient had hypothyroidismand liver cirrhosis, and an another one hadhypertension and HCV. Normal sinus rhythm withno ischemia was the most common finding in ECG,and all patients showed signs of hyperinflated chest on

Figure 1

(a) Linear dimensions of right ventricle: basal diameter, mid cavity, and longitudinal. (b) TAPSE by applyingM-mode at the lateral free wall of RV.(c) Myocardium velocities by TDI at the lateral tricuspid annulus: 1→ S’, 2→ E’. (d) Transmitral flow parameters: E, A, and E/A. RV, rightventricular; TAPSE, tricuspid annular plane excursion; TDI, tissue Doppler imaging.

Ventricular dysfunction in COPD Khalil et al. 461

plain chest radiography. Mean±SD maximum walkdistance among the whole group was 342.75±54.85m, mean±SD maximum walk distance was373.33±41.86m in group A and 312.17±49.32m ingroup B. Mean LV and RV echocardiographicparameters representative of both systolic anddiastolic functions are summarized in Table 2.

Correlation between 6-min walk test and ventricularfunctionThere was a significant positive correlation between 6-min walk distance and ejection fraction of the LVamong the whole study group. There was nosignificant correlation between 6-min walk distanceand other parameters of LV diastolic dysfunction.Regarding the RV, there was a significantcorrelation between 6-min walk distance andtranstricuspid E velocity (Fig. 2), tricuspid E/A(Fig. 3), and transtricuspid deceleration time (Fig. 4)(P=0.011, 0.015, and 0.021, respectively). These areparameters of RV diastolic dysfunction. There was asignificant correlation between 6-min walk distanceand TAPSE (parameter indicative of systolic function)

in mild to moderate COPD. In patients with severe tovery severe COPD, there was a significant correlationbetween 6-min walk distance and RV E/A (Fig. 5).

Prevalence of ventricular systolic and diastolicdysfunction in patients with chronic obstructivepulmonary diseaseLV systolic dysfunction was present in 3.3% of studypopulation (Table 3). There was no significant differencebetween both groups. LV diastolic dysfunction occurredin 18 (30%) patients. Among these 18 patients, grade 1diastolic dysfunction (impaired relaxation) was present in15 (25%) patients and grade 2 diastolic dysfunction(pseudonormal filling) was present in three (5%) patients.

RV systolic dysfunction occurred in 13 (21.7%) patients.There was no significant difference between both groups.RVdiastolic dysfunction occurred in 28 (46.7%) patients.Among these 28 patients, grade 1 diastolic dysfunction(impaired relaxation) was present in 14 (23.3%) patients,grade 2 diastolic dysfunction (psuedonormal filling) waspresent in 11 (18.3%) patients and grade 3 diastolicdysfunction (restrictive filling) was present in three

Table 1 Patients data, laboratory investigations, arterial blood gas, pulmonary function test, and 6-min walk test results

All patients Group 1 Group 2 P value Significance

Age 56.63±10.33 51.63±11.16 61.63±6.38 <0.001 S

Male 46 (76.7) 23 (76.67) 23 (76.67)

Female 14 (23.3) 7 (23.33) 7 (23.33)

Height (cm) 166.82±8.23 167.8±7.34 165.83±9.06 0.359 NS

Weight (kg) 71.55±9.15 71.63±8.1 71.47±10.22 0.944 NS

No smoke 14 (23.3) 7 (23.33) 7 (23.33)

Smoking 28 (46.7) 15 (50) 13 (43.33)

Ex-smoking 18 (30.0) 8 (26.67) 10 (33.33)

No comorbidities 41 (68.3) 23 (76.67) 18 (60)

HTN 13 (21.6) 4 (13.3) 9 (30)

DM 2 (3.3) 1 (3.33) 1 (3.33)

CVS 1 (1.7) 1 (3.33) 0

Old TB 1 (1.7) 0 1 (3.33)

Hypothyroidism 1 (1.7) 0 1 (3.33)

HCV (treated) 1 (1.7) 1 (3.33) 0

Liver cirrhosis 2 (3.3) 2 (6.6) 0

Systolic BP 119.17±12.39 116.67±12.41 121.67±12.06 0.119 NS

Diastolic BP 78.25±7.91 77.5±7.51 79±8.35 0.467 NS

Basal heart rate 83.08±9.69 81.27±10.63 84.9±8.44 0.148 NS

CBC (Hb) (g/dl) 13.10±1.37 13.29±1.42 12.91±1.31 0.286 NS

CBC (TLC ×103) 8.51±3.51 8.41±3.79 8.61±3.27 0.825 NS

pH 7.40±0.04 7.4±0.04 7.39±0.05 0.335 NS

PO2 (on roam air) 65.0±7.0 68.8±6.5 60.37±4.93 <0.001 S

PCO2 43.0±6.0 41.47±5.74 45.33±6.04 0.014 S

FEV1 (l) 1.41±0.58 1.85±0.39 0.97±0.33 <0.001 S

FEV1 of predicted 46.33±16.10% 60.27±6.55% 32.4±9.09% <0.001 S

FVC (l) 2.35±0.93 3.07±0.67 1.63±0.49 <0.001 S

FEV1/FVC 60.70±6.67% 60.53±7.09% 60.87±6.33% 0.848 NS

Maximum walk distance (m) 342.75±54.85 373.33±41.86 312.17±49.32 <0.001 S

Basal SPO2 92.33±2.52% 93.2±2.38% 91.47±2.39% 0.007 S

Post-test SPO2 90.40±3.22% 91.87±2.75% 88.93±3.02% <0.001 S

Post-test heart rate 89.62±12.58 85.13±9 94.1±14.13 0.005 S

The mean and SD of demographic data, comorbidities, signs and symptoms, laboratory investigations, and outcome of 6-min walk testamong the whole study group and comparison between both study groups. BP, blood pressure; CBC, complete blood count; DM, diabetesmellitus; FEV1, forced expiratory volume in 1 s; FVC, forced vital capacity; Hb, hemoglobin; HTN, hypertension; S, significance; TLC, totalleukocyte count.


(5%) patients. There was a significant difference betweenboth groups, with an increased prevalence of diastolicdysfunction among severe and very severe COPD.Prevalence of RV diastolic dysfunction in this groupreached 76%, which supports the accumulatingevidence of the importance of RV pathology in thecourse of the disease.

DiscussionThe main findings in the present study are the highprevalence of RV and LV diastolic dysfunction inpatients with COPD, and the presence of significantcorrelation between RV diastolic dysfunction and 6-min walk test, implying a contribution of RV diastolicdysfunction on exercise incapacity.

In the present study, we found no significant correlationbetween 6-min walk distance and other parameters of

LV diastolic dysfunction. In contrast with our study,López-Sánchez et al. [20] found a significant correlationbetween decreased 6-min walk distance and E/A,indicating contribution of LV diastolic dysfunction inpatients’ exercise intolerance. One of the limitations oftheir study is the exclusion of mild to moderate COPDandvery severeCOPD.Theyonly includedpatientswithsevere COPD, which may explain this difference inresults. Fenster et al. [21] did not found thisassociation between LV diastolic dysfunction and 6-min walk distance reported by López-Sánchez et al..Another study by Schoos et al. [22] did not find acorrelation between LV diastolic dysfunction and 6-min walk distance. Another recent study by Mulleret al. [23] compared patients with COPD having LVdiastolic dysfunction with patients having no diastolicdysfunction using cardiopulmonary exercise test. Theyfound no association between LV diastolic dysfunctionand worsening of exercise tolerance.

Table 2 Echocardiographic parameters of left and right systolic and diastolic functions


Left ventricle

LVEDD (cm) 4.73±0.85 4.71±0.8 4.76±0.91 0.822 NS

LVESD (cm) 3.19±0.66 3.15±0.52 3.23±0.78 0.641 NS

IVS (cm) 1.21±0.21 1.17±0.18 1.25±0.23 0.14 NS

PW (cm) 1.28±0.28 1.22±0.28 1.34±0.28 0.103 NS

EF 60.75±7.45% 61.3±6.23% 60.2±8.56% 0.572 NS

E (m/s) 0.58±0.14 0.59±0.15 0.57±0.13 0.465 NS

A (m/s) 0.63±0.17 0.62±0.17 0.65±0.18 0.469 NS

E/A 0.97±0.30 1.0±0.26 0.95±0.34 0.529 NS

E’ (m/s) 0.10±0.03 0.11±0.04 0.09±0.02 0.046 S

E/e’ 6.30±2.22 6.07±2.46 6.54±1.97 0.411 NS

FS 33.10±5.69% 33.43±4.84 32.77±6.5 0.654 NS

Deceleration time (ms) 218.53±59.96 234.07±56.12 203±60.55 0.044 S

Right ventricle

RV basal diameter (cm) 3.60±0.47 3.6±0.45 3.59±0.5 0.914 NS

RV mid diameter (cm) 2.81±0.47 2.84±0.48 2.78±0.48 0.648 NS

RV long. diameter (cm) 6.46±0.88 6.5±0.74 6.42±1.01 0.707 NS

TAPSE (cm) 2.06±0.35 2.1±0.34 2.03±0.36 0.422 NS

S’ (peak velocity)

Lat. annulus) (m/s) 0.14±0.06 0.13±0.03 0.15±0.09 0.291 NS

RV E (m/s) 0.53±0.11 0.55±0.08 0.51±0.13 0.245 NS

RV A (m/s) 0.55±0.19 0.52±0.11 0.58±0.25 0.246 NS

RV E/A 1.07±0.35 1.09±0.26 1.04±0.43 0.589 NS

E/E’ 5.36±1.91 5.21±1.52 5.52±2.25 0.532 NS

RV decel. time (ms) 200.55±45.72 211.6±39.12 189.5±49.69 0.061 NS

TR reg. velocity (m/s) 1.71±0.88 1.41 (0.99–1.9) 1.66 (1.03–2.57) 0.211 (M) NS

sPAP by (mmHg) 20.09±16.90 12 (9.09–19.44) 16.01 (9.24–31.41) 0.22 (M) NS

Mean echocardiographic parameters in the whole study group and compares between both study groups. EF, ejection fraction; FS,fractional shortening; IVS, interventricular septum (IVS) thickness; Lt vent A, transmitral A wave velocity; Lt vent e’, lateral mitral annuluse’ wave velocity by tissue Doppler; Lt vent. E, transmitral E wave velocity; LVEDD, left ventricle end diastolic dimension; LVESD, leftventricle end systolic dimension; PW, posterior wall thickness; RV A, transtricusped A wave velocity; RV E, transtricusped E wave velocity;S’, right ventricle free wall velocity at tricuspid lateral annulus by tissue Doppler imaging; sPAP, estimated pulmonary artery pressure;TAPSE, tricuspid annular plane systolic excursion.


Watz et al. [24] estimated Pro-BNP as a marker ofmyocardial failure and correlated it with 6-min walktest. They concluded that physical activity is associatedwith LV diastolic dysfunction. Pro-BNP is not specificfor LV pathologies and may be increased in RVdysfunction. Patients with LV dysfunction may alsohave RV dysfunction, which causes the observedexercise intolerance.

Regarding the RV, there was a significant correlationbetween 6-min walk distance and parameters of RVdiastolic dysfunction. This positive correlation amongthe whole study group suggests a decreased exercisecapacity in presence of RV diastolic dysfunctionirrespective of COPD spirometric stage, which maysuggest that even in mild and moderate airflowlimitation, when RV diastolic dysfunction develops,exercise incapacity may evolve.

The role of ventricular dysfunction in progression ofexercise intolerance in COPDwas not fully determined.Furthermore, theRVwas always neglected in traditional

workup and studies to the extent that it was once coinedas ‘the forgotten chamber’ [25]. The present studysupports the results of Fenster et al. [21], who studiedthecorrelationbetween6-minwalk test andRVdiastolicfunction. They found a positive significant correlationbetween 6-min walk distance and RV E/A. Theyconcluded that RV diastolic dysfunction maycontribute to exercise intolerance in patients withCOPD [21]. They referred the association betweenRV diastolic dysfunction and decreased exercisetolerance to diminished RV preload and strokevolume, which may be exacerbated during exercise[21]. Although Schoos et al. [22] did not find thisassociation between lower 6-min walk distance andRV diastolic dysfunction, they reported a reduction inRV diastolic function parameters. In addition, theyexcluded mild COPD and very severe COPD, whichmay affect the overall correlation. Cuttica et al. [26], intheir study of patients without severely impaired lungfunction, demonstrated that structural changes of therightheart are associatedwithadecrement in6-minwalkdistance independent of spirometric stage. However,

Figure 2

Correlation between 6-min walk distance and RV transtricuspid E velocity. A lower 6-min walk distance is associated with lower E velocity amongthe whole study group. RV, right ventricular.

Figure 3

Correlation between 6-min walk distance and right ventricular E/A in the whole study group. A lower 6-min walk distance is associated with lowerE/A ratio.


they did not find an association between 6-min walkdistance andRV systolic function as assessed byTAPSEor LV systolic or diastolic function [26].

Although our study did not include control group,there is a large body of evidence that LVdysfunction is prevalent in patients with COPD

Figure 4

Correlation between 6-min walk distance and right ventricular deceleration time in the whole study group. A lower 6-min walk distance isassociated with lower deceleration time.

Figure 5

Correlation between 6-min walk distance and transtricuspid E/A in group B. A lower 6-min walk distance is correlated with a lower E/A in patientswith severe to very severe COPD. COPD, chronic obstructive pulmonary disease.


[20,27–31]. Prevalence of LV diastolic dysfunctionvaries largely between different studies. Our estimateis relatively close to another study by Gupta et al. [27]

who reported a prevalence of 47.5% among their studygroup which include 40 patients with COPD ofdifferent spirometric stages. Another recent study by

Table 3 Prevalence and grades of left and right ventricular dysfunction


LV systolic dysfunction 2 (3.3) 1 (3.33) 1 (3.33) 1.00 (F) NS

LV diastolic dysfunction 18 (30.0) 6 (20) 12 (40) 0.091 (C) NS

Grade 1 15 (25.0) 6 (20) 9 (30) 0.141 NS

Grade 2 3 (5.0) 0 3 (10)

RV systolic dysfunction 13 (21.0) 4 (13.33) 9 (30) 0.117 (C) NS

RV diastolic dysfunction: 28 (46.7) 7 (23.33) 21 (70) <0.001 (C) S

(a) Grade 1 14 (23.3) 4 (13.33) 10 (33.33)

(b) Grade 2 11 (18.3) 3 (10) 8 (26.67) 0.002 S

(c) Grade 3 3 (5) 0 3 (10)

This table represents the prevalenc of systolic and diastolic dysfunction of the the left and right heart among the whole study group and inboth study groups. LV, left ventricular; RV, right ventricular; S, significance.


Jatav et al. [28] estimated LV diastolic dysfunction tobe 46% in a sample of 100 patients with COPD. This isin contrary to a study performed by Miranda et al. [29]who reported a prevalence of LV diastolic dysfunctionof 88% among their study group and also observed asignificant difference between both groups of study(group 1: mild to moderate COPD and group 2: severeto very severe COPD). López-Sánchez et al. [20]found LV diastolic dysfunction in 90% of theirgroup (80% grade 1 and 10% grade 2). Rawy andFathalla [30] reported a prevalence of 73.3%.However, many authors argue that, Freixa et al.reported a prevalence of 12% in COPD patients ontheir first hospital admissions [31]. But actually Freixaand colleagues found diastolic dysfunction in about60% of their study sample but were exposed to error indata summarization and reporting.

This difference in the prevalence of diastolicdysfunction may be owing to variability inechocardiographic parameters needed to diagnoseand grade diastolic dysfunction. One of thecontributions of the present study is the utilizationof the most recent algorism suggested by the AmericanSociety of Echocardiography published in 2016 fordiagnosis and grading of LV diastolic dysfunction inour patients’ diagnosis and grading. Systemichypertension and cardiac ischemia are the mostcommon causes of diastolic dysfunction. Anothercontribution of the present study is the exclusion ofpatients with a prior history of ischemic heart diseases,and patients with severe arterial hypertension who maynot be excluded in some other studies [29].

In the present study, RV systolic dysfunction occurredin 13 (21.7%) patients. There was no significantdifference between both groups. In the previouslymentioned study by Jatav et al. [28], RV systolicdysfunction was present in 14% of patients. In thestudy by Gupta et al. [27], RV systolic dysfunction waspresent in 7.5% of patients. In their study, RV systolic

dysfunction was diagnosed by eyeballing whenobserving wall motion hypokinesia. Obviously, thismethod may underestimate the presence of impairedfunction in many patients. Vizza et al. [32] estimatedthe prevalence of RV systolic dysfunction (defined byRV ejection fraction<45%) in patients with COPD as59%. An advantage of using TDI to assess RV functionis that measurement is independent of geometricassumptions and endocardial border tracing. Anotherstrength of the present study is the incorporation ofTDI parameters like peak tricuspid lateral annulusvelocity (S’) in assessment of RV systolic functionevaluation.

Importantly, RV dysfunction either systolic or diastolicwas formerly linked to development of pulmonaryhypertension. The increased pulmonary vascularresistance with subsequent increase in RV afterloadwill lead to RV hypertrophy, and then dilation and evenRV failure. Recent studies have demonstrated that RVdysfunction may occur even in the absence ofpulmonary hypertension. They referred RV changesto other mechanisms such as systemic inflammation,microvascular ischemia, hypoxia, systemichypertension and obesity [21,33–37]. Hilde et al.[38] concluded a similar observation about thepresence of RV function changes in absence ofpulmonary hypertension in patients with COPD.From a clinical point of view, exercise training isproved to enhance exercise capacity and quality oflife in patients with heart failure with preservedejection fraction [39,40] which is frequently causedby RV dysfunction [41–44] or LV diastolic dysfunction[45]. Owing to the high prevalence of RV systolic anddiastolic dysfunction and LV diastolic dysfunction inour patients with COPD, exercise training should beprescribed in patients with COPD having theseventricular abnormalities. Moreover, avoidingtachycardia by correcting hypoxemia and optimizingmedication is mandatory to maintain LV filling [46]. Itis not clear whether phosphodiesterase inhibitors


(PDE 5) − which is proved to improve RV function[47] − enhance RV function by direct mechanism or byimproving RV afterload [21]. So, the role of PDE 5 inRV diastolic dysfunction in patients with COPDshould be evaluated. Again, LV diastolic dysfunctionand RV dysfunction should be assessed and managedpromptly in every COPD exacerbation as they may bethe cause of cardiac decompensation responsible forthis exacerbation [48].

LimitationsThe study was cross-sectional, so firm conclusionsabout causality cannot be made. Moreover, we didnot have a control group, so we used referencevalues from literature. Performing echocardiographywas difficult in patients with COPD because of thelimited acoustic window owing to hyperinflation;however, only measurements with a satisfactoryimage quality have been used for analyses.

In conclusion, RV diastolic dysfunction may be acontributing factor in progression of exerciseintolerance in COPD regardless of patients’spirometric stage. Although LV diastolic dysfunctionmay not be associated with exercise intolerance, it is stillprevalent in COPD and must be assessed and managedthrough the course of the disease and especially duringexacerbation.

AcknowledgementsThe authors thank professorMuhammadKamal Salama,MD, professor of cardiology, Aswan University, for hisgreat help and consultation in this study.The authors alsothank their colleagues,DrMahmoudNasr,DrEmadEl-Gammal, Dr Hamada Abdul-Baset, and Dr AsmaaBarakat, cardiologists in Cardiology Department, El-Obour Hospital, for their great help and assistance inperforming echocardiography for their patients.Moreover, the authors thank Dr Eslam Saboukh, alecturer of cardiology, Kafr El-Sheikh University, forhis great help and advice.



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Diagnostic yield of ultrasound-guided transthoracic biopsy inperipheral lung lesionsFayed H. Kawshtya, Ahmed A. Abd Elradib, Ahmed M. Ahmedc

Background Ultrasonography (US) guidance oftransthoracic needle biopsy of peripheral lung lesions is auseful diagnostic technique. It is a relatively easy and safeprocedure under real-time US guidance andmay give enoughtissue sampling of lesions for histopathological examination.The aim of this study was to determine the diagnosticaccuracy and safety of this technique in the diagnosis ofperipheral lung lesions.

Patients and methods A total of 60 patients underwent US-guided percutaneous needle biopsy of peripheral lung lesionsfrom November 2017 to October 2018 in the ChestDepartment. The age of patients ranged from 27 to 79 years,with mean age of 58.4 years. Overall, 48 (80%) patients of thestudied group were males, whereas 12 (20%) patients werefemales.

Results According to the final diagnoses, 48 (80%) caseswere malignant and 12 (20%) cases were benign. Diagnosticaccuracy was 90%, sensitivity was 96%, both specificity andpositive predictive value were 100%, and the negative


predictive value was 60%. Pneumothorax and hemoptysisoccurred in two (3.33%) patients each.

Conclusion Chest US-guided biopsy in the diagnosis ofperipheral lung lesions is a safe and fast procedure with highdiagnostic yield and fewer complications.Egypt J Bronchol 2019 13:469–476© 2019 Egyptian Journal of Bronchology


Keywords: diagnostic accuracy, lung biopsy, lung, transthoracic biopsy,ultrasound

Departments of, aChest Diseases, bRadiological, cPathology, Al-Azhar

University, Assiut, Egypt

Correspondence to Fayed H. Kawshty, MD, Al Forsan Building, Al-Azhar

Street, Assiut City, Assiut, Egypt. Tel: +20 109 332 6825; fax:

02882149358;


Received 15 March 2019 Accepted 8 April 2019






IntroductionPeripheral pulmonary lesions (PPLs) are verycommon, with an increase in frequency of itsidentification in recent years. PPLs are outlined aslesions adjacent to the pleura and having an accessiblewindow for the ultrasound (US). PPLs are solid orfatty solid nodule present beyond the visible range offlexible bronchoscopy, detected by chest radiographyand computed tomography (CT), which may bearising from lung, pleura, chest wall, ormediastinum [1,2].

Chest US is an effective and safe method for evaluationof lesions in the lung periphery, the chest wall, pleuralcavity, and mediastinum. US guidance of needle biopsyto obtain specimens for histopathological examinationprovides real-time imaging of the procedure. US-guided percutaneous transthoracic needle biopsy hasmany advantages over other imaging techniques such asless exposure of the patient to radiation, acceptability,rapid, inexpensive, and bedside procedure. US-guidedtechniques are especially suitable for individuals whoare more susceptible to injury from radiation, such asinfants and pregnant women, and for patients who aredifficult to move [3].

Percutaneous transthoracic needle biopsy is a well-established diagnostic procedure. It has beenidentified as playing a crucial role in diagnosingmany pulmonary lesions [4].

The purpose of our study was to evaluate the safety anddiagnostic accuracy of transthoracic US-guided needlebiopsy in the diagnosis of PPLs.

Patients and methodsThe study was carried out at Chest Department duringthe period from November 2017 to October 2018. Itincluded 60 patients with undiagnosed PPLs asevidenced by chest radiography (posteroanterior andlateral views) and recent contrast-enhanced chest CT.All patients underwent real-time US before biopsy todetect its diagnostic outcome. The study was approvedby the local ethical committee of our university toevaluate and publish information. After explainingthe study details to the patients, written or verbalconsent was taken from all patients.

Inclusion criteriaPatients were selected upon the presence of a mass onthe chest radiograph with accessible US window (notunder rib or retrosternal) and no intervening normalparenchymal tissue between pleural surface and lesion.All patients would have to be fully conscious and fit forthe procedure.



Table 1 Sociodemographic characteristics among patients,size, and location of the lesion and complication (N=60)

Age (years)

Mean±SD 58.4±13.4

Range 68.2 (27–79)

Age groups (years) [n (%)]

20–30 4 (6.7)

31–40 4 (6.7)

41–50 8 (13.3)

51–60 12 (20.0)

61–70 24 (40.0)

71–80 8 (13.3)

Sex [n (%)]

Male 48 (80.0)

Female 12 (20.0)


Exclusion criteriaThe following were the exclusion criteria: (a) Bleedingdiseases (activated partial thromboplastin time ratio orinternational normalized ratio more than 1.3 or plateletcount less than 50 000/mm3); (b) cardiovascularinstability, such as uncontrolled severe hypertension; (c)lack of patient cooperation, for example, alteredconsciousness; (d) contralateral pneumonectomy; (e)borderline respiratory failure (SaO2, 85–90%) andpatient on mechanical ventilation; (f) hypervascularlesion or aneurysm; (g) severe chronic obstructivepulmonary disease (forced expiratory volume in 1 s<1 lor <35% predicted); (h) pyogenic cutaneous lesion(pyoderma); and (i) patient refusal.

Smoking [n (%)]

Smoker 44 (73.3)

Nonsmoker 16 (26.7)

Size of lesion [n (%)]

3–4 6 (10)

4–5 8 (13.4)

5–6 14 (23.3)

6–7 18 (30)

7–8 14 (23.3)

Mean±SD (range) 5.90±1.41 (3–8)

Radiological site [n (%)]

RT upper zone 20 (33.3)

RT middle zone 4 (6.6)

RT lower zone 8 (13.3)

LT upper zone 16 (26.8)

LT middle zone 6 (10)

LT lower zone 6 (10)

Complication [n (%)]

Pneumothorax 2 (3.3)

Hemoptysis 2 (3.3)

LT, left; RT, right.

Table 2 Presenting symptoms of patients

Symptoms n (%)

MethodsAll patients underwent the following: (a) history takingand clinical examination; (b) chest radiograph(posteroanterior and lateral views) before and after themaneuver; (c) recent CT chest with nonionic contrastmedia before the biopsy; (d) laboratory studies, such ascomplete liver functions, kidney functions, completeblood count, erythrocyte sedimentation rate, and bloodsugar; (e) preprocedural evaluation, such as pulmonaryfunction tests, arterial blood gases,ECG, and coagulationprofile (including bleeding and clotting times,prothrombin time and activity, and activated partialthromboplastin time) with the consideration that oralanticoagulants were stopped before the procedure for atleast 48 h; (f) chest US for all patients using high-resolution real-time US; (g) color Doppler US by thesame device in suspected vascular lesions; (h) clinical andradiological follow-up of patients over 1 week after theprocedure to detect the occurrence of any complications;and (i) histopathological examination of biopsy samples.

Dyspnea 30 (50)

Cough 26 (43.3)

Expectoration 22 (36.6)

Chest pain 48 (80)

Hemoptysis 44 (73.3)

Toxic manifestation 14 (23.3)

ResultsThis study included 60 patients with peripheral lunglesions; of them, 48 (80%) patients were males, whereas12 (20%) patients were female. Their age ranged from27 to 79 years, with a mean age of 57.7 years. Thesmoking status among patients was 73.3% smokers and26.7% nonsmokers (Table 1). The maximum numberof patients were with a lesion on right upper zone, andnext came left upper zone. The mean size of the lesionswas 5.90±1.4 cm (3–8 cm) (Table 1).

Most of the patients presented with chest pain (80%)and hemoptysis (73.3%), which is indicative of thelesion to be peripheral, close to the chest wall (Table 2).

Conclusive diagnosis with an initial biopsy wasobtained in 54 (90%) of 60 procedures. Biopsies

were successfully performed in all patients. The mostfrequent diagnosis was adenocarcinoma in 20 (33.3%)patients. Regarding the final diagnosis, the percentageof malignant lesions was as follows: adenocarcinoma in20 (33.3%) patients, squamous cell carcinoma in 14(23.3%) patients, undifferentiated nonsmall cellcarcinoma in 12 (20%) patients, and small-cellcarcinoma in two (3.3%) patients. Percentage ofbenign lesions was as follows: tuberculosis in four(6.7%) patients, organizing pneumonia in six (10%)patients, and thymoma in two (3.3%) patients(Table 3).

Ultrasound-guided transthoracic biopsy peripheral lung lesions Kawshty et al. 471

Six (10%) patients in whom the specimen obtainedby US was nondiagnostic underwent alternativediagnostic procedures (CT-guided needle biopsy).These cases required a second biopsy to get adefinite opinion. Five cases were described asnonspecific inflammatory cells in the firstbiopsies and underwent second biopsies, wheretwo cases were documented to be squamous cellcarcinoma, third and fourth cases weredocumented to be undifferentiated nonsmall cellcarcinoma, and the fifth case was documented tobe small-cell carcinoma. As for the remaining one,an initial report was that of the proteinaceous castwith no viable tissue, and a repeat biopsy wasdemanded, on which conclusive report ofthymoma was obtained.

Table 3 Final histopathological diagnosis of biopsy

Final diagnosis n (%) Diagnostic accuracy ofinitial biopsy [N/n (%)]

Undifferentiated nonsmall cell 12 (20) 10/12 (83.3)

Adenocarcinoma 20 (33.3) 20/20 (100)

Squamous cell carcinoma 14 (23.3) 12/14(85.7)

Small-cell carcinoma 2 (3.3) 1/2 (50)

Thymoma 2 (3.3) 1/2 (50)

Tuberculosis 4 (6.7) 4/4 (100)

Organizing pneumonia 6 (10) 6/6 (100)

Total 60 (100) 56/60 (90)

Table 5 Histopathological results in relation to age

Diagnosis Cell type

Malignant Undifferentiated nonsmall cell

Adenocarcinoma

Squamous cell carcinoma

Small-cell carcinoma

Total

Benign Thymoma

Tuberculosis

Organizing pneumonia

Total

**P value is statistically highly significant.

Table 4 Histopathological results in relation to smoking

Diagnosis Cell type

Malignant Undifferentiated nonsmall cell

Adenocarcinoma

Squamous cell carcinoma

Small-cell carcinoma

Total

Benign Thymoma

Tuberculosis

Organizing pneumonia

Total

*P value is statistically significant. **P value is statistically highly signific

There was a highly significant association betweensmoking and malignant lesions (P<0.03) (Table 4).In addition, there was a highly significant associationbetween age more than or equal to 50 years andmalignant lesions, whereas no significant associationwas detected between the two age groups (<50 yearsand ≥50 years) and benign lesions. This means thatsmoking and aging are risk factors for malignancy(Table 5). This also indicated that the probability ofthe lesions to be malignant was increased if the patientis smoker and old and the probability of the lesions tobe benign was increased if the patient is a nonsmokerand young.

Regarding the final diagnosis, diagnostic accuracywas 90% (27/30), sensitivity was 96%, bothspecificity and positive predictive value were 100%,and the negative predictive value was 60% (Table 6).Complication reported in four (6.66%) patients inthe form of hemoptysis and pneumothorax(two for each). The hemoptysis stoppedspontaneously without specific treatment, andpneumothorax was small, which did not requireintercostal tube drainage and improvedspontaneously (Table 1).

Statistical analysisThe data were collected, tabulated, and statisticallyanalyzed using Stata, version 7.0 software (Stata Corp.,

<50 years ≥50 years P value

2 10 0.005**

2 18

2 12

0 2

6 42

2 0 0.548

4 0

4 2

10 2

Smoker Nonsmoker P value

12 0 0.003**

6 14

14 0

2 0

34 14

0 2 0.049*

4 0

6 0

10 2

ant.


College Station, Texas, USA). Enumeration data werepresented as mean±SD and were analyzed with an

Figure 1

(a) Chest radiograph of a 63-year-old man showing upper left lobe opacishowing TNB entering the mass. CT, computed tomography; TNB, tran

Table 6 Diagnostic yield of ultrasound-guided needle biopsy

Variables %

Sensitivity 88

Specificity 100

PPV 100

NPV 62.5

Accuracy 90

NPV, negative predictive value; PPV, positive predictive value.

unpaired t test. Categorical variables were analyzed withPearson’s χ2 and Fisher exact tests. P value less than 0.05was statistically considered significant (Figs 1–6).

DiscussionTransthoracic ultrasound allows thoracic lesionvisualization and their structural characterization;moreover, the internal echotexture of the lesion canbe evaluated with the help of color Doppler, allowingprecise targeting of central necrosis in large masses [5].

ty; (b) CT chest showing upper lobe mass of 5×4.5 cm; (c) US imagesthoracic needle biopsy; US, ultrasound.

Figure 2

Photograph showing 18 G×20 cm Tru-cut needle biopsy 46026-Quistello (MN) (Italy).

Figure 3

Photograph showing ultrasound Siemens Acuson, ×300.

Figure 4

A biopsy specimen was taken from a lung mass showing moderatelydifferentiated adenocarcinoma (hematoxylin and eosin stain, ×400).

Figure 5

A biopsy specimen was taken from a lung mass showing poorlydifferentiated squamous cell carcinoma (hematoxylin and eosin stain,×200).


In addition, the US allows percutaneous-guidedbiopsies with lower risks compared with the otherradiological guiding methods such as CT. The UShas several advantages: no radiation exposure,accessibility, real-time monitoring, lower costs, andshorter biopsy time [6].

However, there are some limiting factors thatprohibit sonographic evaluation of the chest, whichare dependent on the physical limitations of the USbeam [7]. Its limitations are obscurement of lesionsby aerated lungs and smaller, deep seated, and

Figure 6

A biopsy specimen was taken from a lung mass showing small-cellcarcinoma (hematoxylin and eosin stain, ×400).


cavitary lesions. Sonography is employed forguidance in lung, and for pleural or mediastinallesions in contact with the chest wall and CT forthose not approachable by US. CT scan depicts clearanatomical details and provides access to any space ofthe body. It is, however, overpriced and therefore theneedle is not passed in real time. CT has, among itsblessings, clear depiction of anatomical details andaccess to any area of the body. It, however, isexpensive, takes a long time to perform, andincludes radiation exposure [8].

US is often as effective as CT for the guidance ofthoracic biopsy of peripheral thoracic lesions. CTguidance was necessary only in cases of deeper orsmaller nodules, or where the nodules were situatedclose to the heart and great vessels [9].

Imaging-guided transthoracic needle biopsy is a well-established and safe method for procurement of tissuefrom lung lesions, with high diagnostic accuracy,sensitivity, and specificity. With recent refinementsin sonographic techniques, sonography can be aseffective as CT for guiding transthoracic biopsy ofperipheral chest lesions [10,11].

The results of the present study showed that there were54 (90%) patients out of 60 patients accuratelydiagnosed by US transthoracic true-cut needlebiopsy. In agreement with our study, Yuan et al.

[12] reported a study including 30 patients with verysmall pulmonary peripheral lesions with a success rateof 90%. Moreover, this result was in agreement withEl-Shimya et al. [13] who showed that the diagnosticyield of sonographic examination in PPLs was 90.3%.This result was in accordance with Garcia-Ortega et al.[3]. They documented that the diagnostic accuracy ofpercutaneous US-guided biopsies of peripheralthoracic lesions was 90.4%. This result is alsoconsistent with a study conducted by Blank [14]who reported that the diagnostic yield exceeds 90%.This result is similar to the studies by Jeon et al. [15]and Cao et al. [16] which founded that diagnosticaccuracy of US-guided lung core biopsy was 89.6%.

In contrast, a study by Liao et al. [10] documented thatcorrect diagnosis was obtained in 48 (96%) of the 50patients. Yeow et al. [17] analyzed 631 transthoraciccutting needle lung biopsy procedures, and the resultsshowed that the lesion size is one of the most importantfactors affecting diagnostic accuracy. Pulmonarylesions smaller than 1.5 cm or larger than 5 cm areassociated with lower diagnostic accuracy rates. Thiscan be explained by the fact that the presence of ahigher percentage of tumor necrosis may haveaccounted for the lower diagnostic accuracy rate fortumors larger than 5 cm in diameter. In a study by Caoet al. [16], the overall diagnostic accuracy was 85.9%(104 of 121).

On the contrary, Jeon et al. [15] had shown that theoverall diagnostic accuracy of US-guided transthoracicbiopsy of PPLs was 91.8% (89/97). Liao et al. [10]reported diagnostic accuracy of 96% for US-guidedtransthoracic biopsy of peripheral thoracic lesions,which were less than 3 cm. According to Yang et al.[18], neither lesion location nor lesion size affected theresults of US-guided needle biopsy of thoracic lesions.Muhammad et al. [19] and Jamakani et al. [20] foundthat the diagnostic yield of US-guided tru-cut biopsiesof peripheral lesions was 98%. Moreover, Sconfienzaet al. [21] documented that the diagnostic yield of US-guided biopsies of peripheral lesions was 97.1% (100 of103 biopsies).

Pneumothorax is a common complication, and its ratevaries from 4 to 5%. However, the problem must beconsidered dangerous [22]. Other complications likehemoptysis (4–5%) are found to subside withoutintervention. Air embolism is an extremely rarecomplication in transthoracic needle aspiration(TTNA). It occurs when a fistula is created betweena pulmonary vein and an airway [23]. Hemoptysis andpneumothorax are the foremost frequent complications


of transthoracic biopsy and are principally delicate andself-limiting [24].In our study, two (3.33%) patientdeveloped pneumothorax and another two (3.33%)developed hemoptysis. Overall complications were6.66%. Similar to our study, Cao et al. [16] reportedthat only four (3.3%) patients out of 121 patients hadmild hemoptysis. This result was in agreement withSreelatha et al. [25] who documented thatpneumothorax and hemoptysis occurred in 4% (onepatient out of 22 patients for each one).

This result is closely consistent with the studiesconducted by El-Shimya et al. [13], Diacon et al.[26], and Chira et al. [7], who reported thatincidence of pneumothorax was 4% for US-assistedtransthoracic biopsy.

In contrast, Jamakani et al. [20] founded that incidenceof pneumothorax was 9% (five out of 55 patients) afterUS-guided biopsies. However, Jeon et al. [15] reportedthat post-procedural pneumothorax and hemoptysisoccurred in two patients [two (2.1%) out of 97patients]. In addition, Grasso et al. [27] mentionedthat two (2.4%) patients showed two episodes ofmassive pneumothorax and four (4.8%) patientsexperience low-grade hemoptysis. Sconfienza et al.[21] provided that postbiopsy pneumothorax wasobserved in six (5.8%) of 103 US-guided proceduresand hemorrhage occurred in one (1.0%) of 103 US-guided procedures.

Our study has several limitations. First, the number ofthe patients was small despite the observablevariations regarding diagnosis, lesion size, andlocation. So, larger studies are required to identifythe role of US in the diagnosis of PPLs of differentetiologies. Second, there was a selection bias becauseonly lesions that were in contact with pleural tissuewere selected for biopsy. Further future study can bedone on a large number of patients with peripherallung lesions comparing the tru-cut needle with othertypes of needles. The last limitation to our study is theabsence of a pathologist capable of making an on-the-spot cytology evaluation of the lesion during thebiopsy procedure.

ConclusionThe US is an effective method with high diagnosticyield for the diagnosis of any PPLs. It has manyadvantages such as no radiation exposure, real-timemonitoring, accessibility, inexpensive, short time ofbiopsy, and few complications. Transthoracic needlebiopsy under US guidance was a very efficient, safe, and

less invasive diagnostic method for obtaininghistopathological diagnosis avoiding unnecessarysurgical procedures.



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Sonographic measurement of lung aeration versus rapidshallow breathing index as a predictor of successful weaningfrom mechanical ventilationNabila I. Laza, Mohammad F. Mohammada, Sahar M. Abdelsalamb,Radwa M. Abdelwahabc

Background Lung ultrasonography is a beneficial tool forevaluation of the extent of lung aeration throughmeasurement of the amount of extravascular lung water.Lung ultrasonography offers some advantages over othermethods of assessment of lung aeration including the lack ofionizing radiation and the possibility of use at patient’sbedside. It facilitates dynamic assessment of the lung duringmechanical ventilation and during weaning.

Aim To assess lung aeration by lung ultrasonography inpatients ready for weaning and to validate the significance ofits use as a predictor of weaning outcome in comparison withrapid shallow breathing index.

Patients and methods A prospective observational studywas conducted on 30 critically ill mechanically ventilatedpatients for 48 h or more, and ready to undergo spontaneousbreathing trial (SBT), according to the readiness criteria. Itwas conducted in Beni-Suef University Hospital from October2017 to May 2018. Rapid shallow breathing index wasmeasured before initiation of SBT, and lung ultrasound wasdone, before, during, and after SBT [lung ultrasound score(LUS) 1, 2, and 3] and were used as predictors for SBToutcome. Patients were divided, according to outcome, intosuccessful weaning group (group A) and failed weaning group(group B).


Results In the failed weaning group, LUS1, LUS2, and LUS3were significantly higher than those of the successful weaninggroup. At cutoff value of 8.5, LUS3 could be used forprediction of weaning failure with sensitivity 85% andspecificity 100%. Moreover, there was a statisticallysignificant relation between LUS and the other variablesincluding hospital stay, mechanical ventilation duration, andmortality.

Conclusion Lung ultrasound is useful as a bedside tool thatcan help physicians in their weaning decisions.Egypt J Bronchol 2019 13:477–483© 2019 Egyptian Journal of Bronchology


Keywords: extravascular lung water, lung ultrasound score, rapid shallowbreathing index, spontaneous breathing trial

Departments of, aChest, bRadiology, Faculty of Medicine, Beni-Suef

University, cDepartment of Chest, Beni-Suef Chest Hospital, Beni-Suef,

Egypt

Correspondence to Radwa M. Abdelwahab, MSc, Department of Chest,

Beni-Suef Chest Hospital, Beni-Suef 62611, Egypt. Tel: +20 111 911 3563;


Received 15 January 2019 Accepted 14 July 2019






IntroductionMechanical ventilation is significant as a life-savingintervention in any sort of respiratory failure. It is acrude intervention that is only used in critically illpatients in life-threatening conditions [1]. Pulmonarycomplications of mechanical ventilation increase inincidence with the duration of ventilation, so earlysuccessful weaning is a cornerstone in prevention ofthese complications [2,3]. Weaning failure is a majorproblem that is commonly seen in critically ill patients.Weaning failure includes spontaneous breathing trial(SBT) failure [4] and postextubation distress thatrequires re-intubation or noninvasive ventilationwithin 48h after extubation [5]. Assessment ofreadiness of weaning and the ideal time for extubationplay an important role in determination of predictedhospital stay and prediction of outcome [2]. Manymechanisms are incriminated in weaning failure frommechanical ventilation, for example, alteration of lungresistance or compliance during weaning process, lungderecruitement, spontaneous breathing-induced cardiacdysfunction, andneuromusculardisorders.Most of thesefactors are associated with decreased lung aeration

during SBT and lead to weaning failure [6].Derecruitement, which could be a possible cause ofweaning failure, needs to be directly studied to predictand early diagnose failure of the weaning process [7].Different variables are used as predictors of weaning; therapid shallow breathing index (RSBI) is themost widelyused predictor of weaning success [8]. Visualization ofthe lungs was not possible by ultrasound for a long time.However, nowadays we can use the artefacts produced atthe interface between the lungs and other substances, forexample, fluids, to identify specific pathologies in thelung [9]. Lung ultrasound could be used as a predictor ofweaning failure by detection of lung aeration loss beforeweaning and during the SBT [10]. Being a bedside,noninvasive, radiation free, quicklyperformedprocedureand able to perform a dynamic assessment of changes oflung aeration makes it superior to other procedures that




can be used to assess lung aeration and to predict successof weaning process, so it needs further research andstudies to prove its efficacy and accuracy [11].

Patients and methodsThirty critically ill ventilated patients admitted toBeni-Suef University Hospital from October 2017 toMay 2018 were enrolled in the study. The studyprotocol was approved by the research ethicalcommittee of Beni-Suef University. A writtenconsent was taken from the patients’ close relativesfor agreement of enrollment in the study.

Inclusion criteriaPatients invasively ventilated for 48 h or more andready to undergo a SBT according to the traditionalreadiness criteria were included [12].

Exclusion criteriaPatients less than 18 years old; patients with traumaticlung injury or pneumothorax; patients with interstitiallung disease, bullous lung lesion, or extensivebronchiectasis; patients with chest wall lesion thatimpairs the use of chest ultrasound, for example,burn and open wound; morbid obese patients withBMI of at least 40 kg/m2; and patients with apreviously failed SBT were excluded.

Table 1 Lung aeration patterns as detected during lungultrasonography

Aerationpattern

Score Description

Normal 0 Lung sliding, A lines, and ≤3 B lines

Moderateloss

1 Multiple regularly and irregularly spacedB lines

Severe loss 2 Multiple and coalescent B lines

Completeloss

3 Lung consolidation

Study designRSBI was measured, and lung ultrasonography wasdone just before initiation of SBT [lung ultrasoundscore (LUS) 1]. Included patients were then subjectedto an SBT using (PSV) with pressure support less thanor equal to 10 cmH2O and PEEP up to 5 cmH2O.During this phase, clinical, laboratory, ABG, andventilator data were recorded. Lung ultrasonographywas done at the end of SBT (LUS2) [1]. According toweaning outcome, patients were divided into twogroups: successful weaning group (group A), whichincluded patients who successfully passed an SBT andwere weaned frommechanical ventilation with no needfor re-intubation or noninvasive ventilation in thesubsequent 48 h, and failed weaning group (groupB), which included patients who failed during SBTand were brought back to assist ventilation and patientswho required re-intubation or noninvasive ventilationwithin 48 h after extubation. Lung ultrasonographywas done 4–6 h after extubation (LUS3), andpatients were observed for 48 h to assess the need ofre-intubation or noninvasive ventilation, indicatingfailed weaning. In group B patients, LUS3 was doneon failure of the weaning process or just afterreinstitution of mechanical ventilation.

Study outcomesThe primary outcome measures were respiratorydistress requiring reintubation or noninvasiveventilation within 48 h after weaning.

The secondary outcome measures were hospitalmortality and length of ICU stay within 28 days.

MethodsLung ultrasonography was done using LOGIC PRO100 (KPI Health care company, India) and EDANDUS 60 (Global health company, China) ultrasounddevices by applying B-Mode, with the use of a convexprobe (5.5MHz). The probe was applied vertically overthe examined intercostal spaces in each region, with thepatients lying flat or in the semi-recumbent position.Lung ultrasonography was done before, during, andafter SBT. Lung ultrasound was used to assess thedegree of lung aeration loss according to LUS score[134], according to which the chest wall is divided into12 zones (six zones per side). Each hemithorax isdivided into three zones by the parasternal line,anterior axillary line (AAL), posterior axillary line(PAL) and the paravertebral line, and then eachzone is divided into upper and lower zones, aboveand below fifth intercostal space [13]. One of fourpatterns of aeration was recognized for each region(Table 1 and Fig. 1).

Statistical analysisThe software used in analysis was the version 15 of thestatistical package SPSS (self-propelled semi-submersible, Microsoft, USA). Mean and SD valueswere compared using simple t-test. Pearson’scorrelation test was used to study the associationbetween each two variables among each group fornumerical data. The probability of error (P value) upto 0.05 was considered significant. Receiver operatingcharacteristic (ROC) curve analysis was done to predictthe cutoff points of the test variables (RSBI, LUS1,LUS2, and LUS3) that best predict the binary state ofanother variable.

Figure 1

Lung aeration patterns detected by lung ultrasonography.

Figure 2

Risk factors of the included patients.

LUS VS RSBI for prediction of weaning Laz et al. 479

ResultsThe study included 30 patients; the age of the studiedpatients ranged from 19 to 84 years, with a median ageof 66.5 years and mean±SD was 60.8±20.1 years. Ofthe studied patients, 18 (60%) were males and 12 (40%)were females. Included patients had multiple riskfactors and comorbidities; 76.7% had pneumonia,

and 50% were hypertensive. Risk factors andcomorbidities of included patients are presented inFigs 2 and 3 [2].

Length of hospital stay ranged from 3 to 28 days, withmean±SD of 16.1±8.3 days. Ventilation days ofincluded patients ranged from 2 to 25 days, with

Figure 3

Comorbidities of the included patients.

Table 2 Demographic data and clinical criteria of the includedpatients

Item Range/N (%) Mean±SD

Age 19–48 60.8±20.1

Sex

Male 18 (60)

Female 12 (40)

GCS 10–15 14.1±1.58

MAP 55–78 66.6±6.7

RR 12–30 20.7±5.9

Temperature 36.5–39.5 37.5±0.9

Pulse 61–102 83.1±11.9

BMI 20–40 31.03±4.9

PaO2/FiO2 150–570 244.2±101.5

Peak pressure 19.1–38 25.5±4.4

Plateau pressure 11–28 17.3±4.6

Mean airway pressure 4–23 10.07±3.2

Static compliance 20.2–62.1 39.8±13.2

Ventilator days 2–25 9.7±6.6

Length of ICU stay 3–28 16.1±8.3

Failed weaning 20 (66.7)

Successful weaning 10 (33.3)

Survivors 16 (53.3)

Nonsurvivors 14 (46.7)

FiO2, fraction of inspired oxygen; GCS, Glasgow coma scale;MAP, mean arterial pressure; PaO2, pressure of arterial oxygen;RR, respiratory rate.


mean±SD of 9.7±6.6 days. Of the included patients, 10(33.3%) were successfully weaned; however, theremaining 20 (66.7%) were classified as failedweaning patients owing to either failure of SBT orowing to the need of re-ventilation within 48 h afterweaning. Of the included patients, 16 (53.3%) weresurvivors, whereas 14 (46.7%) patients werenonsurvivors. Demographic data and clinical criteriaof the included patients are described in Table 2.

Statistical results of included patients were correlatedwith the study outcomes; a statistically significantdifference was found between mean pressure ofarterial oxygen (PaO2)/fraction of inspired oxygenratio for patients who failed weaning (213±70) andthat for who were successfully weaned (306.2±128.3),with P value 0.015. Moreover, a statistically significantdifference was found between mean LUS1, LUS2, andLUS3 for patients who failed to be weaned and that forwho were successfully weaned, with P value less than0.05. Correlative data between different variables andstudy outcomes are illustrated in Tables 3–5.

Receiver operator characteristic curves were used to testthe LUS1, LUS2, and LUS3 and RSBI as predictors offailure of weaning from mechanical ventilation. Thepositive actual state is failed weaning (Table 6 andFig. 4). ROC curve illustrates the potential of RSBI,LUS1, LUS2, and LUS3 as predictors of weaningfailure as follows: At a cutoff value of 34.5 (25–94)the sensitivity of RSBI was 80% and the specificity was30% [area under the curve (AUC)=0.590), positivepredictive value (PPV)=69.6%, and negative predictivevalue (NPV)=42.8%]. At a cutoff value of 11.5

(1.5–20), the sensitivity of LUS1 was 90% and thespecificity was 50% (AUC=0.773, PPV=78.3%, andNPV=71.4%). At a cutoff value of 8.5 (0.5–18.5), thesensitivity of LUS2 was 80% and the specificity was70% (AUC=0.830, PPV=84.2%, and NPV=63.6%).At a cutoff value of 8.5 (2–21), the sensitivity of LUS3was 85% and the specificity was 100% (AUC=0.903,PPV=100%, and NPV=76.9%).

Table 3 Correlative data between successful and failedweaning groups

Item Failedweaning

Successfulweaning

Pvalue

Age (years) 62±18.7 58.3±23.3 0.854

Sex

Female 8 (40) 4 (40) 0.456

Male 12 (60) 6 (60)

GCS 14±1.7 14.3±1.2 0.457

MAP 59.6±2.3 71.9±6.1 0.564

RR 27.6±2.3 22.6±2.3 0.745

Temperature 37.8±0.6 37.8±0.3 0.986

Pulse 92.4±3.2 85.1±11.6 0.365

BMI 30.1±3.1 33±7 0.765

PaO2/FiO2 213.3±70 306.2±128.3 0.015

RSBI 57.8±23.65 49.8±18.2 0.390

LUS1 14.3±5.3 10.2±4.4 0.044

LUS2 10.7±4.8 6.3±2.7 0.012

LUS3 13.5±5.5 5.7±2.1 <0.001

Duration ofventilation

12.7±6 3.7±2.4 <0.001

Length of ICU stay 19.7±7.1 8.9±5.2 <0.001

FiO2, fraction of inspired oxygen; GCS, Glasgow coma scale;LUS1, lung ultrasound score during positive pressure ventilation;LUS2, lung ultrasound score during spontaneous breathing trial;LUS3, lung ultrasound score after weaning from mechanicalventilation; MAP, mean arterial pressure; PaO2, pressure ofarterial oxygen; RR, respiratory rate; RSBI, rapid shallowbreathing index.

Table 4 Correlative data between survivors and nonsurvivors

Items Survivors Nonsurvivors P value

Duration of ventilation 9.1±7.9 10.2±4.9 0.545

Length of ICU stay 15.5±9.8 16.8±6.3 0.634

LUS1 11.25±5.25 14.85±4.81 0.061

LUS2 7.87±5.14 10.86±3.65 0.082

LUS3 8.06±5.09 14.14±5.28 0.003

LUS1, lung ultrasound score during positive pressure ventilation;LUS2, lung ultrasound score during spontaneous breathing trial;LUS3, lung ultrasound score after weaning from mechanicalventilation.

Table 5 Correlation between lung ultrasound score duringspontaneous breathing trial and length of ICU stay

Length of ICU stay LUS2

Pearson correlation (r) 0.364

P value 0.048

LUS2, lung ultrasound score during spontaneous breathing trial.

Table 6 ROC curve analysis for lung ultrasound score 1, 2, 3and rapid shallow breathing index in relation to weaningsuccess

RSBI LUS1 LUS2 LUS3

Cutoff point 34.5 11.5 8.5 8.5

Low probability of failure <25 <1.5 <0.5 <2

Intermediate probability offailure

25–94 1.5–20 0.5–18.5 2–21

High probability of failure >94 >20 >18.5 >21

Sensitivity (%) 80 90 80 85

Specificity (%) 30 50 70 100

PPV (%) 69.6 78.3 84.2 100

NPV (%) 42.8 71.4 63.6 76.9

AUC 0.590 0.773 0.830 0.903

AUC, area under the curve; LUS1, lung ultrasound score duringpositive pressure ventilation; LUS2, lung ultrasound score duringspontaneous breathing trial; LUS3, lung ultrasound score afterweaning from mechanical ventilation; NPV, negative predictivevalue; PPV, positive predictive value; ROC, receiver operatingcharacteristic; RSBI, rapid shallow breathing index.


DiscussionChest ultrasound is a quick bedside test and anoninvasive reliable technique for the assessment ofpulmonary congestion [14]. This study hypothesis wasbuilt on the assumption that LUS can accurately detectextravascular lung water and quantify for the degree ofaeration loss. Hence, it can be used to detect SBT-associated lung derecruitement and can significantlypredict the results of weaning before and duringinitiation of the weaning process. This study showeda statistically significant difference between meanPaO2/fraction of inspired oxygen ratio of patientswho failed to be weaned and that of who weresuccessfully weaned, with P value 0.015. Similar tothese results, Osman and Hashim [15] stated that themean value PaO2 was higher in the successful weaning

group than the failed weaning group, with mean valuesof 69.7 and 46.6, respectively, which was consideredstatistically significant.

This study could not find a statistically significantdifference between failure and successful weaninggroups regarding any of the clinical and laboratorydata. Similarly, Tenza-Lozano et al. [16] performeda prospective cohort study including patients ventilatedfor more than 24 h and ready to be weaned. Nosignificant relation between hemodynamicmonitoring and study outcomes could be detected.Against these results, Haji et al. [17] found thatthere was a significant increase in the respiratoryrate for those who failed weaning than those whosucceeded, with P value of 0.02. This differencefrom this study can be explained by singlemeasurement of the hemodynamic parameters atinitiation of the SBT and were not followed aftersuccess or failure of SBT. Moreover, in this study,respiratory rate was estimated on pressure supportventilation not on T-piece.

This study did not find any significant statisticaldifference in mean values of RSBI betweensuccessful weaning group and failed weaning group.Similarly, Tenza-Lozano et al. [16] could not detect astatistical significance in the difference of mean values

Figure 4

ROC curve analysis.


of RSBI in the failed group versus that of the successfulgroup. In the contrary, Osman and Hashim [15] foundthat RSBI was higher in the failure than the successgroup, with median values of 113.9 and 71.9,respectively, which was considered statisticallysignificant.

A statistically significant difference was found betweenmean LUS1, LUS2, and LUS3 for patients who failedto be weaned and that for who were successfullyweaned. In the same context, Shoaeir et al. [18]performed lung ultrasound for all patients beforeweaning, during SBT, and 6 h after extubation. LUSscore was higher in the failed weaning group than thesuccessful weaning one, with P value less than 0.001[3]. Similarly, Haji et al. [17] found that LUS in theanterior and lateral chest wall regions was significantlydifferent between successful and failed group, withmedian values of 11 and 17, respectively, with Pvalue of 0.007. Moreover, it showed a lowersignificant statistical analysis when comparing thetotal score by adding the posterior wall score, withthe result of weaning with median values of 22 and 18for the successful and failed groups, respectively, with Pvalue of 0.06.

Moreover, Soummer et al. [19] proved that LUS beforeSBT trial was 6–13 in the successful group and 13–17in the failure group, with P value less than 0.001. At theend of SBT, failed weaning group showed LUS 16–21,which is extremely higher than the successful group,7–13, with P value less than 0.001. Following 4–6 h

after extubation, failed weaning group showedsignificant derecruitement in lung aeration, withLUS ranging from 17 to 23, against the successfulgroup, with LUS ranging from 7 to 15, with P valueless than 0.001.

Similar to this work, Banerjee and Mehrotra [20]found a significant relation between weaning resultsand LUS, with mean values of 7.23±3.69 and 20.77±5.79 for successful and failed groups, with P value lessthan 0.0001.

In this study, there was a statistically significantdifference in the duration of mechanicalventilation between patients who failed theweaning process and those who were successfullyweaned from mechanical ventilation. Similarly,Shoaeir et al. [18] found a significant relationbetween days on mechanical ventilation andweaning results, with P value less than 0.001.Moreover, Banerjee and Mehrotra [20] found thatduration of mechanical ventilation was higher infailed group, with P value less than 0.0001.

This study showed a significant difference in the lengthof ICU stay between failed and succeeded weaninggroups of patients. In the same context, Shoaeir et al.[18] found that the length of stay in ICU wassignificantly higher in the failed group, with P valueless than 0.001. Similarly, Tenza-Lozano et al. [16]showed with statistically significant values that failedweaning group of patients had a higher duration of


ICU stay than successfully weaned group, with P valueless than 0.002.

This study showed a significant linear correlationbetween LUS2 and length of ICU stay, with P valueof 0.048. In this context, Tierney et al. [21] found thathigher LUS was positively associated with increasedlength of ICU stay (P=0.003).

ROC curve was plotted to test RSBI, LUS1, LUS2,and LUS3 as predictors of weaning, and results wereconcomitant with the results of Soummer et al. [19]who found that LUS at the end of SBT more than 17was highly specific for predicting postextubationdistress with AUC=0.86, and a LUS score up to 12was highly sensitive for excluding postextubationdistress. Moreover, Osman and Hashim [15] foundthat SBT-LUS less than 12 has high probability forsuccess, 12–17 for intermediate probability for success,and more than 17 for high probability for failure. Inanother way, Tenza et al. [16] found that the optimalcutoff point for successful weaning was 7, withsensitivity 76% and specificity 73% (AUC=0.80).

ConclusionLung ultrasonography is a useful method in assessmentof the degree of lung aeration during positive pressureventilation, during and after SBT. So, it can be used asa good predictor of the weaning results. The bestpredictive measurement for LUS is at 4–6 h after asuccessful weaning trial. Early prediction of weaningfailure by LUS can be helpful to prevent re-intubationby early initiation of NIV before actual failure.

AcknowledgementsThis study was supported by Beni-Suef UniversityHospital.



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Clinical outcome of weaning in mechanically ventilated patientswith chronic obstructive pulmonary diseaseSuzan Salama Sayeda, Khalid Hussein Ahmeda, Sayed Abdelsabour Kinawyb,Islam Galal Sayedb

Background Chronic obstructive pulmonary disease(COPD) represents a significant reason for mortality andmorbidity worldwide that induces a high socioeconomicburden, with exacerbations necessitating mechanicalventilation representing a major aspect of illnessmanagement. Many patients with COPD frequentlypresented with troubles in the liberation from mechanicalventilation. The aim of the current study was to verify thevalidity of the weaning categorization that is classifiedaccording to the difficulty and length of the weaningprocedure in mechanically ventilated patients with COPD andits effect on the different clinical and mortality outcomevariables.

Patients and methods A total of 102 patients with COPDwho achieved the weaning criteria were classified accordingto the length and difficulty of weaning procedure into simpleweaning group (n=60, 58.8%) and nonsimple weaning group(which include difficult and prolonged weaning categories)(n=42, 42.2%). The outcome measures are the length ofmechanical ventilation, the duration of ICU stay, and lastly themortality rate.

Results Regarding baseline data recorded at admission, nosignificant difference between both weaning groups wasfound apart from Acute Physiology and Chronic Health


Evaluation score II. The nonsimple weaning group hadconsiderably higher duration of invasive mechanicalventilation, length of ICU stays, and lastly the mortality rate, incomparison with the simple weaning group.

Conclusion Weaning categorization according to the lengthand the difficulty of the weaning procedure may be used as asuitable predictor of outcome in severe COPD exacerbationwith the requirement for invasive mechanical ventilation.Egypt J Bronchol 2019 13:484–488



Keywords: chronic obstructive pulmonary disease, mechanical ventilation,simple weaning, weaning

aProfessor of Chest Diseases and TB, Faculty of Medicine, Assiut

UniversitybLecturer of Chest Diseases and TB, Faculty of Medicine, Aswan

University, Egypt

Correspondence to Islam Galal Sayed, MD in Chest Diseases and

Tuberculosis, Lecturer of Chest Diseases and TB, Faculty of Medicine,

Aswan University, 17111, Egypt. Tel: 01116681549;


Received 21 January 2019 Accepted 19 May 2019






IntroductionMorbidity and mortality owing to chronic obstructivepulmonary disease (COPD) represents a worldwidepandemic. Severe COPD exacerbations may requiremechanical ventilation, which is lifesaving [1]. Oncemechanical ventilation is initiated, planning for weaningoff should be started. Timing of weaning is crucial toavoid many complications. Weaning off mechanicallyventilated patients with COPD is considered a majorchallenge in critical care practice. Weaning wascategorized according to the length and difficulty ofthe weaning procedure into three categories (simple,difficult, and prolonged weaning categories),according to the latest recommendations [2].

Weaning should be performed early as soon as possible.It is important to recognize those patients at the time ofadmission who are likely to have poor outcome, so thatsuch patients can be monitored closely and managedaggressively [3].

A topic of constant research for over many years is thedifficulty to predict the weaning outcome in patientswith COPD undergoing mechanical ventilation [2].

This study aimed to verify the validity of the weaningclassification and its effect on the outcome inmechanically ventilated patients with COPD.

Patients and methodsStudy design and settingThis is an observational study conducted in a RICU,during the period from October 2017 to April 2018.

Study populationA total of 183 patients with COPD requiring invasivemechanical ventilation were admitted in our RICU[ventilation was performed with the Puritan-Bennett840 ventilator (Nellcor Puritan-Bennett 840 ventilator,Florida, USA)]. Of them, 81 patients did not startweaning either because of death in 66 patients orunplanned extubation in 15 patients. The remaining



Figure 1

Scheme of the result analysis. The flow chart shows that the study includes 183 patients; however, 81 patients were excluded as 66 patients diedbefore the start of weaning and 15 patients had unplanned extubation. Therefore, the included 102 patients were classified into simple weaninggroup (60 patients) and nonsimple weaning group (42 patients).

Mechanically ventilated patients with COPD Sayed et al 485

102 patients met the weaning criteria and startweaning, as shown in Fig. 1.

Inclusion criteriaThe study included all patients with COPD requiringinvasive mechanical ventilation with the followingcriteria: respiratory rate more than or equal to 35breath/min, use of accessory muscles of respiration,disturbed conscious level, respiratory acidosis, andPaO2/FiO2 less than 200.

Exclusion criteriaThe exclusion criteria include age less than 18 years,patients with central nervous system disordersunrelated to hypercapnic encephalopathy orhypoxemia, patients with postarrest encephalopathy,and patient with previous tracheostomy.

Study classificationPatients were classified according to the duration anddifficulty of weaning procedure into two groups: simpleweaning group included 60 patients who tolerated thefirst trial of spontaneous breathing, and nonsimpleweaning group (including difficult and prolongedweaning category) included 42 patients, of which thedifficult weaning category included 37 patients whofailed first spontaneous breathing trial (SBT) butsucceeded on second or third trial within 7 days, andthe prolonged weaning category included five patientswho did not tolerate at least three weaning trials orwhen weaning duration exceeded 7 days from the firsttrial of weaning.

The following data were recorded:

(1)
Demographic data including age, sex, smoking,and BMI.
(2)
Arterial blood gases. (3) Full laboratory assessment. (4) Hemodynamic data, including mean arterial blood
pressure, calculated as diastolic pressure+1/3 pulsepressure, respiratory rate (RR), heart rate, andtemperature.

(5)
Acute Physiology and Chronic Health Evaluation(APACHE) II score was measured on the day ofICU admission. APACHE II score consists of 12variables including, vital signs (heart rate, meanarterial blood pressure, RR, temperature, andGlasgow coma score), variables derived fromroutine venous blood tests (hematocrit, whiteblood cell count, serum potassium, serum sodium,and serum creatinine), and two variables derivedfrom arterial blood gas tests (serum pH and PaO2).
(6)
Ventilatory data were recorded at admission andafter half hour of SBT, including spontaneous tidalvolume, RR, minute ventilation, peak pressure,plateau pressure, and static compliance. Valueswere displayed on the ventilator and we used theaverage of three breaths. In addition, weaningindices were recorded including rapid shallowbreathing index (RSBI) obtained by dividingspontaneous RR by exhaled tidal volume (l) andthe new integrative weaning index (IWI) calculatedas (Cst, rs×SaO2/RSBI), in which Cst, rs is staticcompliance and SaO2 is arterial oxygen saturation.

Table 2 Ventilator parameters at the end of the firstspontaneous breathing trial

Simpleweaning(N=60)

Nonsimpleweaning (N=42)

Pvalue

Exhaled tidalvolume (ml)

360±80 280±90 0.000*

RR (breath/min) 23.92±4.60 32.98±5.62 0.000*

P peak (cmH2O) 21.58±3.29 21.02±2.99 0.711

P plateau (cmH2O) 18.02±2.28 17.55±2.32 0.450

V?E(l/min) 7.34±1.54 8.85±2.01 0.000*


Allpatientswhofulfilled the subsequentweaningcriteriaunderwent SBT, which comprised the respiratorycriteria, including sufficient inspiratory effort, PaO2

more than 60 mmHg or SaO2 more than or equal to90% or more with FiO2 less than or equal to 0.4, andnormal or baseline PaCO2, and cardiovascular criteria,involving heart rate less than or equal 140 beats/min,absence ofmyocardial ischemia andwithin normalmeanarterial bloodpressurewithout thenecessity for theuse ofvasopressors, patient is arousable or has Glasgow comascore more than or equal to 13, and there were noelectrolyte disturbances.

Ethical considerationThe study was agreed by the Institutional EthicsCommittee Faculty of Medicine. Moreover, a writtenconsent was given by the surrogate decision maker.

Statistical analysisStatistical analysis was done by SPSS, version 21 (IBMInc., Armonk, New York, USA). Nonparametric testswere used in the current study.

Auto-PEEP(cmH2O)

1.34±0.58 1.28±0.60 0.634

Static compliance(ml/mmHg)

49.40±14.09 32.42±11.06 0.000*

Data are expressed as mean±SD. P peak, peak pressure; Pplateau, plateau pressure; PEEP, positive end expiratory pressure;RR, respiratory rate; V?E, minute ventilation. P value less than0.05 is considered statistically significant. *means significant.

ResultsDemographic data and patient characteristics atadmission of both groups of weaning aresummarized in Table 1. There was a majorsignificant variation between both groups regarding

Table 1 Demographic data and patient characteristics for the studi

Simple weaning (N=60

Demographic data

Age (years) 59.70±12.36

Sex

Male 42 (70.0)

Female 18 (30.0)

BMI (kg/m2) 28.64±6.83

Smoking

Smoker 41 (68.3)

Nonsmoker 19 (31.7)

Smoking index 29.84±6.04

ABG at admission

pH 7.24±0.07

PaCO2 (mmHg) 82.55±19.22

PaO2 (mmHg) 52.22±17.51

HCO3 37.30±9.75

SaO2 (%) 75.57±15.62

PaO2/FiO2 130.54±43.77

Hemodynamic data at admission

Mean blood pressure (mmHg) 86.60±22.46

Respiratory rate (breath/min) 16.39±4.48

Heart rate (beat/min) 116.42±18.94

Temperature (°C) 37.90±0.60

APACHE II score 19.27±2.74

Data are presented as mean±SD, median (range) or n (%). ABG, arteriEvaluation. Nonsmoker; those who never smokers.

the APACHE II score, whereas there was nosignificant difference regarding the other parameters.

Regarding the ventilator parameters at the end of thefirst SBT, the simple weaning group illustrated highlysignificant higher values for exhaled tidal volume andstatic compliance and lower values for RR and exhaledminute ventilation, whereas regarding peak pressure,plateau pressure, and auto-positive end expiratorypressure, no significant differences between bothgroups were observed (Table 2).

ed groups

) Nonsimple weaning (N=42) P value

61.19±11.64 0.437

29 (69.0) 0.918

13 (31.0)

27.62±6.32 0.644

25 (59.5) 0.360

17 (40.5)

28.88±7.42 0.337

7.23±0.09 0.746

80.79±22.24 0.930

53.64±22.98 0.806

34.40±10.26 0.132

73.50±17.63 0.703

134.11±57.45 0.806

83.26±21.48 0.528

20.39±5.38 0.583

115.19±17.24 0.528

38.04±0.36 0.12

22.62±3.66 0.001*

al blood gases; APACHE, Acute Physiology And Chronic Health

Mechanically ventilated patients with COPD Sayed et al 487

Simple weaning group was associated with highervalues of RSBI and IWI (Table 3), whereasnonsimple weaning was associated with higherlength of mechanical ventilation, duration of ICUstays, extubation failure percentage, tracheostomyneed, need for noninvasive mechanical ventilationafter weaning and its duration, and mortalityoutcome (Table 4).

DiscussionWe found that the incidence of simple weaning in ourstudy was 58.8, difficult weaning was 36.3, andprolonged weaning was 4.9. Similar to our results,one previous study found that the prevalence of thethree weaning categories was 54, 26, and 19%,respectively [4]. Moreover, another study reportedthat the simple weaning incidence was three timesmore common than the other weaning categories [5].

Regarding the demographic data of the studiedpatients in the current study, both groups matchedregarding age, sex, duration of the disease, BMIs, andsmoking status (P>0.05). Similar results weredemonstrated by Elgazzar et al. [6] who found thatthe weaning outcome was not affected by the age, sex,or the state of smoking.

Table 4 Outcome variables

Simple weanin

Total duration of mechanical ventilation (days) 3.66±1

Length of ICU stay (days) 7.53±5

Need for NIV

Yes 19 (31

No 41 (68

Duration of NIV (days) 1.68±1

Extubation outcome

Success 52 (86

Failure 8 (13.

Need for tracheostomy

Yes 2 (3.3

No 58 (96

Mortality outcome

Died 3 (5.0

Discharge 57 (95

Data are expressed as mean±SD and n (%). NIV, noninvasive mechanisignificant. *means significant.

Table 3 Weaning indices

Simple weaning (N=60)

RSBI (breath/min/l) 79.47±24.78

IWI 68.69±39.51

Data are expressed as mean±SD. IWI, integrative weaning index; RSBIconsidered statistically significant. *means significant.

APACHE II score is a major important score used toevaluate the disease severity and the expected mortality.In the current study, there was a major differencebetween both groups in APACHE II score. Thisresult was in harmony with the results of someprevious studies which detected that weaning failuremostly associated with a higher levels of APACHE IIscore [7,8], but disagree with other studies [9,10].

Regarding the ventilator parameters at the end of thefirst SBT, we observed that the nonsimple weaninggroup shows lower levels of the exhaled tidal volumeand exhaled minute ventilation, but no significantdifferences were noted regarding other ventilatorparameters. Sellares et al. [11] reported similarresults. Exhaled tidal volumes above 5ml/kg havebeen considered as good weaning predictors [12].However, Monaco et al. [13] found opposite results.

Regarding static compliance measured at the end of thefirst SBT, we summarized low levels of staticcompliance in the nonsimple weaning group, andthis agrees with one study, which found that therewas a significantly decreased respiratory complianceand increased respiratory resistance in the weaningfailed group [14], but this result is in contrast withMabrouk et al. [15].

g (N=60) Nonsimple weaning (N=42) P value

.31 4.44±2.55 0.042*

.26 11.64±7.79 0.000*

.7) 24 (57.1) 0.010*

.3) 18 (42.9)

.19 2.71±1.59 0.004*

.7) 26 (64.3) 0.003*

3) 16 (35.7)

) 7 (16.7) 0.031*

.7) 35 (83.3)

) 18 (42.9) 0.000*

.0) 24 (57.1)

cal ventilation. P value less than 0.05 is considered statistically

Nonsimple weaning (N=42)

142.02±50.74 0.035*

26.45±17.05 0.000*

, rapid shallow breathing index. P value less than 0.05 is


RSBI was the most commonly studied index to expectthe weaning outcome. The value of RSBI in the presentstudy was significantly higher in the nonsimpleweaning group (89.9±31.7 vs. 83.7±21.6). Theseresults are concordant with previous studies [15–17],which found that RSBI was significantly less than orequal to 100 in their patients who tolerated the SBTcompared with those who failed SBT. However, thisresult is in contrast with two other studies [10,18]. Weobserved a significant difference in term of IWIbetween both groups of weaning, Sellares et al. [11]reported similar results.

Ghoneim et al. [19] found that there was a significantlyshorter duration of mechanical ventilation in thepatients who passed the SBT than those who failed(P<0.01). These results agree with ours finding.

We also summarize a significant difference betweenboth groups regarding the length of ICU stay, and thisagrees with Mahmoud and Mohamad [20] who foundthat the longer duration of ICU stay was one of themost important factors associated with unsuccessfulweaning.

In the present work, we observe a significant differencebetween both groups regarding the need fornoninvasive mechanical ventilation after weaning.Sellares et al. [11] documented similar results. ICUmortality in our study was significantly increased in ournonsimple weaning group compared with simpleweaning group. In agreement, Boles et al. [2] intheir study found that there was a difference in themortality between the three weaning categories.

Limitations of the studyOur study is a single-center study with a small sampleof selected patients and also has specific clinicalcharacteristics of the study population (patients withCOPD). So we need many future studies with a largenumbers of patients to emphasizing our results.

Summary and conclusionWeaning categorization according to the length andthe difficulty of the weaning procedure in mechanicallyventilated patients with COPD may be considered asan acceptable predictor of outcome.



References1 Díaz MC, Ospina-Tascón GA, Salazar BC. Respiratory muscle

dysfunction: a multicausal entity in the critically ill patient undergoingmechanical ventilation. Arch Bronconeumol 2014; 50:73–77.

2 Boles JM, Bion J, Connors A, Herridge M, Marsh B, Melot C, et al.Weaning from mechanical ventilation: statement of the Sixth InternationalConsensus Conference on Intensive Care Medicine. Eur Respir J 2007;29:1033–1056.

3 Nemer SN, Barbas CS, Caldeira JB, Cárias TC, Santos RG, Almeida LC,et al. Anew integrative weaning index of discontinuation from mechanicalventilation. Crit Care 2009; 13:R152.

4 Tonnelier A, Tonnelier JM, Nowak E, Gut-Gobert C, Prat G, Renault A,et al. Clinical relevance of classification according to weaning difficulty.Respir Care 2011; 56:583–590.

5 Saiphoklang N, Auttajaroon J. Incidence and outcome of weaning frommechanical ventilation in medical wards at Thammasat UniversityHospital. PLoS one 2018; 13:e0205106.

6 Elgazzar AE, Walaa M, Salah A, Yousif AR. Evaluation of the minuteventilation recovery time as a predictor of weaning in mechanicallyventilated COPD patients in respiratory failure. Egypt J Chest DisTuber 2013; 62:287–292.

7 Zaytoun T, Mahrous A, Dawood A, Elsammk M, Abd-El-Halim A, Attia A.Chromogranin A (CgA) as a marker of weaning in mechanically ventilatedcritically ill septic patients. Egypt J Chest Dis Tuber 2014; 63:955–961.

8 Islam S. APACHE score as a predictive index for weanability frommechanical ventilation. Bangladesh Crit Care 2013; 1:18–22.

9 Sanabria A, Gómez X, Vega V, Domínguez LC, Osorio C. Prediction ofprolonged mechanical ventilation for intensive care unit patients: a cohortstudy. Colomb Med 2013; 44:184–188.

10 Lee CS, Chen NH, Chuang LP, Chang CH, Li LF, Lin SW, et al.Hypercapnic ventilatory response in the weaning of patients withprolonged mechanical ventilation. Can Respir J 2017; 2017: 7381424.

11 Sellares J, Ferrer M, Cano E, et al. Predictors of prolonged weaning andsurvival during ventilator weaning in a respiratory ICU. Intensive CareMed2011; 37:775–784.

12 Holliday JE, Hyers TM. The reduction of weaning time from mechanicalventilation using tidal volume and relaxation feedback. Am Rev Respir Dis1990; 141:1214–1220.

13 Monaco F, Drummond GB, Ramsay P, Servillo G, Walsh TS. Do simpleventilation and gas exchange measurements predict early successfulweaning from respiratory support in unselected general intensive carepatients? Br J Anaesth 2010; 105:326–333.

14 Metwally A, Hussein K, El-Abdeen AZ, Hamed A, Ez-Eldeen A. The impactof pulmonary functions on outcome of intubated patients with chronicobstructive pulmonary disease. Egypt J Broncho 2015; 9:125–132.

15 Mabrouk AA, Mansour OF, El-Aziz AA, Elhabashy MM, Alasdoudy AA.Evaluation of some predictors for successful weaning from mechanicalventilation. Egypt J Chest Dis Tuber 2015; 64:703–707.

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Diaphragm and weaning from mechanical ventilation:anticipation and outcomeRasha M. Abdelhafeez, Ahmed M. Abumossalam, Eman O. Arram,Mohsen M. Elshafey, Mohammed E. Abushehata

Background Determining the optimal moment to extubate acritically ill patient remains a challenge. The parameters ofdiaphragm sonography offer precious data in the evaluationand follow-up of critically ill patients onmechanical ventilation.

Aim To evaluate the diaphragm role in the weaning outcomethrough the following objectives: detect the associationbetween ultrasonographic parameters of diaphragm[thickness, excursion, and velocity of contraction (slope)] andweaning outcome, success, or failure, in addition toevaluation of the weaning process by measuring the totalduration of ventilation, weaning duration, ICU stay, andreventilation.

Patients and methods A longitudinal, observational,prospective study. The primary endpoint was weaningoutcome (failed or successful), while the secondary endpointsincluded length of ICU stay, weaning duration, ventilationduration, presence or absence of complications, andmortality. It was conducted on 240 (138men and 102 women)invasively mechanically ventilated patients aged between 20and 78 years were chosen from our Respiratory ICU of ChestDepartment, Mansoura University Hospital.

Results There were statistically significant higher values of allsonographic measurements in the survived compared with


the died group (P<0.001). There was statistically significantlyhigher sonographic diaphragmatic measurements in thesuccessful group compared with the failed group (P<0.001).

Conclusion The measurement of percent change ofdiaphragmatic thickness as well as excursion and slope couldbe applied in correlation more with weaning outcome with asensitivity of 100% and specificity of 97.4%.Egypt J Bronchol 2019 13:489–497© 2019 Egyptian Journal of Bronchology


Keywords: Acute Physiology and Chronic Health Evaluation score,diaphragmatic ultrasound, excursion, mechanical ventilation, thickness,weaning

Thoracic Medicine Department, Mansoura University Hospital, Mansoura,

Egypt

Correspondence to Ahmed M. Abumossalam, Associate professor (MD) of

Pulmonary and Critical Care Medicine, Department of Pulmonary and

Critical Care Medicine, Faculty of Medicine, Mansoura University,

Mansoura, 35516, Egypt. Tel: +20 100 842 4320; fax: +2 050 2202834;


Received 12 February 2019 Accepted 22 May 2019






IntroductionDetermining the optimal moment to extubate acritically ill patient remains a challenge. Prematureremoval of mechanical ventilation (MV) entails ahigh risk of weaning failure, prompting reintubationthat exposes the patient to unnecessary hemodynamicand respiratory stress. Thus, both early and delayedweaning are associated with increased mortality, stay inthe ICU, and economic cost [1].

The process of weaning from ventilatory support takesalmost 40% of the time spent on a ventilator. Weaningindices such as spontaneous tidal volumes, minuteventilation, and rapid shallow breathing index(RSBI) have been used extensively in clinicalpractice for weaning purposes. These parametersmeasure the overall respiratory volumes produced bythe muscles of breathing and do not take into accountthe independent contribution of the diaphragm whichis a major determinant to delayed weaning,highlighting the significance of diaphragmaticassessment [2].

Assessment of respiratory muscles, particularly thediaphragm, is lacking in the daily practice of ICUs

despite the strong evidenced diaphragmaticdysfunction in ICU patients on MV [3].

Imaging plays an imperative role with clinical data inthe evaluation and management of ICU patients [4],consequently ultrasound in ICUs is growing [5] due tosafety, rapidity, easy carriage and hopeful outcomeachieved for management [6]. Ultrasonographyoffers informations in real time about the organ’sfunction and morphology as well as evaluation of thestatus of aeration of pulmonary parenchyma [7] andfunctional status of the diaphragm offering hints on thepossibility of successful weaning [8].

Ultrasonography is an easy noninvasive technique toevaluate diaphragmatic movement characters such asforce, amplitude, and speed of contraction (slope),particular outline of movement, and alterations inthe thickness of diaphragm. The parameters of




diaphragm sonography offer precious data in theevaluation and follow-up of critically ill patients onMV, and help to understand weaning failure [9].

AimThe aim of our study is to evaluate the role ofdiaphragm in weaning outcome through thefollowing objectives: Detect the association betweenultrasonographic parameters of diaphragm [thickness,excursion and velocity of contraction (slope)] andweaning outcome, success, or failure, in addition toevaluation of the weaning process by measuring totalduration of ventilation, weaning duration, ICU stay,and reventilation.

Patients and methodsPatientsTwo hundred and forty (138 men 102 women)invasively MV patients aged between 20 and 78years were chosen from our respiratory ICU ofChest Department, Mansoura University Hospitalfrom April 2015 to May 2017.

Ventilators used were of event medical inspiration type;Event medical inspiration LS ventilator version 3.2.5Event Medical Limited (Ireland). All patients weresubjected to pressure-controlled ventilation withsedation with midazolam and possible musclerelaxation with atracurium for 24–48 h and werethen subjected to intermittent mandatory ventilation(PSIMV) for 24–48 h followed by spontaneous mode(PSV) for 12–24 h, then extubation if the patient didnot improvewith subsequent failure. Spontaneousmode used was pressure support ventilation.

Exclusion criteria

(1)
Age less than 18 years. (2) MV due to:
(a) Before ICU admission cardiorespiratoryarrest.

(b) Advanced pulmonary fibrosis.(c) Pulmonary metastasis.(d) Kyphoscoliosis.(e) Neuromuscular disorders.
Hepatosplenomegaly/tense ascites. (3)
(4)
Patients who died before third day of ventilation. (5) Pregnancy more than 28 weeks.
Study designA longitudinal, observational, and prospective study.The primary endpoint was weaning outcome (failed or

successful), while the secondary endpoints includedlength of ICU stay, weaning duration, ventilationduration, and presence or absence of complicationsand mortality.

Weaning outcome

(1)
Weaning success [10]:(a) Simple weaning: weaning duration less than
week and patient weaned successfully from thefirst weaning trial.

(b) Difficult weaning: weaning duration up toweek and patient weaned successfully afterthree weaning trials.

Weaning failure [6]:
(2) Weaning failure was defined as one of thefollowing:
(a) Nonscheduled extubation.(b) Need for reintubation.(c) Need for noninvasive positive pressure

ventilation of the patient within 48 h afterextubation.

(d) Delayed extubation.(e) Tracheostomy.

Patients and methodsThe study was conducted after prior approval fromMansoura Medical Research Ethics Committee andobtaining patient’s relatives’ written informedconsent.

For all patients, the following steps were done

(1)
Clinical evaluation with attention to age, sex,original disease, and cause of admission torespiratory ICU.
(2)
Admission severity assessment by using AcutePhysiology and Chronic Health Evaluation II(APACHE II) score.
(3)
Daily weaning checklist parameters recording. (4) Daily assessment for spontaneous breathing trail. (5) Riker sedation/agitation scale recording: evaluating
patient orientation and synchronization [11].
(6) Transthoracic echopulmonography at first day of
MV, at the start of weaning and just beforeextubation.

The ICU attendant physicians were blinded to theultrasound result. Patients were managed and weanedaccording to our ICU protocol. On the first day ofMV,all patients were on mandatory ventilation (PC, VC,PRVC). Patients were put on spontaneous mode when

Fig. 1

Diaphragm and weaning from MV Abdelhafeez et al. 491

fulfilling the four weaning checklist criteria plusbecoming afebrile (T<38C, 100.4°F).

Ultrasonographic examinations were carried out by twoinvestigators using a portable ultrasonography(Mindray DP-2200; Shenzhen Lontek ElectronicTechnology Co., Limited, China) with 3.5–5MHzcurved and 7–10MHz linear high-frequency probetransducers for adult. Ultrasonography wasperformed in supine position; using both motion(M) mode and two-dimensional brightness (B)mode, find the best approach and select theexploration line of each hemi-diaphragm. Liver wasused as a window on right hemi-diaphragm, and thespleen was used on the left hemi-diaphragm.

Right hemi-diaphragm ultrasonographyThe probe was placed between the midclavicular andanterior axillary lines, in the subcostal area, anddirected medially, cranially, or dorsally, so that theultrasound beam reached perpendicularly theposterior third of the right hemi-diaphragm (Fig. 1).

(a) Probe position for B and M-mode diaphragmatic excursion meas-urements with 3.5–5MHz probe. (b) B-mode diaphragm sonography.The bright line reflects the diaphragm. (c) M-mode diaphragm so-nography. Arrows indicate the beginning and the end of the diaphrag-matic contraction.

Left hemi-diaphragm ultrasonographyA subcostal or low intercostal probe position waschosen between the anterior and midaxillary lines toobtain the best imaging of the left hemi-diaphragm.

Multiple respiratory cycles of at least three cycles wereexamined and recorded and the average was taken.Every side measurements (right and left hemi-diaphragms) were measured and calculated separatelyand then the average of measurements of the two sideswas calculated.

Statistical analysisThe collected data were coded, processed, and analyzedusing the statistical package for the social sciences(SPSS) version 15 for Windows (SPSS Inc.,Chicago, Illinois, USA). Qualitative data werepresented as number and percent. Comparisonbetween groups was done by χ2-test, for example:comparison between different diaphragmaticfunctions in sonographic-based diaphragmaticfunction score. Quantitative data were tested fornormality by the Kolmogorov–Smirnov test.Normally distributed data were presented as mean±SD. Student’s t-test was used to compare betweentwo groups, for example: comparison of sonographicdiaphragmatic measurements in successful and failedgroups just before extubation. F-test (one-way analysisof variance) was used to compare between more thantwo groups, for example: comparison of sonographic

diaphragmatic measurements in different weaningmethods. P value less than 0.05 was considered tobe statistically significant. The receiver operatingcharacteristic curve was used to determine the cutoffvalues of sonographic measurements [percent changein diaphragmatic thickness (tdi%), excursion and slope]that predict weaning success.

ResultsTwenty-seven patients from the 267 patients did notcomplete the study for being dead before the third dayof ventilation. Two hundred and forty (138 men and102 women) patients completed the study, their ageranged from 20 to 78 years. From the 240 patients, 117had successfully weaned from which 111 patients werewith simple weaning and six patients were withdifficult weaning, and 123 patients had failedweaning from which 99 patients were with terminalextubation, 12 patients with reventilation, sevenpatients with delayed extubation, and five patientswere with tracheostomy (Table 1).


Pneumonia was the disease with the highest percentagein this study (47.5%), followed by mixed diseases(47.1%) and then chronic obstructive pulmonarydisease (COPD) (27.1%).

There was statistically significant higher APACHE IIscore (P<0.001) (indicating more severity) in the failedcompared with the successful group. Duration of ICUstay was statistically significantly higher in thesuccessful group compared with the failed group(P=0.001). Duration of using sedation per hour was

Table 1 Primary diagnosis of ventilated patients

N (%)

Pneumonia 123 (51.3)

ARDS 16 (6.7)

COPD 65 (27.1)

SDB 31 (13)

Bronchiactasis 7 (2.9)

Cavitary lesions 6 (2.5)

Alveolar hemorrhage 4 (1.7)

Lung cancer 5 (2.1)

Pulmonary embolism 12 (5.0)

Bronchial asthma 2 (0.8)

TB 13 (5.4)

DPLD 9 (3.7)

Combined pulmonary diseases 113 (47.1)

ARDS, acute respiratory distress syndrome; COPD, chronicobstructive pulmonary disease; DPLD, diffuse parenchymatouslung disease; SDB, sleep-disordered breathing; TB, tuberculosis.

Table 3 Sonographic measurements during the course of ventilatio

N

First day of ventilation

Percent change of diaphragmatic thickness 240

Diaphragmatic excursion 240

Diaphragmatic slope 240

At the start of weaning




Just before extubation




Table 2 Relation of Acute Physiology and Chronic Health Evaluatioduration, and duration of using sedation per hour to weaning outc

Weaning outcome

Failed (N=123) Successful (N=117)

APACHE II score 24.65±5.92 15.19±5.49

ICU stay duration 5.77±2.90 7.00±2.50

Weaning duration 3.65±2.09 3.51±1.62

Ventilation duration 5.02±2.29 5.11±2.44

Duration of sedation/h 62.85±11.36 30.37±11.08

APACHE II score: acute physiology and chronic health evaluation II sco

statistically significantly higher in the failed group(indicating prolonged mandatory ventilation)compared with the successful group (P<0.001),while there were no statistical significance ofweaning and ventilation duration (Table 2).

There was statistically significant higher APACHE IIscore (indicating more severity) in died compared withthe survived group (P<0.001). ICU stay duration wasstatistically significantly higher in the survivedcompared with the died group (P=0.016). Durationof using sedation per hour was statistically significantlyhigher in the died compared with the survived groupwith P value less than 0.001, while there were nostatistical significance of weaning duration andventilation duration (Tables 3–5).

There were statistically significantly higher values of allsonographic measurements in the survived comparedwith the died group (P<0.001). There was statisticallysignificantly higher sonographic diaphragmaticmeasurements in the successful group compared withthe failed group (P<0.001; Tables 6 and 7).

DiscussionThe appropriate timing of weaning from MV isfundamental in critically ill patients. Inappropriate

n

Range Mean±SD

17–76% 32±0.11

0.89–2.50 cm 1.79±0.42

0.54–1.70 cm/s 1.12±0.67

26.0–76.05% 32.14±12.97

0.90–2.54 cm 1.80±0.44

0.55–1.70 cm/s 1.69±0.40

31.39–76.05% 42.67±10.22

1.28–2.52 cm 2.23±0.19

1.04–1.72 cm/s 1.45±0.15

n II score, ICU stay duration, weaning duration, ventilationome

P P

Survived (N=141) Died (N=99)

<0.001 17.16±6.78 24.13±6.29 <0.001

0.001 6.73±2.52 5.86±3.04 0.016

0.593 3.52±1.65 3.68±2.15 0.593

0.756 5.17±2.79 4.99±2.35 0.600

<0.001 35.96±11.88 62.75±10.87 <0.001

re.

Table 4 Sequential sonographic measurements relation to weaning outcome

Weaning outcome P

Failed (N=123) Successful (N=117)

First day of mechanical ventilation

Percent change of diaphragmatic thickness 24.63±3.49 40.36±10.64 <0.001

Diaphragmatic excursion 1.40±0.18 2.15±0.16 <0.001

Diaphragmatic slope 0.79±0.44 1.39±0.16 <0.001

At start of weaning

Percent change of diaphragmatic thickness 22.47±4.53 41.93±10.24 <0.001

Diaphragmatic excursion 1.38±0.19 2.19±0.17 <0.001

Diaphragmatic slope 0.73±0.10 1.41±0.15 <0.001

There were statistically significant higher sonographic measurements in the successful group (P<0.001) compared with the failed group.

Table 5 Relation of sonographic measurements to final outcome/weaning outcome just before extubation

Survived (N=141) Died (N=99) P Failed (N=123) Successful (N=117) P

Percent change of diaphragmatic thickness 38.57±11.91 22.32±4.61 <0.001 22.47±4.53 41.93±10.24

Diaphragmatic excursion 2.04±0.37 1.39±0.18 <0.001 1.38±0.19 2.19±0.17 <0.001

Diaphragmatic slope 1.29±0.30 0.73±0.11 <0.001 0.73±0.10 1.41±0.15 <0.001

Table 6 Interobserver and intraobserver variability

Third Interobserver(N=20)

Intraobserver(N=20)

P

Percent change ofdiaphragmatic thickness

39.52±10.73 39.54±10.76 0.996

Diaphragmatic excursion 2.26±0.19 2.26±0.19 1.000

Diaphragmatic slope 1.48±0.12 1.48±0.13 0.967

There were no statistically significant differences betweeninterobserver and intraobserver sonographic measurements.

Table 7 The receiver operating characteristic curve of percentchange of diaphragmatic thickness

Percent changeof diaphragmatic

thickness

Diaphragmaticexcursion

Diaphragmaticslope

Cutoffpoint

30.105% 1.84 cm 0.94 cm/s

Areaunder thecurve

1.0 0.999 0.995

Sensitivity(%)

100 100 100

Specificity(%)

97.4 96.4 94.6

P value <0.001 <0.001 <0.001


extubation leads to post-extubation respiratory failurethat results in reintubation and accordingly pooroutcome [12].

Many weaning parameters such as maximuminspiratory pressure [13], RSBI, minute ventilation,CROP index (dynamic compliance, respiratory rate,oxygenation, and maximum inspiratory pressure index)and tracheal airway occlusion pressure 0.1 s (P=0.1)had been used. Despite the use of these parameters,there are still errors in weaning assessment [14].Diaphragm, the major muscle of inspiration, plays acritical role in the pathophysiology of respiratory failure[15]. Therefore, evaluation of the diaphragm functionis crucial in patients subjected to weaning from MV[16]. Ultrasonography has emerged to evaluatediaphragm function and predict weaning outcomefrom MV [17].

Weaning success could be categorized as being simpleor difficult. Simple weaning patient takes less than 1week and only one trial to be weaned, while difficultweaning takes more than 1 week and more than threetrials. On the other side, weaning failure was

considered as one of the following: nonscheduledextubation, need for reintubation, noninvasivepositive pressure ventilation of the patient within48 h after extubation, delayed extubation, andterminal extubation or tracheostomy.

Two hundred and forty (138 men and 102 women)invasively MV patients were chosen for our study, 117(48.8%) had successfully weaned from which 111patients were with simple weaning and six patientswere with difficult weaning, and 123 (51.2%) patientshad failed weaning from which 99 (41.2%) patientswere with terminal extubation, 12 patients withreventilation, seven patients with delayed extubation,and five patients were with tracheostomy.

Pirompanich andRomsaiyut [18] in their study assessedthe role of diaphragm-thickening fraction and RSBI inweaning from MV. The recorded 25 (73.5%) patients


were successfully weaned and nine (26.5%) patientsfailed weaning. This is because they conducted theirstudy on 34 patients only, butwe conducted our study on240 patients which are relatively larger in number.Besides, we considered died patients (99) as part ofthe weaning failure as terminal extubation.

Weaning outcome was nearly matching with theweaning outcome of Luo et al. [19] with adifference in the number of studied patients betweenour study (240) and their study (60). They conductedtheir study on 60 patients assessing the role ofdiaphragm in weaning outcome by ultrasound indifficult-to-wean patients. There were 27 (45%)patients who were successfully weaned and 33 (55%)patients who failed weaning. From the failed group, 14(23.3%) patients had died.

Also, weaning outcome was different from the resultsof Samanta et al. [20] and Yoo et al. [12] in weaningoutcome. Samanta et al. [20] conducted their study on64 patients. There were 59 (92.2%) patients withsuccessful weaning and five (7.8%) patients in failedweaning. Yoo et al. [12] conducted their study on 60patients from which 47 (78.3%) patients weresuccessfully weaned and 13 (21.7%) failed weaning.They assess patients only just before extubation anddefined weaning failure as patients reventilated orneed NIV within 48 h of extubation or needtracheostomy.

Weaning outcome in our study was affected byAPACHE II score, ICU stay duration, and durationof using sedation. APACHE II score was significantlyincreased in the failed group; 24.65 compared to thesuccessful group; 15.19 with, indicating more severityin the failed group (failed group and successful group).ICU stay significantly increased in the successfulgroup, 7.00 days compared with the failed group;5.77 days as the failed group included the deadpatients as a terminal extubation and died earlydecreasing the ICU stay duration of the failedgroup. Duration of using sedation was statisticallysignificantly higher in the failed group, 62.85 h(indicating prolonged mandatory ventilation)compared with the successful group, 30.37 h. Therewas no statistical significance of ventilation duration inweaning outcome.

The final outcome (survived or died) is affected as wellby the APACHE II score, ICU stay duration, andduration of using sedation. APACHE II scoresignificantly increased in the died group; 24.13compared with the survived group, 17.16, indicating

more severity of disease in the died group. ICU stayduration was significantly high in the survived group;6.73 days compared with the died group, 5.86 days.Duration of using sedation was statisticallysignificantly higher in the died group, 62.75,indicating prolonged mandatory ventilation withdecreased parameters values of successful weaningcompared with the survived group, 35.96. Therewas no statistically significant effect of weaningduration and ventilation duration on the finaloutcome.

There was statistically significant difference of percentchange of tdi between the first day of ventilation(40.08%) and just before extubation (42.52%) with(P<0.001). Diaphragmatic excursion on the thirdday of ventilation was 1.72 cm (±0.42) comparedwith the first day of ventilation (1.78 cm) with nostatistical significance. In comparison to just beforeextubation, diaphragmatic excursion on the third day ofventilation was 2.19 cm compared with just beforeextubation (2.21 cm) with no statistical significance.There was statistically significant difference ofdiaphragmatic excursion between the first day ofventilation (2.15 cm) and just before extubation(2.21 cm).

There was a decrease in diaphragmatic slope on thethird day of ventilation compared with the first day ofventilation and just before extubation with statisticalsignificance between the first day of ventilation and justbefore extubation, and the third day of ventilation andjust before extubation dissimilar between first and thirdday of ventilation that showed no statistical significantdifferences. This is matched with Goligher et al. [21] intheir study which was conducted on 107 MV patientswith 10 nonventilated ICU patients. They found thatover the first week of ventilation, diaphragm thicknessdecreased by more than 10% in 44% patients, wasunchanged in 44% patients, and increased by morethan 10% in 12% patients, and diaphragmaticcontractile activity decreased with increasingventilator driving pressure and controlled ventilatormodes, and thickness did not vary over timefollowing extubation or in nonventilated patients.

Along the course of ventilation, there was improvementin the sonographicmeasurements ofdiaphragmfromthefirst day of ventilation, in which the patients are sedatedand on mandatory ventilation (increased ventilatorsupport) to just before extubation in which thepatients are on spontaneous mode (no sedation, little,or no ventilator support). At the third day of ventilation,the sonographic measurements were decreased


comparedwith the first day of ventilation and just beforeextubation. This may be related to the effect of MV,sedation, and initial pathology of the disease.

The sequential sonographic diaphragmaticmeasurements were statistically significantly higherin the successful compared with the failed group. Aswell as sonographic measurements were significantlyincreased in the survived compared with the diedgroup. That may reflect the mandatory role ofdiaphragmatic muscle in weaning program and inendurance of respiratory system. This is consistentwith Yoo et al. [12] who conducted their study on60 MV patients. They showed that patients had agreater tdi% with the successful group; 42.1%compared with the failed group, 22.5% with P value0.03.

In this study, the tdi%, at cutoff point was 30.105%, thesensitivity and specificity to predict weaning successwere 100 and 97.4%, respectively, with P value less than0.001 and area under the curve of 1.0 (Fig. 2). This ismatched to Zambon et al. [22], Ferrari et al. [23], Baesset al. [24], Ali andMohamad [25], Farghaly andHasan[26], DiNino et al. [27] and Agamy et al. [28] in whichthe cutoff values of tdi% associated with weaningsuccess in their studies were 30–36, more than 36,30, 30, 30–36, 30, and more than 40%, respectively.Ferrari et al. [23] concluded in their study that thesensitivity was 82% and the specificity was 88% whileBaess et al. [24], recorded in their study 69% sensitivityand 71% specificity. DiNino et al. [27] concluded intheir study a sensitivity of 88% and a specificity of 71%.Agmy et al. [28] referred in their study on 78 COPD

Fig. 2

At a cutoff point of 30.105%, the sensitivity and specificity of percent changcutoff point of 1.84 cm, the sensitivity and specificity of diaphragmatic excuthe sensitivity and specificity of the diaphragmatic slope were 100 and 9

patients in their exacerbation. They measurediaphragmatic thickening in the pressure supportmode only with a sensitivity and specificity of 88and 92%, respectively.

The resultswere higher than that ofOsmanandHashim[29], Pirompanich and Romsaiyut [18] and Samantaet al. [20]. Osman and Hashim [29] concluded that thecutoff value of tdi% associated with weaning success intheir study was 28% with a sensitivity and specificity of88.9 and 100%, respectively. Pirompanich andRomsaiyut [18] conducted their study on 34 MVpatients. The cutoff value of tdi% that predictweaning success was 26% or more with a sensitivity of96% and specificity of 68%. Samanta et al. [20]conducted their study on 64 MV patients. The cutoffvalue of tdi% that predicts weaning success was 24.5%with a sensitivity of 97% and specificity of 81%.

Regarding diaphragmatic excursion, diaphragmaticexcursion of our studied patients in the successfulgroup was 2.19 cm compared with the failed group,(1.38) with P value less than 0.001. These values weremuch more than others detected with Yoo et al. [12]who showed that diaphragmatic excursion in thesuccessful group was 1.65 cm compared with thefailed group, 0.8 cm with P value less than 0.001. Ata cutoff point of 1.84 cm or more, the sensitivity andspecificity of diaphragmatic excursion that predictweaning success were 100 and 96.4%, respectively,with P<0.001 and area under the curve of 0.999.This is different with most of the studies as Saeedet al. [30] who conducted their study on 50 COPDpatients observed that the cutoff point of

e of diaphragmatic thickness were 100 and 97.4%, respectively. At arsion were 100 and 96.4%, respectively. At a cutoff point of 0.94 cm/s,4.6%, respectively.


diaphragmatic excursion associated with weaningsuccess was 1.1 cm with a sensitivity of 86.4% and aspecificity of 87.5%. Osman and Hashim [29] showedthat the cutoff value of diaphragmatic excursion that isassociated with weaning success was 1.0 cm with asensitivity and specificity of 83.3 and 100%,respectively. Hayat et al. [31] conducted their studyon 100 MV patients. The cutoff value of weaningsuccess was 1.2 cm with a sensitivity and specificityof 78.95 and 70.83%, respectively. Zambon et al. [22]concluded in their systematic review study that cutoffvalues ranged from 1.0 to 1.4 cm predict weaningsuccess. Baess et al. [24] showed that the cutoffpoint of diaphragmatic excursion that predictsweaning success was 1.0 cm or more with asensitivity and specificity of 69.57 and 41.29%,respectively. Ali and Mohamad [25] concluded thatthe cutoff value of diaphragmatic excursion associatedwith weaning success was 1.5 cm with a sensitivity andspecificity of 88.7 and 84.3%, respectively. Luo et al.[19] in their study which was conducted on 60 patients,the cutoff value of diaphragmatic excursion of weaningsuccess was 1.2 cm with a sensitivity of 80% andspecificity of 68.4%. Matamis et al. [9] in which thecutoff value of diaphragmatic excursion was 1.8 cm ormore which was associated with weaning success.Diaphragmatic slope in the successful group was1.41 cm/s compared with the failed group; 0.73 cm/swith P value less than 0.001. At a cutoff point of0.94 cm/s or more that predicts the weaning success,the sensitivity and specificity of the diaphragmaticslope were 100 and 94.6%, respectively, with P valueless than 0.001 and at an area under the curve of 0.995.Matamis et al. [9] concluded in their study that theslope value which predicts weaning success was 1.3 cm/s (±0.4).

As regards complications, there were statisticallysignificant higher complications; pneumothorax(2.1%) and VAP (11.8%) in the weak group with Pvalue 0.002 compared with the moderate; and in goodfunction group there were no complications (0.0%).Wefaced some limitations along the course of the study.First, poor image quality as we lost the cooperation andorientation of some MV patients. Second, difficulty inapproaching the left hemi-diaphragm, especially inobese and those with splenectomy.

ConclusionThe measurement of tdi% as well as excursion andslope could be applied in correlation more withweaning outcome with a sensitivity of 100% andspecificity of 97.4%.



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Usefulness of different prognostic scores for AECOPD:APACHE II, BAP65, 2008, and CAPS scoresRania A. Sweeda, Mostafa Abd El Mageed Shaheena, Esraa A. El Gendyb

Purpose The purpose of this study was to compare fourdifferent scores [Acute Physiology and Chronic HealthEvaluation (APACHE II); elevated blood urea nitrogen,altered mental status, pulse >109/min, age >65 years(BAP65); chronic obstructive pulmonary disease (COPD) andAsthma Physiology Score (CAPS); and 2008 score) to testtheir predictive properties for the need of mechanicalventilation (MV) and short-termmortality in patients with acuteexacerbation COPD (AECOPD).

Patients and methods This study enrolled 100 consecutivepatients with acute exacerbation COPD, over a 6-monthduration, admitted to the Emergency Department inAlexandria Main University Hospitals. The four scores werecalculated for each patient, and clinical data and outcome(need for MV and mortality during hospitalization or within aweek after discharge) were recorded.

Results Their mean age was 61.1±10.7 years, and 88%weremales. Duration of hospital stay was less than or equal to 20days in 67%. Mortality rate was 4%. Overall, 40% requiredMV. Blood urea nitrogen, pulse, CO2, pH, alteredconsciousness, and white blood cell were significantpredictors of mortality in univariate but not multivariateanalysis. Previous MV, cyanosis, and paradoxical abdominalmovement were significant predictors of need for MV. Thehighest area under the receiver operating characteristic curvewas that of APACHE II score regarding either mortality


prediction [area under the curve (AUC), 0.982; P=0.001] orneed for MV (AUC, 0.959; P<0.001), followed by BAP65score for mortality prediction (AUC, 0.967; P=0.002) and2008 score for predicting the need for MV (AUC, 0.851;P<0.001).

Conclusion All studied scores correlated significantly withmortality, but only APACHE II and 2008 score correlatedsignificantly with the need for MV. The highest area under thereceiver operating characteristic curve was that of APACHE IIscore regarding either mortality or need for MV prediction.Previous need for MVwas themost important predictor for theneed for MV. The routine use of these practical scores intriage of patients may direct early interventions to reducemortality rate.Egypt J Bronchol 2019 13:498–504© 2019 Egyptian Journal of Bronchology


Keywords: mechanical ventilation, morbidity, mortality, predictors

Departments of, aChest Diseases, bEmergency Medicine, Alexandria Main

University Hospital, Alexandria, Egypt

Correspondence to Rania A. Sweed, MD, 26 Mostafa Kamel Officer

Buildings, Alexandria, Zip code 00000, Egypt. Tel: +20 155 018 9963;


Received: 2 March 2019 Accepted: 9 July 2019






IntroductionChronic obstructive pulmonary disease (COPD) isnow reported to be the fourth leading cause of deathworldwide and expectedly will be the third by 2020[1–3]. Exacerbations occur in moderate-severe forms,rather than mild COPD [4] The American ThoracicSociety and European Respiratory Society defineCOPD exacerbation [acute exacerbation COPD(AECOPD)] as an acute change in patient’sdyspnea, cough, or sputum that is beyond normalvariability and that is sufficient to warrant a changein therapy [5]. Scores for mortality prediction inAECOPD could aid in decisions regarding the levelof care and allow early hospital discharge, thus reducingmorbidity and mortality. The BODE index, which canpredict mortality in patients with stable COPD, is notsuitable in the setting of acute exacerbation [6]. Othertools for mortality prediction are CURB-65(confusion, urea, respiratory rate, blood pressure, and65 years of age or older), BAP65 [elevated blood ureanitrogen (BUN), altered mental status, pulse >109/min, and age >65 years], and DECAF score (dyspnea,eosinopenia, consolidation, acidaemia, and atrialfibrillation) [7–9].

The aim of this study was to compare four differentscores [Acute Physiology and Chronic HealthEvaluation (APACHE II), BAP65, CAPS (COPDand Asthma Physiology Score), and 2008 scores] andtest their predictive properties for need of mechanicalventilation (MV) and short-term mortality duringhospital admission or within a week after dischargeto home in a population of patients hospitalized withAECOPD.

PatientsThis study was conducted on 100 consecutive adultpatients with AECOPD, over a 6-month duration,admitted to the Emergency Department, AlexandriaMain University Hospitals. We included patientsolder than 40 years, both sexes, smoking history ofmore than 10 cigarette pack years, primarily diagnosedas having COPD (supported by spirometric evidence



Table 1 2008 score

2008 score Points

Age (years)

<70 0

≥70 1

MRC (baseline, steady state)

0–1 0

2–3 1

4–5 2

Number of signs of severity at entry

0 0

1–2 2

3 and more 3

Signs of severity: cyanosis, use of accessory inspiratory muscles,paradoxical abdominal movement, asterixis, neurologicalimpairment, and lower limb edema. MRC, Medical ResearchCouncil.

Prognostic scores predicting outcomes of AECOPD Sweed et al. 499

of airflow obstruction during stable state if present)presenting with acute exacerbation of COPD (anevent in the natural course of the diseasecharacterized by a change in the patient’s baselinedyspnea, cough, and/or sputum that is beyond normalday-to-day variations and may have warranted achange in regular medication in a patient withunderlying COPD) [3] requiring hospitaladmission. We excluded patients experiencing life-threatening comorbidities such as any malignancies,liver failure, or end-stage renal disease; patients withother lung diseases; patients who did not wish toparticipate; patients with previous inclusion in thestudy; patients with exacerbation owing topneumothorax; and patients with primary reason foradmission other than AECOPD. Informed consentshad been taken from all participants before theirinclusion in the study. Approval of Research Ethicscommittees of the Faculty of Medicine, AlexandriaUniversity, had been obtained before the study.

MethodsThis prospective cohort study enrolled 100 patientswith COPD exacerbation who attended EmergencyDepartment of Alexandria Main University Hospitaland required admission. All patients were subjected tothorough history taking. Assessment of stable-statedyspnea was based on the extended MedicalResearch Council Dyspnea Score [10]. Cause ofAECOPD, previous need for MV (noninvasive orinvasive MV), and long-term home oxygen therapywere reported.

Clinical examination included primary survey ofairway, breathing, and circulation. Secondary surveyconsisted of full clinical examination including numberof signs of severity (cyanosis, use of accessoryinspiratory muscles, paradoxical abdominalmovement, asterixis, neurological impairment, andlower limb edema) [11]. ECG, Glasgow coma scale[12] assessment, random blood sugar measurement,complete blood picture, arterial blood gases (ABG),serum urea, BUN and creatinine, and serum sodium,potassium, and albumin were examined. Chestradiograph was done and computed tomography ifneeded.

The following scores were calculated on admission:APACHE II [13], BAP65 [7], CAPS [14], and 2008score [15] (Table 1). Patients’ prognosis was recorded.Outcome measures included the need for MV, lengthof hospital stay, and in-hospital mortality. Accordingly,we classified our patients into different groups, as either

nonsurvivors or survivors, and the latter group wasfurther classified according to their need for MV.The study was approved by our institutional researchethics committee. Informed consent was obtained fromall individual participants included in the study.

Statistical analysis of the data [16]Data were fed to the computer and analyzed using IBMSPSS software 20.0. (IBMCorp., Armonk, New York,USA) [17] Qualitative data were described usingnumber and percent. The Kolmogorov–Smirnov testwas used to verify the normality of distribution.Quantitative data were described using range(minimum and maximum), mean, SD, and median.Significance of the obtained results was judged at the5% level. χ2 test was used for categorical variables, tocompare between different groups. Fisher exact orMonte Carlo correction was used for correction forχ2 when more than 20% of the cells have expectedcount less than 5. Student t test was used for normallydistributed quantitative variables, to compare betweentwo studied groups. Mann–Whitney test was used forabnormally distributed quantitative variables, tocompare between two studied groups. Receiveroperating characteristic (ROC) curve was plotted,where area under the receiver operating characteristiccurve (AUROC) denotes the diagnostic performanceof the test. Regression was used to detect the mostindependent/affecting factor for MV.

ResultsOur study enrolled 100 patients. Demographic andclinical data are shown in Table 2. Mean arterial pH,PaCO2, PaO2, HCO3, and O2 saturation were 7.34±0.07, 63.71±18.21 mmHg, 52.67±9.04 mmHg, 32.22±6.18 mEq/l, and 80.7±10.11%, respectively. Acidotic

Table 2 Comparison between the different groups according to clinical data

Survived(N=96) [n

(%)]

Nonsurvived(N=4) [n(%)]

Test ofsignificance

P Survived Test ofsignificance

P

Mechanicallyventilated

(N=36) [n (%)]

Nonmechanicallyventilated (N=60)

[n (%)]

Sex

Male 85 (88.5) 3 (75.0) χ2=0.667 FEP=0.405 30 (83.3) 55 (91.7) χ2=1.540 FEP=0.321

Female 11 (11.5) 1 (25.0) 6 (16.7) 5 (8.3)

Age (years) 60.94±10.83

65.0±3.56 t=0.745 0.458 58.97±9.49 62.12±11.48 t=1.384 0.170

Comorbidities

DM 22 (22.9) 1 (25.0) χ2=0.009 FEP=1.000 6 (16.7) 16 (26.7) χ2=1.274 0.259

Hypertension 15 (15.6) 1 (25.0) χ2=0.251 FEP=0.508 5 (13.9) 10 (16.7) χ2=0.132 0.717

OSA 4 (4.2) 0 (0.0) χ2=0.174 FEP=1.000 3 (8.3) 1 (1.7) χ2=2.504 FEP=0.147

IHD 8 (8.3) 0 (0.0) χ2=0.362 FEP=1.000 2 (5.6) 6 (10.0) χ2=0.582 FEP=0.706

AF 6 (6.3) 2 (50.0) χ2=9.986* FEP=0.031* 3 (8.3) 3 (5.0) χ2=0.427 FEP=0.669

Previous needfor MV

28 (29.2) 3 (75.0) χ2=3.771 FEP=0.087 25 (69.4) 3 (5.0) χ2=45.230* <0.001*

Respiratoryrate cycle/min

32.34±6.16

34.25±19.12 t=1.999 0.855 33.6±8.89 31.58±3.55 t=1.307 0.198

Heart ratebeat/min

89.09±14.59

126.25±17.97

t=4.953* <0.001* 90.58±16.80 88.20±13.15 t=0.773 0.441

MAP (mmHg) 95.06±14.94

81.68±23.50 t=1.717 0.089 92.46±17.30 96.62±13.23 t=1.327 01.188

Temperature(°C)

37.46±0.66

38.23±1.52 t=1.003 0.389 37.49±0.75 37.45±0.60 t=0.303 0.762

GSC 14.58±1.04

11.5±1.91 U=7.0* <0.001* 14.58±1.68 14.98±0.13 U=917.50* 0.006*

Asterixis 6 (6.3) 1 (25.0) χ2=2.074 FEP=0.255 5 (13.9) 1 (1.7) χ2=5.736 FEP=0.027*

Neurologicalimpairment

8 (8.3) 4 (100.0) χ2=30.556* FEP<0.001* 7 (19.4) 1 (1.7) χ2=9.309 FEP=0.004*

LL edema 43 (44.8) 3 (75.0) χ2=1.411 FEP=0.331 24 (66.7) 19 (31.7) χ2=11.146* 0.001*

Cyanosis 17 (17.7) 4 (100.0) χ2=15.675* FEP=0.002* 14 (38.9) 3 (5.0) χ2=17.732* <0.001*

Accessoryinspiratorymuscle use

45 (46.9) 4 (100.0) χ2=4.337 FEP=0.054 21 (58.3) 24 (40.0) χ2=3.037 0.081

Paradoxicalabdominalmovement

33 (34.4) 4 (100.0) χ2=7.095* FEP=0.017* 24 (66.7) 9 (15.0) χ2=26.625* <0.001*

AF, atrial fibrillation; DM, diabetes mellitus; IHD, ischemic heart disease; LL, lower limb; MAP, mean arterial blood pressure; MV,mechanical ventilation; OSA, obstructive sleep apnea. χ2: χ2 test for comparing between the different groups. FEP: P value for Fisher’sexact for χ2 test for comparing between different groups. t, P: t and P values for Student t test for comparing between the different groups.U, P: U and P values for Mann–Whitney test for comparing between the two groups. *Statistically significant at P less than or equal to0.05.


exacerbations (pH<7.35) were present in 48 (48%)patients.

For all studied patients, the mean values of the fourstudied scores on admission were as follows: APACHEII score 14.43±6.40, CAPS 31.62±7.19, BAP65 score0.89±0.96, and 2008 score 3.11±1.37. Duration ofhospital stay ranged from 3 to 48 days, with a meanof 16.92±9.66 days. A total of 67 (67%) patients stayedat hospital for less than or equal to 20 days or less.Moreover, 40 (40%) patients requiredMV (15 requirednoninvasive and 25 required invasive MV). Overallmortality rate was 4% (all were on invasive MV).For study analysis and comparison, patients were

classified according to clinical outcome intononsurvivors and survivors, with the latter beingfurther subdivided according to their need for MV.

Table 2 shows the comparison between differentgroups regarding demographic and clinical data. Asfor laboratory investigations, comparing survivors withnonsurvivors, ABG analysis showed that both pH andpartial pressure of carbon dioxide in arterial blood(PaCO2) were the only statistically significantlydifferent parameters (P=0.050 and 0.003,respectively). BUN was significantly higher amongnonsurvivor group as well (P=0.043). Comparingpatients who required MV and those who did not

Table 3 Agreement (sensitivity and specificity) for Acute Physiology and Chronic Health Evaluation II, CAPS, BAP65, and 2008scores to predict mortality

AUC P 95% CI Cut off Sensitivity Specificity PPV NPV

APACHE II 0.982* 0.001* 0.955–1.008 >22 100.0 95.83 50.0 100.0

BAP65 score 0.967* 0.002* 0.907–1.028 >1 100.0 78.12 16.0 100.0

2008 score 0.948* 0.002* 0.900–0.996 >4 100.0 89.58 28.6 100.0

CAPS 0.896* 0.008* 0.758–1.033 >43 75.0 94.79 37.5 98.9

APACHE, Acute Physiology and Chronic Health Evaluation; AUC, area under the curve; BAP65, elevated blood urea nitrogen, alteredmental status, pulse more than 109/min, age more than 65 years; CAPS, chronic obstructive pulmonary disease (COPD) and AsthmaPhysiology Score; CI, confidence intervals; NPV, negative predictive value; PPV, positive predictive value. *Statistically significant at Pless than or equal to 0.05.

Figure 1

ROC curve for APACHE II, CAPS, BAP65, and 2008 scores to predictmortality. APACHE, Acute Physiology and Chronic Health Evalua-tion; BAP65, elevated blood urea nitrogen, altered mental status,pulse more than 109/min, age more than 65 years; CAPS, chronicobstructive pulmonary disease (COPD) and Asthma PhysiologyScore; ROC, receiver operating characteristic.


require, all ABG components showed significantdifference (P<0.001). There was no significantdifference regarding BUN, but creatinine levelsshowed significant difference (P=0.002). Serumalbumin as well was significantly low among patientswho required MV (P=0.002).

Comparison between the different studied groupsregarding the four scores revealed that all studiedscores were significantly higher among nonsurvivorsthan survivors: APACHE II score (28.50±4.04 vs.13.84±5.79, respectively; P<0.00), CAPS score(43.25±7.27 vs. 31.14±6.80, respectively; P<0.00),BAP65 score (3.25±0.96 vs. 0.79±0.83, respectively;P<0.00), and finally, 2008 score (5.0±0.0 vs. 3.03±1.34, respectively; P=0.002). Comparing patientswho required MV with patients who did not, onlyAPACHE II and 2008 score were significantlydifferent (19.36±4.28 vs. 10.53±3.66, respectivelyfor APACHE II and 4.0±0.72 vs. 2.45±1.29,respectively, for 2008 score), with P value less than0.001 for each.

Regarding prediction of mortality, BUN, urea, pulse,CO2, pH, altered level of consciousness, and whiteblood cell (WBC) were all significant in univariate butnot in multivariate analysis. Performance of differentscores regarding mortality prediction is shown inTable 3 and Fig. 1. APACHE II had the highestarea under the curve and highest specificity followed byBAP65, and all showed significant correlations withmortality.

As for predicting the need forMV, multivariate logisticregression showed significant correlations withprevious need for MV, cyanosis, paradoxicalabdominal movement, PaCO2, HCO3, O2

saturation%, and elevated creatinine levels (P=0.001,0.018, 0.007, 0.004, 0.013, 0.006, and 0.045,respectively). The most powerful predictor washistory of previous MV. Performance of differentscores regarding predicting the need for MV isshown in Table 4 and Fig. 2. APACHE II had the

highest area under the curve followed by 2008 score.Only APACHE II and 2008 score showed significantcorrelations.

DiscussionPredictors of mortality in stable disease do not reflectmortality during AECOPD [18]. Few clinical scoreshave been tested to assess the prognosis of AECOPDto help clinicians take proper decisions regardingadmission and level of care [7,9,19]. In this study,we compared different studied groups regardingindividual parameters included in the four scoresfollowed by comparing the performance of the fourdifferent scores regarding their ability not only topredict mortality but the need for MV as well. Themajority of our patients were males owing to the factthat smoking is more prevalent among males in our

Table 4 Agreement (sensitivity and specificity) for Acute Physiology and Chronic Health Evaluation II, CAPS, BAP65, and 2008scores to predict mechanical ventilation patients

AUC P 95% CI Cut off Sensitivity Specificity PPV NPV

APACHE II 0.959* <0.001* 0.926–0.992 >14 92.50 83.33 78.7 94.3

2008 score 0.851* <0.001* 0.779–0.923 >3 77.50 76.67 68.9 83.6

CAPS 0.580 0.177 0.457–0.703 >30 55.0 50.0 42.3 62.5

BAP65 score 0.525 0.675 0.407–0.643 >1 27.50 76.67 44.0 61.3

APACHE, Acute Physiology and Chronic Health Evaluation; AUC, area under the curve; BAP65, elevated blood urea nitrogen, alteredmental status, pulse more than 109/min, age more than 65 years; CAPS, chronic obstructive pulmonary disease (COPD) and AsthmaPhysiology Score; CI, confidence intervals; NPV, negative predictive value; PPV, positive predictive value. *Statistically significant at Pless than or equal to 0.05.

Figure 2

ROC curve for APACHE II, CAPS, BAP65, and 2008 scores to predictthe need for mechanical ventilation. APACHE, Acute Physiology andChronic Health Evaluation; BAP65, elevated blood urea nitrogen,altered mental status, pulse more than 109/min, age more than 65years; CAPS, chronic obstructive pulmonary disease (COPD) andAsthma Physiology Score; ROC, receiver operating characteristic.


society. Cardiovascular comorbidities were commonin our patients, and half the nonsurvivors experiencedatrial fibrillation (AF) versus 6% of survivors, withP=0.031. An Australian study revealed thatcardiovascular events contributed to 26% of long-term mortality following the first episode ofAECOPD [20]. Others reported that in patientsadmitted to the hospital for AECOPD, cardiactroponin elevation was an independent predictor ofall-cause mortality [21]. Similarly, another studyreported that 33.3% of deceased patients had AF,whereas none in the discharged group had AF[22,23]. The common association of AF andCOPD may be attributed to blood gasesderangements in COPD and can be linked toincreased mortality. Thus, it is important to screenpatients with AECOPD for concomitant acutecardiac events.

Among all ABG parameters, only academicexacerbations and PaCO2 were significantly higherin nonsurvivors than survivors, with P=0.05 and0.003, respectively. Another study matched ourresults, where PaCO2 was high and pH wassignificantly low in the nonsurviving group, whereasother parameters were statistically insignificant,suggesting a poor survival if exacerbation isaccompanied by type 2 respiratory failure at the timeof admission [24]. WBC, BUN, and urea were alsosignificantly higher in nonsurvivors (P=0.023, 0.043,and 0.042, respectively). High BUN might beattributed to impaired circulation or acute kidneyinjury leading to poor outcomes. Comparing groupsthat requiredMV or not, serum creatinine and albumindiffered significantly (P=0.002 for both). Low albuminlevels may represent underlying poor nutrition beforeadmission, eventually leading to poor hospitaloutcomes [25,26].

Patients who required intubation had a mortality rateof 16% (four of 25), which was significantly higher thanpatients who did not require intubation, as none ofthem died, P=0.003. This either reflects the severity ofthe underlying COPD or the sequence of ventilator-associated complications. Others reported mortalityrate of 21.6% among intubated patients, which wassignificantly higher than nonintubated patients(mortality rate 4.2%) [27,28]. These results highlightthe value of noninvasive ventilation; nevertheless, it isimportant to ensure close monitoring becausenoninvasive ventilation failure has been linked tohigh mortality [17].

Overall mortality was 4% in our study. Shorr et al. [7]reported similar mortality rate (4%). The 2008 UKNational COPD Audit showed mortality rate of 7.7%[29]. Steer et al. [9] reported higher mortality rates of10.4%, which may be because they enrolled patientsolder than our study population. The mortality rateranged from 4 to 14% in the Asia-Pacific regionbased on different cohorts and was as high as 25%for patients requiring ICU admission [30–33]. On


the contrary, others reported lower mortality rates,where the in-hospital mortality rate was 2% [34].This difference may reflect the variation in thresholdfor hospital admission among different countries andvariation in mean age of population enrolled; inaddition, some studies enrolled only patientsrequiring ICU admission. Finally, this variationmay be attributed to difference in severity ofCOPD and quality of care. Roche et al. [15]found that age is an independent risk factor for in-hospital mortality. Similarly, a Japanese study found ahigher mortality in patients with older age [30].Yousif and El Wahsh [35] found that thenonsurvivors had a statistically significant higherage, with P=0.012. In the present study, we foundno correlation between age and mortality; moreover,on comparing between survivors and nonsurvivors,there was no significant difference regarding age,with P=0.458. We attribute our findings to therelatively small sample size.

Limsuwat et al. [27] using multivariate analysisdemonstrated that the mortality rate wassignificantly associated with a low mean arterialblood pressure [odds ratio (OR) 0.91, 95%confidence interval (CI)]s, an intubation event (OR6.12, 95% CI), and an elevated BUN (OR 1.06, 95%CI) (P<0.05 each). In the current study, BUN, urea,pulse, CO2, pH, altered level of consciousness, andWBC were all significant in univariate but notmultivariate analysis.

On the contrary, regarding prediction of need for MV,multivariate analysis revealed that history of previousneed for MV was a powerful predictor (OR 43.59, 95%CI, as well as cyanosis OR 19.23, 95% CI) andparadoxical abdominal movement (OR 20.02, 95%CI). We excluded the four studied scores frommultiregression analysis to eliminate the possibilitythat a composite score would obscure thecontribution of individual items.

We tried to compare the four studied scores. TheBAP65 score was developed by Shorr et al. [7] whostated that it had AUROC of 0.77 and correlated wellwith both in-hospital mortality and the need for MV.It also showed consistent results between twocompletely different populations (American andLebanese) [36]. Another study showed that bothDECAF and BAP65 scores were good predictors ofboth mortality and the need for ventilation [22]. Wetotally agree with and confirm the previous findingsregarding mortality prediction. In the current study,BAP65 score correlated well with mortality, with a

sensitivity of 100% and specificity of 78%, withAUROC of 0.967. This could be explained by thefollowing: elevated BUN, tachycardia, and alteredconsciousness were significantly more commonamong nonsurvivors, and elevated BUN andtachycardia reflect intravascular volume depletion[34,37]. Altered mental status is also a better ofindicator of hypercapnia than the PaCO2, which canbe chronically elevated without manifesting clinicaldisturbance [34]. Nevertheless, we disagree regardingits sensitivity for prediction of need for MV, as in thecurrent study, BAP65 score did not show significantcorrelation with the need for MV where sensitivity was27% and specificity was 76%, AUROC=0.525. Yousifand El Wahsh [35] compared among DECAF,modified DECAF, 2008, and BAP65 scores topredict mortality and found that the highestAUROC curve was that of the BAP65 score(0.861). Our study revealed higher AUROC curvefor BAP65 but agreed regarding the fact thatBAP65 was superior to 2008 score in mortalityprediction. In agreement with Steer et al. [9], wefound that APACHE II score was also superior toBAP65 and CAPS score regarding mortalityprediction, with AUROC=0.982.

This is not a retrospective study. It did not rely onalready available medical records. It focused on readilyavailable parameters on admission. Patients youngerthan 40 years old were excluded to avoid bronchialasthma misdiagnosis. As for limitations, we relied onprevious diagnosis of COPD. The relation betweenFEV1 and AECOPD outcomes was not determined.Finally, frequency of last year exacerbations, details ofdrug treatment, and sputum bacteriology were alsomissing.

ConclusionIn conclusion, all studied scores correlated significantlywith mortality, but only APACHE II and 2008 scorescorrelated significantly with the need for MV. Previousneed for MV, presence of cyanosis, and paradoxicalabdominal movement can predict the need for MV.APACHE II score proved to be the most powerfulscore, as it not only predicted mortality but also theneed for MV. BAP65 score follows for mortalityprediction and 2008 follows for predicting the needfor MV. BAP65 score has the advantages of beingsimple and objective. The four scores are practical andnot time consuming. We recommend larger studies toconfirm our results and the routine use of these scoresin triage of patients; nevertheless, individualizationmust be done based on clinical judgment.


AcknowledgementsThe authors acknowledge Professor MahmoudIbrahim Mahmoud for providing the idea of thiswork and his continuous guidance.


Conflicts of interestThere are no conflcits of interest.

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https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death

https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death


Post ICU syndrome among survivors from respiratory criticalillness. A prospective studyAhmad Abbas, Niveen E. Zayed, Samah M. Lutfy

Context Post-ICU syndrome (PICS) is a common impairmentthat develops after critical illness and persists after discharge.It is considered when a new or worsening impairment inphysical, cognitive, or mental status develops amongsurvivors from critical illness.

Aim To assess the prevalence of PICS and to define theprofile of patients at risk of each domain.

Patients and methods A total of 420 critically ill patientswere assessed at the time of ICU discharge for presence ofone or more domains of PICS: cognitive dysfunction,psychiatric impairment, and physical disability.

Results A total of 220 (52.4%) patients without preexistingimpairment developed one or more PICS forms. Half of theparticipants developed cognitive impairment, 14.29%developed depression, 26.19 developed anxiety, and 35.71%experienced both muscle weakness and impaired balance.PICS presented in three different patterns: pattern A, with onedomain, in which 2.38% presented with either cognitive orpsychiatric affection; pattern B, with two (19.05%) domains,where 80 patients had cognitive dysfunction, combined withphysical affection in 30 patients and psychiatric impairment in50 patients; and pattern C, with all PICS domains (28.57%).


Multivariate analysis was used to detect independentpredictors associated with each domain of PICS.

ConclusionSurvivors from critical illness should be screenedfor different domains of PICS. Cognitive impairment wasevident in those with prolonged duration of mechanicalventilation (MV), delirium, stroke, and hypotension.Psychiatric impairment was evident in females with prolongedsedation and duration of MV, delirium, and hypoglycemia.Physical impairments were evident in those with sepsis,undernutrition, and prolonged duration of MV.Egypt J Bronchol 2019 13:505–509© 2019 Egyptian Journal of Bronchology


Keywords: anxiety, cognitive dysfunction, depression, muscle weakness,post-intensive care unit syndrome

Department of Chest, Zagazig University, Zagazig, Egypt

Correspondence to Ahmad Abbas, MD, Department of Chest, Zagazig

University, Zagazig 44519, Egypt. Tel: +20 115 535 5369;


Received 25 April 2019 Accepted 16 July 2019






IntroductionPost-ICU syndrome (PICS) is a common impairmentthat develops after critical illness and persists afterdischarge from ICU. It is considered when a new orworsening impairment in physical, cognitive, or mentalhealth status develops among survivors from criticalillness [1].

Long-term cognitive impairment is reported to occurin 25% of ICU survivors, a percent that reaches 75% ina few studies [2]. Risk factors include prior cognitiveimpairment, prolonged mechanical ventilation (MV),glucose dysregulation, duration of delirium, stroke,alcoholism, hypoxia, hypotension, severe sepsis, andacute respiratory distress syndrome (ARDS) [3].

Moreover, psychological disability (anxiety,depression, and posttraumatic stress disorder) isobserved up to 62% among survivors after dischargefrom ICU with the same risk factors for cognitivedysfunction in addition to the use of sedation andanalgesia in ICU, female sex, lower level ofeducation, and preexisting psychiatric impairment[4–6].

Critical illness neuromuscular weakness is consideredthe most common physical impairment that occurs in

more than quarter of ICU population. Risks includemultisystem organ failure, sepsis, prolonged sedationuse, and MV (>7 days) [7,8].

This study aimed at assessment of the prevalence ofPICS among survivors from respiratory critical illnessand to define the profile of patients at risk of eachdomain.

Patients and methodsThis study was conducted at respiratory ICU (RICU),Zagazig University Hospitals, from January 2017 toJanuary 2019. It included 420 critically ill patientsassessed at time of ICU discharge.

Inclusion criteriaAll critically ill patients admitted to RICU for morethan 24 h owing to various diagnoses with no history ofneurocognitive impairment during the period of thestudy and who agreed to participate were included.




Exclusion criteriaParticipants with any of the followings were excluded:malignancy, neuromuscular pathology, pregnancy, andpatients discharged on long-term ventilatory support ortracheostomy.

All participants were subjected to the followings:

(1)
Good history taking and event documentationduring RICU stay with stress on previous ICUadmission or MV, respiratory failure, recentventilatory support and its duration, sepsis, useof sedation, cardiac arrest, ARDS, stroke,delirium, undernutrition, hypoglycemia,hypotension, and hypoxemia.
(2)
Assessment of cognitive impairment usingMontreal Cognitive Assessment scale, Arabicversion. It is a 30-point test in which a scoreless than 26 points represents a mild cognitiveaffection [9].
(3)

Table 1 Prevalence of different domains of post-intensivecare unit syndrome

Post-ICU syndrome domain Total (N=420)

Cognitive dysfunction 210 50

Depression 60 14.29

Anxiety 110 26.19

Assessment of psychosocial problems:(a) The Generalized Anxiety Disorder-7 scale,

Arabic version: scores of 5, 10, and 15 arethe cut-off points for mild, moderate, andsevere anxiety, respectively [10].

(b) Beck Depression Inventory-second editionscale, the Arabic translation: it assesses thepresence and severity of depression. Each itemis related to how the patient had felt in theprevious two weeks. There is a four-point scalefor each item ranging from 0 to 3. Total scoreof 14–19 represents mild form of depression,20–28 represents moderate form, and 29–63represents severe form [11].

Muscle weakness 150 35.71
Assessment of muscle power strength using (4)
Table 2 Patterns of post-intensive care unit syndrome

Post-ICU syndrome patterns Total (N=420)

Free 200 47.62

A pattern 20 4.76

Balance affection 150 35.71

Categorical data represented as n (%).

Medical Research Council sum score: the sumof the manual muscle test using the Oxford 0–5scales for the following muscle groups bilaterally −shoulder abduction, elbow flexion, wrist extension,hip flexion, knee extension, and dorsiflexion. Ascore of less than 48/ is consistent with ICU-acquired weakness [12].

Cognitive dysfunction 10 2.38
(5) Physical disorder 10 2.38
B pattern 80 19.05

Balance evaluation using Berg Balance Scale. Scoreof less than 45/58 indicates an increased risk forfalls [13].


Physical disorder 30 7.14

Psychic disorder 50 11.90

C pattern 120 28.57


Physical disorder 120 28.57

Ethical statementThe study was approved by Ethics Committee ofFaculty of Medicine, Zagazig University. A writteninformed consent was obtained from all participants.

Psychic disorder 120 28.57

Categorical data represented as n (%). A pattern, patients withone form of post-ICU disorders; B pattern, patients with two formsof post-ICU disorders; C pattern, patients with all forms of post-ICU disorders.

Statistical analysisAll statistics were performed using Minitab 17.1.0.0for Windows (2013; Minitab Inc., State College, PA,

USA). Continuous variables were presented as mean(SD) and nonparametric data as number (%).Multivariate logistic regression analysis with stepwiseelimination was used to determinate the independentpredictors of PICS domains. P value above 0.05 wasconsidered significant.

ResultsThis study enrolled 695 patients; of them, 420 patientscompleted the study, as 75 patients were excludedaccording to the exclusion criteria, 80 patients diedbefore discharge, and 95 patients refused to participate.A total of 420 survivors from critical illness wereevaluated at the time of ICU discharge. Table 1demonstrated the prevalence of different domains ofPICS. Overall, 220 (52.4%) patients withoutpreexisting impairment developed one or more PICSforms. Half of the participants developed cognitiveimpairment, 14.29% developed depression, 26.19%developed anxiety, and finally, 35.71% experiencedboth muscle weakness and impaired balance.

Different patterns of PICS were shown in Table 2.PICS presented in three different patterns: pattern A,with one domain, in which 2.3% presented with eithercognitive or psychiatric affection; pattern B, with two

Table 4 Multiple regression analysis of variables associatedwith post-intensive care unit cognitive dysfunction

Factors OR 95% CI P

Age 0.99 (0.9656, 1.0167) 0.48

Duration of MV use 1.19 (1.1019, 1.2887) <0.001

Stroke 2.52 (0.9504, 6.6978) 0.038

Delirium 3.21 (1.7657, 5.8448) <0.001

Hypoglycemia 3.55 (1.4006, 8.9820) 0.07

Hypotension (septic shock) 3.44 (1.5205, 7.7645) 0.002

Pearson; χ2=404.6, P=0.5, the model was adjusted for allvariables using stepwise elimination. CI, confidence interval; MV,mechanical ventilation; OR, odds ratio. P value consideredsignificant if less than 0.05.

Table 5 Multiple regression analysis of variables associatedwith post-intensive care unit psychiatric disorder

PICS among survivors from critical illness Abbas et al. 507

(19.05%) domains, where 80 patients had cognitivedysfunction, combined with physical affection in 30patients and psychiatric impairment in 50 patients; andpattern C, with all PICS domains (28.57%).

Demographic and risk factors of patients with PICS areshown in Table 3. In this study, patients at risk of PICShad mean age of 51.23±10.07 years, 65% were males,previously ICU admitted were 31.82%, and previouslymechanically ventilated were 9.09%. Recent institutionof ventilatory support owing to respiratory failure wasobserved in 95.45%, with mean duration of MV of 9.82±4.21 days. Sepsis was found in 13.64%, prolongedsedation in 36.36%, cardiac arrest in 4.55%, ARDS in13.64%, stroke in 18.18%, delirium in 45.45%, poornutrition in45.45%,hypoglycemia in18.18%,and finallyhypotension in 22.73%.

Multiple regression analysis of variables associated witheach domain of PICS is shown in Tables 4–6. Multipleregression analysis of variables associatedwith post-ICUcognitive dysfunction revealed four independentpredictors: duration of MV [odds ratio (OR)=1.19,confidence interval (CI)= 1.1–1.28, P<0.001), stroke(OR=2.52, CI=0.95–6.69, P=0.038), delirium(OR=3.21, CI=1.76–5.84, P<0.001), andhypotension (OR=3.44, CI=1.52–7.67, P=0.002).Multiple regression analysis of variables associatedwith post-ICU psychiatric disorder revealed fiveindependent predictors: duration of MV (OR=0.80,CI=0.669–0.946, P=0.005), prolonged use ofsedation (OR=11.50, CI=24.55–52.64, P<0.001),

Table 3 Demography and risk factors of patients with post-intensive care unit syndrome

Characteristics Total (N=220)

Age 51.23 10.07

Sex (male) 143 65

Previous ICU admission (yes) 70 31.82

Previous MV 20 9.09

Recent MV 210 95.45

Duration of MV use 9.82 4.21

Sepsis 30 13.64

Prolonged sedation 80 36.36

Respiratory failure 210 95.45

Cardiac arrest 10 4.55

ARDS 30 13.64

Stroke 40 18.18

Delirium 100 45.45

Undernutrition 100 45.45

Hypoglycemia 40 18.18

Hypotension 50 22.73

Hypoxemia 210 95.45

ARDS, acute respiratory distress syndrome; MV, mechanicalventilation. Continues data were represented as mean and SD,categorical data as n (%).

delirium (OR=4.92, CI=2.094–11.55, P<0.001),hypoglycemia (OR=9.96, CI=3.068–32.31, P=0.02),and female sex (OR=0.01, CI=0.005–0.0349,P=0.01). Multiple regression analysis of variablesassociated with post-ICU physical disorder revealedthree independent risk factors: duration of MV(OR=1.35, CI=1.2073–1.517, P<0.001),undernutrition (OR=0.88, CI=0.393–1.963,P=0.015), and sepsis (OR=6.28, CI=1.323–29.801,P=0.02).

DiscussionIn the current study, among 420 survivors enrolled andassessed at the time of discharge from ICU, 200(47.6%) patients were free from PICS, whereas 220

Factors OR 95% CI P

Duration of MV use 0.80 (0.6696, 0.9465) 0.005

Prolonged sedation 11.50 (24.5536, 52.6416) <0.001

Delirium 4.92 (2.0949, 11.5522) <0.001

Hypoglycemia 9.96 (3.0686, 32.3122) 0.02

Sex (female) 0.01 (0.0051, 0.0349) 0.01

Pearson; χ2=203.4, P=1, the model was adjusted for all variablesusing stepwise elimination. CI, confidence interval; MV,mechanical ventilation; OR, odds ratio. P value consideredsignificant if less than 0.05.

Table 6 Multiple regression analysis of variables associatedwith post-intensive care unit physical disorder

Factors OR 95% CI P

Age 0.97 (0.9367, 0.9946) 0.2

Previous ICU admission 2.22 (0.9760, 5.0288) 0.06

Previous MV support 3.44 (0.4460, 26.6083) 0.23

Duration of MV use 1.35 (1.2073, 1.5175) <0.001

Undernutrition 0.88 (0.3932, 1.9639) 0.015

Sepsis 6.28 (1.3238, 29.8016) 0.02

Deviance; χ2=258.7, P=1, the model was adjusted for all variablesusing stepwise elimination. CI, confidence interval; MV,mechanical ventilation; OR, odds ratio. P value consideredsignificant if less than 0.05.


(52.4%) patients without preexisting impairmentdeveloped one or more PICS forms.

Half of the participants developed post-ICU cognitiveimpairment, 14.29% developed depression, 26.19%developed anxiety, and finally 35.71% experiencedboth ICU muscle weakness and impaired balance.

In a study by Marra et al. [14], one or more PICSproblems were present in 64% and 56% after 3 monthsand 1 year after discharge, respectively. Of patientsassessed, 38% had cognitive dysfunction, 26% hadphysical weakness, and 33% had depressiveimpairment. At 12 months, 33% had cognitiveimpairment,21%haddisability, and31%haddepression.

One study reviewed the medical records of patientsreferred for psychiatric consultation and screening ofcognitive dysfunction or mood affection at dischargefrom the ICU during the 12-month period, and 43.3%screened positive for cognitive impairment and 60%experienced depressive symptoms [15].

Jones et al. [16] reported that 86.7% of patients oneweek after discharge from ICU showed evidence ofstrategic thinking and problem-solving impairment.

In Torgersen et al. [17], 64.3% of enrolled patients hada cognitive impairment at time of ICU discharge.

Global cognition and executive function were assessedin the study by Pandharipande et al. [18] 3 and 12months after discharge on 821 adults with respiratoryfailure or shock. Overall, 6% of their participants hadcognitive impairment at baseline. After 3 months, 40%of participants had cognitive score similar to patientswith moderate traumatic brain injury (1.5 SD), and26% had scores similar to patients with mildAlzheimer’s disease (2 SD).

In the current study, PICS presented in three differentpatterns: pattern A, which had one domain, in which2.3% presented with either cognitive or psychiatricaffection; pattern B, with two domains (19.05%),where 80 patients had all cognitive dysfunctioncombined with physical affection in 30 patients andpsychiatric impairment in 50 patients; and pattern C,which developed all PICS domains (28.57%).

Marra et al. [14] reported that co-occurrence of two ormore PICS domains was present in 25% of patients at 3months and 21% of them after 1 year of discharge. Alldomains of PICS were present in only 6 and 4% at 3and 12 months, respectively.

In this study, patients at risk of PICS had mean age of51.23±10.07 years, 65% were males, previously ICUadmitted patients represented 31.82%, and previouslymechanically ventilated patients represented 9.09%.Recent institution of ventilatory support owing torespiratory failure was observed in 95.45%, withmean duration of MV of 9.82±4.21 days. Sepsis wasfound in 13.64%, prolonged sedation in 36.36%,cardiac arrest in 4.55%, ARDS in 13.64%, stroke in18.18%, delirium in 45.45%, poor nutrition in 45.45%,hypoglycemia in 18.18%, and finally hypotension in22.73%.

Marra et al. [14] found that survivors who were freefrom PICS tended to be younger, more educated, lessfrail, and had fewer comorbidities than those withPICS. Severity of illness on admission was similarbetween those with or without PICS. Patients whowere mechanically ventilated, developed sepsis, coma,or delirium during ICU course were more likely to havePICS.

In comparison with studies that assessed PICS ingeneral ICUS, our results agreed that more than fiveof every 10 patients developed PICS and the majoritydeveloped cognitive impairment. On the contrary, ingeneral ICU, the majority developed one domain ofPICS and was suggested to be a sequela of severecritical illness rather than part of syndrome, whereasin the current work, more than one in every fourpatients developed PICS with all three domains.This difference can be attributable to the following:first, different patient characteristics in RICU, inwhich 95% of the studied population had respiratoryfailure and blood gases disturbances in addition toshare a high percent of other accusing factors ofPICS with general ICUS, and second, the differentenrolling criteria and PICS definition between studies.In this study, only new insults were taken intoconsideration, and patients were included on thebasis of not having a previous defect compared withother studies that included those with new or worsenedprevious dysfunction [1,5,7].

Multiple regression analysis of variables associated withpost-ICU cognitive dysfunction revealed fourindependent predictors: duration of MV, stroke,delirium, hypotension (Table 4). Chung et al. [15]in their study pointed contrary results, in whichpost-ICU cognitive impairment was not associatedwith the usual risk factors including age, preexistingcognitive impairment, and duration of delirium in theICU but was associated with the initial severity ofillness assessed by SOFA score.

PICS among survivors from critical illness Abbas et al. 509

Pandharipande et al. [18] concluded that persistentworsened cognition was independently associated withlonger duration of delirium, a finding that was notinfluenced by sedation or analgesia, age, preexistingcognition, and organ failures during ICU course.

Multiple regression analysis of variables associated withpost-ICU psychiatric disorder revealed fiveindependent predictors: duration of MV, prolongeduse of sedation, delirium, hypoglycemia, and female sex(Table 5).

Chung et al. [15] in their report found a significantassociation between female sex (88.9%) and post-critical illness depressive disability and trends ofassociation between post-ICU memories andduration of ventilatory support and ICU length of stay.

Davydow et al. [19] in their systematic review included1213 patients and reported a prevalence of post-criticalillness significant depression in 28%. Neither sex norage was an independent risk factor. Moreover, severityof illness on admission was not a risk factor. However,early post-ICU depressive symptoms were a strongpredictor for subsequent depression.

Multiple regression analysis of variables associated withpost-ICU physical disorder revealed three independentrisk factors: duration ofMV, undernutrition, and sepsis(Table 6).

Regarding study limitation, this study assessed short-term acute effects of surviving a critical care illness as allevaluation was done at the time of discharge, but long-term effects that might persist cannot be assessedowing to difficult follow-up and recruitment ofsurvivors in multiple assessment visits.

ConclusionSurvivors from critical illness should be screenedfor different domains of PICS. Half of participantsin the current work experienced post-ICU cognitiveimpairment, which was evident in those withprolonged duration of MV, delirium, stroke, andhypotension. Moreover, 14.29 and 26.19% werereported to have depression and anxiety, respectively,which were more evident in females with prolongedduration of MV and sedation, delirium, andhypoglycemia. Finally, post-ICU physical impair-ments were reported in 35.71%, which was evidentin those with sepsis, undernutrition, and prolongedduration of MV.



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Effect of different classes of obesity on the pulmonary functionsamong adult Egyptians: a cross-sectional studyAshraf M. Hatem, Mohamed S. Ismail, Yasmine H. El-Hinnawy

Introduction Obesity is a common chronic disease,representing a major health hazard. Obesity has severaldelirious effects on the respiratory functions.

Aim of the study To study the effect of obesity on pulmonaryfunctions among our local population of obese adults and toassess the correlation between the severity of lung functionimpairment and the degree of obesity.

Patients and methods Healthy nonsmoker adult patientswere recruited in our cross-sectional study. After full medicalevaluation, measurement of height and weight, andcalculation of BMI, patients underwent spirometry tests withmeasurement of forced expiratory volume in first second(FEV1), forced vital capacity (FVC), forced mid-expiratoryflow, and peak expiratory flow rate. Then, they were classifiedaccording to their BMI into five groups.

Results The study included 293 patients divided into fivegroups according to their BMI. Significant statisticaldifferences were noticed between nonobese patients andpatients with classes II and III obesity regarding FVC, FEV1,and forced mid-expiratory flow, but no differences regarding


peak expiratory flow rate and FEV1/FVC ratio. Overall, 28.9%of the total obese patients presented with restrictive pattern ofspirometry, 2.8% with obstructive, and 2.4% with mixedpatterns.

Conclusion Obesity of especially marked degrees with BMIof more than 35 kg/m2 negatively affects the spirometricparameters. Restrictive pattern was the commonestabnormality observed in the spirometry of obese patients.Egypt J Bronchol 2019 13:510–515© 2019 Egyptian Journal of Bronchology


Keywords: body mass index, Egyptian population, obesity, spirometry

Department of Pulmonary Medicine, Cairo University, Cairo, Egypt

Correspondence to Mohamed S. Ismail, MD, Tiba Gardens, 6th October

City, Giza, 12566, Egypt. Tel: +20 111 001 4998; fax: +20 111 001 4998;


Received 5 March 2019 Accepted 20 March 2019

IntroductionObesity is abnormal or excessive body fat accumulation,which increases the hazards of morbidities [1]. It is themost commonmetabolic disease worldwide [2].Obesityrepresents a major health problem, where it has beenassociated with increased incidence of cardiac andmetabolic diseases as well as some cancers [3]. Obesitycan be associated with many different adverse effects onthe respiratory functions, including alterations in therespiratory mechanics, weakness of the diaphragmaticand other respiratory muscle strength, and impairmentin the gas exchange, leading to limitations in pulmonaryfunction tests (PFT) [4]. One of the widely usedmeasures of obesity is the BMI [3]. The highest levelof obesity among adults was observed among Egyptiansaccording to a publication in 2017 [5].

Aim of the studyThe aim was to study the effect of obesity on pulmonaryfunctions amongour local populationof obese adults andto assess the correlation between the severity of lungfunction impairment and the degree of obesity.






Patients and methodsThis is a cross-sectional study that was conducted atChest Department, Kasr Al Ainy hospitals, CairoUniversity, during the period between 2016 and 2018.

This work was approved by the ethical committee of theFaculty of Medicine, Cairo University. A total of 293patients of both sexes within the age of 18–60 years wereincluded. We included patients who were referred forprebariatric surgery evaluation, in addition to volunteersand hospital visitors. Full clinical assessment includingthoroughhistory taking, physical examination, andPFTwas done for all the study participants. Signed informedconsent was obtained from each patient. We excludedcases with any active cardiopulmonary complaint ordisease, any past history of cardiopulmonary disease,or evidence of chest wall deformities.

Anthropometric measures (height and body weight)were obtained for all included patients. BMI wascalculated according to the formula of weight inkilogram divided by height square in meters.

The cases were categorized into five groups accordingto BMI with reference to the WHO cutoffs [6].

(1)

Medk

Group 1 (normal weight) with BMI of18.5–24.9 kg/m2.

now DOI: 10.4103/ejb.ejb_21_19


Effect of different classes of obesity Hatem et al. 511

(2)

Figu

Stud

Group 2 (overweight) with BMI of 25–29.9kg/m2.

(3)
Group 3 (class I obesity) with BMI of 30–34.9kg/m2.
(4)
Group 4 (class II obesity) with BMI of 35–39.9kg/m2.
(5)
Group 5 (class III obesity) with BMI of more thanor equal to 40 kg/m2.
Spirometry (prebronchodilator and postbronchodilator)wasperformed for all the studypopulationbyusingZAN100 spirometer, 1999 (ZAN Messgeraete GmbHCompany, Oberhulba, Germany) according to theAmerican Thoracic Society criteria [7].

The measured pulmonary function parametersincluded the forced expiratory volume in first second(FEV1), forced vital capacity (FVC), peak expiratoryflow rate (PEFR), forced mid-expiratory flow (FEF25–75%), and the ratio of FEV1 to FVC (FEV1/FVC)in terms of percentage.

We interpreted the spirometric results whereobstructive pattern was diagnosed when FEV1/FVCratio was less than 70% of the predicted value, whereasa restrictive pattern was diagnosed with FVC% lessthan 80% of the predicted value in the presence ofnormal FEV1/FVC, and a mixed pattern wasdiagnosed with the combination of the both [8].

Statistical analysisDatawere coded and entered using the statistical packageSPSS, version 25 (SPSS, IBM Corporation, New York,USA). Data were summarized using mean and SD for

re 1

y groups.

quantitative variables and frequencies (number of cases)and relative frequencies (percentages) for categoricalvariables. Comparisons between groups were doneusing analysis of variance with multiple comparisonspost-hoc test in normally distributed quantitativevariables, whereas nonparametric Kruskal–Wallis testand Mann–Whitney test were used for non-normallydistributed quantitative variables [9]. For comparingcategorical data, χ2 test was performed. Exact test wasused instead when the expected frequency is less than five[10].Pvalues less than0.05were consideredas statisticallysignificant.

ResultsOur study population (Fig. 1) included 293 patients,who were divided according to their BMI into fivegroups irrespective of age or sex. A total of 93 (13.3%)patients formed the normal BMI group. The groupwith overweight patients contained 43 (14.6%)patients, class I obesity group had 46 (15.7%)patients, class II obesity group had 33 (11.3%) cases,whereas class III obesity group had the greatest share,where it included 132 (45%) patients. Data from malesand females were grouped together because we did notfind significant statistical differences between bothsexes regarding the effects of BMI on thespirometric values. Patient characteristics and meanvalues for age, weight, height, and BMI aresummarized in Table 1. It was observed that obesecases were older than the normal and overweightpatients, with significant statistical difference. Ourstudy included 168 males and 125 females within allgroups, withmale sex significantly predominant among

Table 1 Study population demographics

Normal weight Over weight Class I obesity Class II obesity Class III obesity

Mean SD Mean SD Mean SD Mean SD Mean SD P value

Age 32.90 9.79 38.63 14.60 42.63 14.16 47.91 15.87 43.67 13.49 <0.001

Weight 66.21 9.34 80.05 7.72 95.80 10.03 105.79 12.78 137.77 26.19 <0.001

Height 169.13 8.58 170.07 6.98 171.35 7.33 167.21 10.98 166.61 11.02 0.032

BMI 22.89 1.96 27.49 1.21 32.51 1.57 37.70 1.52 49.52 7.98 <0.001

Figure 2

Sex distribution among study groups. F, female; M, male.

Table 2 Mean values for pulmonary function parameters

Normal weight Overweight Class I obesity Class II obesity Class III obesity P value

Mean SD Mean SD Mean SD Mean SD Mean SD

FVC 4.38 0.79 4.40 0.85 3.85 1.03 3.22 1.27 3.26 1.10 <0.001

FVC% 103.22 12.05 103.70 10.37 89.53 22.97 88.94 26.01 87.78 17.46 <0.001

FEV1 3.61 0.65 3.60 0.73 3.09 .81 2.62 1.08 2.69 .89 <0.001

FEV1% 100.67 11.05 102.50 10.34 89.89 19.48 87.09 26.35 86.56 17.63 <0.001

FEV1/FVC 82.74 7.83 81.87 6.80 81.88 7.65 80.83 8.53 82.49 7.32 0.800

FEF 25–75% 88.76 18.74 91.17 17.42 82.87 26.79 75.81 29.62 76.55 25.68 0.005

PEF% 89.72 14.10 92.27 21.96 86.09 16.69 80.71 21.26 81.77 18.88 0.006

FEV1, forced expiratory volume in first second; Flow FEF 27–75, forced mid-expiratory flow; FVC, forced vital capacity; PEF, peakexpiratory flow.


all the study groups (Fig. 2), except within class IIIobesity group, where female sex was predominant, withstatistical significance.

Mean values for pulmonary function parameters (FVC,FEV1, PEFR, FEF 25–75%, and FEV1/FVC ratio) forthe different BMI categories are summarized in Table 2.Therewere significantdifferences inpulmonary functionsin relation to theBMIof the patients.Although themeanvalues of these spirometric parameters lie within thenormal range for all the groups, they were significantlylower when compared with those for the normal andoverweight groups. On comparing the spirometric values

between nonobese and obese patients, we foundstatistically significant differences in FVC and FVC%(P<0.001), FEV1 and FEV1% (P<0.001), and FEF25–75% (P<0.001) between nonobese groups andclasses II and III obesity. Differences regarding theseparameterswere foundalsobetweennonobese groups andclass I obesity group but without significant statisticaldifferences. No significant differences were foundbetween our study groups regarding PEFR and FEV1/FVC ratio.

From Table 3, abnormal PFT was found in 34.1% (72of 211 patients) of the total number of obese patients;

Table 3 Percentage of normal and abnormal pulmonaryfunction test among obese patients

BMI NormalPFT

RestrictivePFT

ObstructivePFT

Mixed Total

Class Iobesity(BMI30–34.9)

34(73.9)

11 (23.9) 1 (2.17) 0 (0) 46(100)

Class IIobesity(BMI35–39.9)

21(63.6)

8 (24.2) 2 (6) 2 (6) 33(100)

Class IIIobesity(BMI ≥40)

84(63.6)

42 (31.8) 3 (2.27) 3(2.27)

132(100)

Total 139(65.9)

61 (28.9) 6 (2.8) 5(2.4)

211(100)

Values are expressed in n (%). PFT, pulmonary function test.


of which, the commonest pattern was restrictive(28.9%) followed by the obstructive and mixedpatterns (2.8 and 2.4%, respectively). It was foundthat BMI has a direct influence on the spirometricmeasures, as the number of patients with abnormalPFT pattern increased at higher BMI values.

DiscussionWe conducted this study to highlight the effect ofobesity on the pulmonary functions of healthyindividuals. Our study included 293 participantsbetween volunteers and obese patients referred forprebariatric surgery evaluation. These patients werecategorized according to their BMI into five groups.To our knowledge, this is the first study to address therelationship between different classes of obesity inotherwise healthy individuals and spirometric valuesamong the Egyptian population.

This study used spirometry as it is a simple, economic,and reproducible tool for pulmonary functionassessment in obese patients. We did not includeother PFTs (e.g. lung volumes and diffusioncapacity). This was also attributed to the fact thatmost of our obese patients were selected from thosewho were referred for prebariatric surgery assessment,where physical examination, chest radiography, andspirometry were routinely required.

From our results, spirometric parameters weredecreased with increasing BMI, where the meanvalues of FVC, FEV1, and FEF 25–75% weresignificantly lower in patients with classes II and IIIobesity when compared with the nonobese patients.These findings were consistent with Schoenberg et al.[11] who declared that increase BMI was associated

with a decrease in the pulmonary function values.Decline in the pulmonary function measures isdirectly proportional with the increase in the BMIand was more marked in patients with extremeobesity (BMI>35 kg/m2), which was also theconclusion of other studies [12].

Ratio between FEV1 and FVC was preserved in ourstudy, and no significant differences were noticedbetween nonobese and obese groups of patientsregarding FEV1/FVC ratio, indicating equalreduction in both parameters (FEV1 and FVC) anddenoting that there were no direct relations betweenobesity and airway obstruction [13,14]. Regarding thatpoint, other studies were opposite to ours, whereincrease in BMI was associated with associated withincreased incidence of airflow limitation andsubsequent decrease in FEV1/FVC ratio [15].Lazarus et al. [16] found that the FEV1 to FVCratio decreases with increasing BMI in overweightand obese men and in morbidly obese women.

We did not find any significant differences betweennonobese and obese groups regarding PEFR. This wasdifferent from previous research findings whichshowed negative correlation between obesity and thePEFR values, which were lower in the obese patients[17,18].

Abnormalities of the BMI, by either increase ordecrease (underweight), were associated with adecline in the pulmonary functions. This was theconclusion of a previous work which showed thecorrelation between increasing of BMI and decreaseof FVC, but without significant effects on FEF 25–75rates [19].

Several factors related to obesity could be responsiblefor this relative decline in the pulmonary functionparameters in our study including impairment of therespiratory system elasticity, elevated mechanical loadof breathing [20], impaired respiratory musclesstrength [21], in addition to decrease in chest wallcompliance and improper expansion of the diaphragmby themechanical effects of abdominal accumulation ofexcess fat [22].

Similar results were obtained by Mahajan et al. [23]where significant decline in FVC and FEV1 wasobserved between obese and nonobese patients,accompanied by insignificant differences regardingPEFR and FEF 25–75% between the nonobese andobese patients, irrespective of age, but this study wasconducted on adult males only.


Moreover, FVC decline with increased BMI wasobserved in a long-term Canadian study [24].According to Jones and Nzekwu [25], it was assumedthat for every increase in body weight by approximately10 kg, there was a fall of FEV1 of 51ml in women, andthey concluded that increased BMI has significantnegative effects on all of the lung volumes.

Our results showed a significant reduction in the FEF25–75 rates which was proportionate with the increasein BMI. A similar study showed that FEF 25–75%declined significantly in severe obesity [26]. On thecontrary, insignificant differences between nonobeseand obese patients regarding FEF 25–75% weredeclared by other studies [23].

In contrast to our study, there was no effect of increasedBMI on different spirometric parameters when studiedover a small sample size of obese adult women [27].Inour study, abnormal PFT with predominance of therestrictive pattern was observed among obese group ofpatients. The ratio of the abnormal function testsincreased significantly with the increase in BMI,reaching its highest proportion among the group ofpatients with BMI more than 40 kg/m2 (31.8%). Thiswas consistent with the findings of Prajapati et al. [28]where abnormal PFT was observed in 58% of theincluded obese patients, from which the commonestpattern was restrictive, representing 32%.

Spirometric values can be affected by increased BMIregardless of age. In a cross-sectional study addressingchildren between 7 and 18 years old, it was shown thatspirometric parameters were adversely affected withobesity especially in cases with extreme obesity [29].

Many studies were conducted in different parts of theworld and showed different results. Ethnicaldifferences could be present affecting the relationbetween obesity and pulmonary functions, forexample, a similar study conducted in Saudi Arabiashowed different results from ours [18]. On thecontrary, a Jordanian study showed results nearlyresembling ours [17]. These differences could alsobe attributed to the distribution of body fat in obesepatients which was not addressed in our study and wasone of our study limitations.

ConclusionObesity is a major health problem in our country. Ourstudy showed a statistically significant correlationbetween increasing of BMI and decline ofpulmonary functions, and the more the BMI the

more the function impairment. Therefore, obesity ofespecially moderate and severe degrees is associatedwith increased risk of pulmonary function impairment.This would emphasize the importance of weightcontrol among Egyptians.


Conflicts interestThere are no conflicts of interest.

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A study of the relationship between pulmonary function testsand both fasting plasma glucose and glycated hemoglobinlevels among asymptomatic cigarette smokersMagdy M. Khalila, Rehab M. Mohammeda, Omnia H.S. Hassanb

Background Tobacco smoke has been recognized as anindependent risk factor for chronic obstructive pulmonarydisease and diabetes mellitus type 2.

Aim To investigate the association between the presence ofairflow obstruction in pulmonary functions and both fastingplasma glucose (FPG) and glycated hemoglobin (HbA1c)levels among asymptomatic cigarette smokers.

Patients and methods Pulmonary function indices viaspirometry and both FPG and HbA1c levels were altogetherassessed in 300 apparently healthy participants attendinghealth checkup clinic. Participants were stratified according totheir smoking status into group I, which included 150 currentand former cigarette smokers (ever smokers), and group II,which included 150 participants with no history of smoking(never smokers).

Results FPG and HbA1c% were significantly higher amonggroup I compared with group II (P=0.001 and 0.002,respectively). However, percent of forced expiratory volumein 1 s/forced vital capacity ratio less than 70%was statisticallymore prevalent among group I compared with group II (17.3,6.7%, respectively, P=0.042), and the odds ratio (OR) of itsoccurrence increasedwith FPGmore than or equal to 100mg/dl [OR=2.91; 95% confidence interval (CI)=1.62–4.01],HbA1c more than or equal to 5.6% (OR=2.07; 95%


CI=0.12–0.77), age more than or equal to 50 years(OR=2.85; 95% CI=1.69–3.99), smoking index more than orequal to 25 pack-years (OR=3.11; 95% CI=1.85–3.66), andBMI more than or equal to 25 kg/m2 (OR=2.33; 95%CI=0.06–0.84).

Conclusion Risk of chronic obstructive pulmonary disease isincreased among asymptomatic current or former cigarettesmokers, especially elderly with impaired glucosehomeostasis.Egypt J Bronchol 2019 13:516–522© 2019 Egyptian Journal of Bronchology


Keywords: chronic obstructive pulmonary disease, cigarette smoking,diabetes mellitus, fasting plasma glucose, glycated hemoglobin, pulmonaryfunction tests

aDepartment of Chest Diseases, Ain Shams University, bDepartment of

Chest Diseases, Police Hospital, Nasr City Branch, Cairo, Egypt

Correspondence to Rehab M. Mohammed, MD, PhD, 54 Abdel Rahman El

Rafaey Street, El Hegaz Square, Heliopolis, Cairo 11786, Egypt. Tel: +20

122 3012 0418;








IntroductionTobacco smoke is a major cause of preventablemorbidity and mortality worldwide. Nicotine, one ofthe many components of tobacco smoke, is establishedto be associated with decrease in the sensitivity ofinsulin in humans, linking smoking with insulinresistance. However, the mechanisms responsible forthis remain unclear, and hence, cigarette smoking hasbeen considered an important risk factor for thedevelopment of insulin resistance and ultimately type2 diabetes mellitus (DM) [1].

Furthermore, tobacco smoke is also the commonestrisk factor for chronic obstructive pulmonary disease(COPD) worldwide [2], which is a disease known toaffect the quality of life significantly [3] as it increasesthe annual decline rate in forced expiratory volume inthe first second (FEV1) and overall, leading toincreased mortality and morbidity compared withnonsmokers [4].

The current understanding of the pathophysiology ofCOPD is that smoking causes the release of cytokines,namely, tumor necrosis factor alpha soluble receptor,

interleukin-1, and interleukin-6, all of whichupregulate the activity of neutrophil and macrophageand consequently initiate a cascade of systemicinflammation [5,6]. Strikingly, these mediators havealso been postulated to play a role in developing insulinresistance in muscle and liver tissue, whichconsequently increases the likelihood of futuredevelopment of DM [7]. Cigarette smoking-relatedsystemic inflammation is therefore hypothesized as thejoint etiology independently influencing both lungfunction and glycemia [8,9].

Several large-scale western studies have linked COPDto DM, an example of which is an American studyconducted on 47 million people investigating theprevalence of COPD-associated comorbidities, andit concluded that DM is significantly prevalent inpatients with COPD [10].



PFT, FPG, HbA1c in asymptomatic smokers Khalil et al. 517

On the contrary, research into the association betweendiabetes and pulmonary function in diabeticsubpopulation free from overt pulmonary disease hasresulted in inconsistent outcomes among studies. Arecent meta-analysis on 40 studies attempted tocharacterize pulmonary function in such populationcompared with normoglycemic participants andconcluded that impaired pulmonary function, but ofthe restrictive pattern, is the most frequentlyencountered pattern [11].

However, another study analysis of a large Japanesecohort (follow-up period up to 2 years) suggested thatlow lung volume might in fact be a risk factor forimpaired glucose tolerance rather than impairedfasting glucose (IFG), but an association betweenprediabetes and early stages of COPD could not befound [12].

The aim of the current study was to investigate theassociation between prevalence of airflowobstruction as reflected in pulmonary functiontests measured via spirometry and two markersthat define glycemic status, namely, fasting plasmaglucose (FPG) levels and glycated hemoglobinconcentration (HbA1c%) among asymptomaticcigarette smokers.

Patients and methodsThis is a prospective observational study enrolling300 participants, aged 40 years or older, attendingthe health checkup clinic at Nasr City PoliceHospital during the period between April 2017and April 2018. Participants were grouped intotwo groups based on their smoking status: groupI, which included 150 apparently healthy cigarettesmokers (whether current or former) (ever smokersgroup) as the study group, and group II, whichincluded 150 never smokers matched for as acontrol group (never smokers group). Smokingstatus is defined by the Centers for DiseaseControl and Prevention, where current smoker isthe one who has smoked 100 cigarettes in his or herlifetime and who currently smokes cigarettes, andformer smoker is the one who has smoked 100cigarettes at least in his or her lifetime but whohad quit smoking, whereas a never smoker is the onewho has never smoked or has smoked less than 100cigarettes in his or her lifetime. Cumulative exposureto tobacco smoke was also quantified via calculationof the smoking index (SI) defined as number ofcigarette packs smoked a day for a year (pack-years)[13].

Exclusion criteriaAny individual with physician-diagnosed airwaydisease and patients known to be on antidiabeticmedication (insulin and/or oral hypoglycemictherapy) to avoid bias related to drug-induced effectson glycemic markers were excluded.

All enrolled participants were subjected to full historytaking (with emphasis on detailed smoking historyregarding duration and amount of cigarettes smokedper day) and physical examination, including BMI,which was calculated as person’s weight in kilograms/square the height in meters (kg/m2), where18.5–24.9 kg/m2 was considered normal/healthybody weight and 25 kg/m2or more was the referencepoint for overweight according to Centers for DiseaseControl and Prevention [14].

Laboratory investigationsVenous blood samples were collected after 12 h offasting and measured via automatic clinicalchemistry analyzer. FPG levels were measured byenzymatic methods. High-performance liquidchromatography was used to assess HbA1c followingthe National Glycohemoglobin StandardizationProgram protocol [15].

Serum FPG level of 100mg/dl or more was taken as acutoff value to indicate IFG, a category of intermediatehyperglycemia, whereas HbA1c concentrations of5.6% or higher was taken as a cutoff value to reflectchronic hyperglycemia. Both are glycemic markers forprediabetic stage according to the American DiabetesAssociation [16].

Standard pulmonary function testSpirometry was carried out with spirosift spirometer5000 FUKDa NENSHI. Percent of predicted forcedvital capacity (FVC, %pred), FEV1 %pred, FEV1 :FVC ratio (FEV1/FVC %), and forced expiratory flowrate 25–75 (FEF 25–75, %pred) were all measured,taking the best out of three technically satisfactoryperformances [17]. FEV1/FVC % is 70–80% innormal adults; values less than 70% reflect airflowlimitation according to the Global Initiative forChronic Obstructive Lung Disease (GOLD) [2].

Ethical considerationsOur study gained approval by the ethical committeeboard. All participants gave informed consent beforeenrollment. All participants were reassured about theconfidentiality of the data, their right to withdraw fromthe study at any time without giving any reasons, and


without effecting their rights to medical care in case ofrefusal to participate.

Statistical analysesAnalysis of the collected data was done via the‘Statistical Package for the Social Sciences (SPSS,IBM Corp. Armonk, NY, USA) for Windows’program, version 22.0. Mean±SD was calculated forcontinuous data, whereas number (percentage) wascalculated for qualitative data. Analytical statisticswas then performed between group I (ever smokersgroup) and group II (never smokers group) using thefollowing: independent t test to compare between thetwo groups with normal continuous data distribution,Mann–Whitney test to compare between the twogroups with nonnormal continuous data distribution,and χ2 test or Fisher exact test to compare between twoor many categorical groups. Finally, logistic regressionanalysis was done along with calculation of the odds

Table 1 Baseline characteristics of the enrolled participants

Group I (ever smokers) (N=150)

Age (years)

Range 40–62

Mean±SD 50.43±6.75

Sex [n (%)]

Male 137 (91.3)

Female 13 (8.7)

BMI (kg/m2)

Range 21.5–36.1

Mean±SD 27.75±4.27

SI (pack-years)

Range 18.0–38.1

Mean±SD 27.83±5.70

Spirometry FVC, %pred

Range 65–95

Mean±SD 79.22±9.18

FEV1, %pred

Range 55–70

Mean±SD 62.63±4.44

FEV/FVC, %

Range 59.1–104.6

Mean±SD 80.06±10.36

<70% [n (%)] 26 (17.3)

FEF25–75, %pred

Range 52–70

Mean±SD 60.77±5.86

Glycemic control indices FPG (mg/dl)

Range 82–165

Mean±SD 125.9±24.90

HbA1c (%)

Range 5.35–6.35

Mean±SD 5.88±0.31

FEF 25–75, %pred, percent of predicted forced expiratory flow at 25–75forced expiratory volume in 1 s; FEV1/FVC, %, percent of ratio of FEV1predicted forced vital capacity; HbA1c, glycated hemoglobin concentrati0.01. NS, nonsignificant, P value more than or equal to 0.05. S, signific

ratios (ORs) and 95% confidence intervals (CI) toevaluate the probability of an FEV1/FVC, % <70%among different variables considered as potential riskpredictors. P value less than 0.05 was consideredstatistically significant.

ResultsBaseline characteristics of all 300 enrolled participantsare summarized in Table 1. The two groups (everversus never smokers) were matched for age, sex,and BMI with no statistically significant differences(P=0.2362 for age, P=0.3463 for sex, and P=0.209 forBMI).

Both indices of glycemic control were higher amongparticipants of group I (ever smokers) compared withthose of group II (never smokers), with a highlysignificant statistical difference between both groups

Group II (never smokers) (N=150) P value

40–62 0.2362 NS

50.99±6.57

135 (90) 0.3463 NS

15 (10)

22–35.6 0.209 NS

27.36±4.09

–

–

75–100 0.0083 HS

87.43±7.45

60–80 0.001 HS

71.23±5.63

63.3–103.95 0.042 S

81.99±8.90

10 (6.7)

63–78 0.003 HS

69.96±4.67

72–125

97.37±14.73 0.001 HS

4.62–5.92 0.002 HS

5.31±0.42

% of the pulmonary volume; FEV1, %pred, percent of predictedto FVC; FPG, fasting plasma glucose; FVC, %pred, percent ofon; SI, smoking index. HS, highly significant, P value less thanant, P value less than 0.05.

Table 2 Correlation between smoking index and each of theglycemic and spirometric variables in group I (ever smokers’group) (N=150)

Smoking index (pack-years)Pearson correlation (r) P value

HbA1c 0.377 0.021 S

FPG 0.462 0.015 S

%FVC −0.468 0.001 HS

%FEV1 −0.499 0.005 HS

%FEV/FVC −0.426 0.002 S

FEF 25–75 −0.368 0.0311 S

FEF 25–75, %pred, percent of predicted forced expiratory flow at25–75% of the pulmonary volume; FEV1, %pred, percent ofpredicted forced expiratory volume in 1 s; FEV1/FVC, %, percentof ratio of FEV1 to FVC; FPG, fasting plasma glucose; FVC, %pred, percent of predicted forced vital capacity; HbA1c, glycatedhemoglobin concentration. HS, highly significant, P value less than0.01. S, significant, P value less than 0.05.


(P=0.001 for FPG level versus P=0.002 forHbA1c%).

On the contrary, spirometric parameters were lower ingroup I compared with group II, with a highlysignificant statistical difference regarding FVC, %pred (P=0.0083), FEV1, %pred (P=0.001), andFEF 25–75, %pred (P=0.003) and a significantstatistical difference regarding FEV1/FVC, %(P=0.042).

Moreover, the prevalence of airflow obstruction asdefined by FEV1/FVC, % less than 70% was higheramong group I participants compared with group II[26/150 (17.3%) and 10/150 (6.7%), respectively] witha statistically significant difference between bothgroups (P=0.042).

In addition, Pearson correlation coefficient wascalculated to investigate the association between theSI and each of the glycemic and spirometric indices ingroup I. Results revealed a significant positivecorrelation between SI and both FPG level(r=0.462, P=0.015) and HbA1c % (r=0.377,P=0.021) versus a significant inverse correlation withFVC, %pred (r=−0.468, P=0.001), FEV1, %pred(r=−0.499, P=0.005), FEV/FVC, % (r=−0.426,P=0.002), and FEF 25–75, %pred (r=0.368,P=0.0311) (Table 2).

Further analysis of the potential risk factors that mightbe associated with the occurrence of airflow obstruction(% FEV1/FVC<70%) among group I participants wasthen done. Comparing participating smokers withFEV1/FVC less than 70% with their counterpartswith FEV/FVC more than or equal to 70%, therewas a statistical significant difference in age(P=0.003), BMI (P=0.036), SI (P=0.016), and bothglycemic markers FPG (P=0.016) and HbA1c%(P=0.041), whereas there was no statisticaldifference regarding the sex of the enrolledparticipants (P=0.218) (Table 3).

Multivariable logistic regression was performed toevaluate the OR and 95% CI of airflow obstruction(FEV1/FVC, % <70%) associated with theaforementioned variables considered as potential riskpredictors, including clinical variables age, BMI,cumulative smoking exposure quantified by SI, inaddition to both of the glycemic control indices,FPG and HbA1c. Results of the current studyrevealed that the risk of developing FEV1/FVC, %less than 70% almost tripled among ever smokers withSI more than or equal to 25 pack-years (OR=3.11, 95%

CI=1.85–3.66), with FPG more than or equal to100mg/dl (OR=2.91, 95% CI=1.62–4.01) and thosewho were more than or equal to 50 years (OR=2.85,95% CI=1.69–3.99), whereas this risk doubled amongever smokers with BMImore than or equal to 25 kg/m2

(OR=2.33, 95% CI=0.06–0.84) and HbA1c morethan or equal to 5.6% (OR=2.07, 95%CI=0.12–0.77) (Table 4).

DiscussionSeveral epidemiological studies have gatheredincreasing body of evidence that tobacco smoke is anindependent risk factor for COPD, insulin resistance,and type 2 DM [1,2], perhaps through initiation ofsystemic inflammation, which is common to both[8,9,18].

The presence of a common ground and a jointetiology between both diseases led us tohypothesize that an association between pulmonaryfunction and glycemic status could possibly existamong cigarette smokers even at very early stagesbefore clinical characteristics for either diseasesbecame evident. If such hypothesis is valid andsuch an association could be established so thatthe existence of one could be a predictor for theother, this could be of great significance in thecontext of preventive medicine as early detectionis key to disease control, delaying, and decreasingrisk of complications as well as reducing medicalcosts.

In the current work, we aimed to clarify the associationbetween pulmonary function tests, particularly airflowlimitation, and two glycemic indices, namely, FPG andHbA1c, among asymptomatic apparently healthycigarette smokers.

Table 3 Comparison of potential risk factors associated with percent forced expiratory volume in 1 s/forced vital capacity ratioless than 70% in group I (ever smokers group)

%FEV/FVC (<70%) (N=26) %FEV/FVC (≥70%) (N=124) P value

Sex

Male/female 20/6 117/7 0.218 NS

Age

Range 40.0–62.0 40.0–60.0 0.003 HS

Mean±SD 52.96±7.30 48.90±6.54

BMI

Range 22.1–35.50 21.5–36.10 0.036 S

Mean±SD 28.26±4.07 26.86±4.32

SI

Range 22.00–36.00 18.00–38.00 0.016 S

Mean±SD 29.69±5.24 26.10±5.82

FPG

Range 82.00–165.00 83.00–145.00 0.016 S

Mean±SD 122.69±18.32 106.48±18.26

HbA1c

Range 5.35–6.35 5.35–6.35 0.041 S

Mean±SD 5.94±0.34 5.81±0.31

%FEV1/FVC, percent of ratio of FEV1 to FVC; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin concentration; SI, smokingindex. HS, highly significant, P value less than 0.01. S, significant, P value less than 0.05. NS, nonsignificant, P value more than or equalto 0.05.

Table 4 Multiple logistic regression analysis of different riskfactors associated with percent forced expiratory volume in1 s/forced vital capacity ratio less than 70%

Factor OR CI at 95.0% P value

Age (years)

<50 1.00 1.69–3.99 0.021 S

≥50 2.85

BMI (kg/m2)

<25 1.00 0.06–0.84 0.016 S

≥25 2.33

SI (pack-years)

<25 1.00 1.85–3.66 0.018 S

≥25 3.11

FPG (mg/dl)

<100 1.00 1.62–4.01 0.0068 HS

≥100 2.91

HbA1c (%)

<5.6 1.00 0.12–0.77 0.026 S

≥5.6 2.07

CI, confidence interval; FPG, fasting plasma glucose; HbA1c,glycated hemoglobin concentration; OR, odds ratio; SI, smokingindex. HS, highly significant, P value less than 0.01. S, significant,P value less than 0.05. NS, nonsignificant, P value more than orequal to 0.05.


FPG and HbA1c levels are recommended by theAmerican Diabetes Association as diagnostic andscreening tools for diabetes where elevated levelsindicate IFG and chronic hyperglycemia, respectively[16]. IFG is a category of intermediate hyperglycemiabut is not considered a clinical entity by itself, but it is arisk factor for future diabetes and diabeticcomplications. Moreover, it is associated withimpaired secretion of insulin, whereas HbA1c

concentration is a marker of long-term glucosehomeostasis .which reflects average plasma glucosein the past 2–3 months [19].

The main findings of the current work were thatFEV1/FVC, % was significantly lower among eversmokers compared with never smokers, and theprevalence of FEV1/FVC less than 70% was higheramong group I participants compared with group II(17.3 vs. 6.7%, respectively; P=0.042). Moreover, bothFEV1, %pred and FEF 25–75, %pred values weresignificantly lower in group I versus group II(P=0.001 for the former vs. P=0.003 for the latter).

Both glycemic indices were significantly higher amongever smokers compared with never smokers (P=0.001for FPG level vs. P=0.002 for HbA1c%), and the mean±SD of both markers was also significantly higheramong smokers with FEV1/FVC less than 70% (26/150) than among their counterparts with FEV/FVCmore than or equal to 70% (124/150) (P=0.016 forFPG vs. P=0.041 for HbA1c%). These results are inaccordance with those of Akpinar et al. [20] whoreported that fasting hyperglycemia, one of thecomponents of metabolic syndrome, was significantlyprevalent among patients with COPD. Our results alsomatch those of Baba et al. [3] who studied ∼1000apparently healthy Japanese volunteers and found thatHbA1c levels were increased in patients having FEV1/FVC less than 70% than in patients without an airflowlimitation. Moreover, Sato et al. [21] found a greaterdecline in FEV1 among smokers with metabolic


syndrome and suggested that the underlying systemicinflammation may cause endothelial dysfunction,which might have a synergistic effect on the directendothelial injury secondary to tobacco, resulting inenhanced annual decline in FEV1.

On studying the correlation between the total smokingexposure as quantified by the SI in pack-years and eachof the aforementioned variables, results showsignificant inverse correlation between SI and %FEV/FVC (r=−0.426, P=0.002), %FEV1 (r=0.499,P=0.005), and FEF 25–75 (r=0.368, P=0.0311) versusa significant positive correlation with both FPG level(r=0.462, P=0.015) and HbA1c % (r=0.377,P=0.021). These findings matched that of Sargeantet al. [8] who reported a dose–response relationshipbetween HbA1c levels and daily number of cigarettessmoked and a positive association with SI. They addedthat the adjusted increase inHbA1c for a SI of 20 pack-years is 0.08 and 0.07% for men and women,respectively, concluding that cigarette smoking haslong-term effect on glucose homeostasis.

Logistic regression analysis was conducted todetermine the risk of developing airflow obstruction(FEV1/FVC<70%) associated with differentpotential risk predictors, and our results showedthat the risk was highest (three folds) withcumulative smoking exposure more than or equal to25 pack-years (OR=3.11, 95% CI=1.85–3.66), withFPG more than or equal to 100mg/dl (OR=2.91,95% CI=1.62–4.01), and with age more than or equalto 50 years (OR=2.85, 95% CI=1.69–3.99), whereasthe risk was less (two folds) with BMI more than orequal to 25 kg/m2 (OR=2.33, 95% CI=0.06–0.84)and HbA1c more than or equal to 5.6% (OR=2.07,95% CI=0.12–0.77).

Therefore, according to the results of this study, therewas a higher likelihood of FEV1/FVC less than 70%with FPG levels of 100mg/dl or above than withHbA1c% of 5.6% or more (OR=2.91 vs. 2.07,respectively), which implies a stronger associationbetween the occurrence of airflow limitation and theglucose tolerance status rather than glucosehomeostasis. This finding is intriguing and ratherdifficult to explain because FPG reflects plasmaglucose level at a certain point in time, whereasHbA1c gives estimated average plasma glucose levelsfor the past 2–3 months, which makes it a steadier andmore reliable glycemic parameter. A possibleexplanation might be that elevated FPG and IFGmight be transitory and reversible leading to bias inresults as they are affected by several factors such as

diet, stress [22], as well as the amount of cigarettessmoked at the time of testing because nicotine intobacco smokers decreases insulin sensitivity bybinding to nicotinic acetylcholine a1 receptors inhuman skeletal muscle, and this effect isproportional to the daily number of cigarettessmoked [23].

Some limitations of the present study should bementioned: the study population had a small samplesize, but we owe this to the relatively small numberattending the health checkup clinic, as a relatively smallpercent of Egyptian population has the culture of doingregular checkups without having any complaints.Moreover, this was an observational study, in whichthe results reflect a certain point in time, so furtherlongitudinal (follow-up) studies are needed to assessthe progress of the findings. Moreover, the possibilityof bias in selection cannot be denied, because wecollected our data only from patients attendinghealth checkup clinic, and further studies amongdifferent population groups are required to prove ordisapprove our results. Moreover, we did not assay theoral glucose tolerance test, and therefore,underestimation of cases who have impaired glucosetolerance with fasting normoglycemia is a possibility.Furthermore, inflammatory markers reported to berelated to lung dysfunction and the development ofdiabetes were not assessed.More studies are required toclarify the mechanisms linking risk of diabetes and lungdysfunction. These studies need to focus on dataconcerning insulin secretion and resistance, andother clinical markers related to diabetes and itsdevelopment. Finally, our findings suggestassociation between airflow obstruction and impairedglycemic status, but association does not necessarilymean causation, and further studies are need to tacklethis point.

ConclusionOur results suggest an association and increased risk ofCOPD among asymptomatic cigarette smokers,current/former, especially elderly with impairedglucose homeostasis. Developing a risk scorecombining known risk factors for COPD includinga measure of blood glucose in screening studies wouldbe recommended.




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Delay in the diagnosis and management of tuberculosis amongpatients in the Suez Canal AreaNoha M. Abu Bakr Elsaida, Amany H. Refaata, Lamiaa A. Fialaa,b,Eman R. Hamedc

Background Delayed diagnosis of tuberculosis (TB) canenhance the transmission of infection and worsen prognosis.

Aim To identify the risk factors of delay in the management ofTB cases for early management.

Patients and methods A cross-sectional analytic study wasperformed to assess the proportion, duration of delay, and itsdeterminants in the diagnosis and management of TB. Toassess the risk factors of delay, the sample was dichotomizedinto ‘delay’ and ‘nondelay’ groups taking the median totaldelay in the diagnosis and management of TB as a cutoffpoint. The study included 183 TB patients, who wereregistered in TB records during the study period (first ofJanuary to end of June, 2017). Data were collected by aninterview questionnaire.

Results Nearly half of patients (49.20%) had unacceptabletotal delay in the diagnosis and management of TB. Themedian of total delay, patient delay, and health-care systemdelay were 65, 14, and 20 days, respectively. Significant riskfactors of total delay in the diagnosis and management of TBwere not consulting the health-care provider after onset ofsymptoms (P=0.002), visiting initially the health facility otherthan the chest hospital/TB clinics (P=0.019), not consulting achest physician initially (P=0.043), negative sputum smear(P=0.001), more than two health visits before initial diagnosis(P<0.001), while low-degree TB stigma was protective(P=0.006).


Conclusion Nearly half of patients had unacceptable totaldelay in the diagnosis and management of TB. The maindeterminants were seeking pharmacies instead of visitinghealth-care providers, not visiting initially chest hospital/TBclinics, not consulting a chest physician initially, negativesputum smear, and more than two health visits before initialdiagnosis.Egypt J Bronchol 2019 13:523–530



Keywords: health-care system delay, patient delay, Suez Canal Area andEgypt, total delay, tuberculosis

Departments of , aDepartment of Public Health, Community, Environmental

and Occupational Medicine, Faculty of Medicine, Suez-Canal University,

Ismailia, Egypt, bDepartment of Health Sciences, Faculty of Health and

Rehabilitation Sciences, Princess Noura Bint Abdulrahman University,

Riyadh, Saudi Arabia, cInternal Medicine, Chest Diseases, Faculty of

Medicine, Suez Canal University, Ismailia, Egypt

Correspondence to Eman R. Hamed, MD, Department of Internal Medicine,

Chest Diseases and Tuberculosis, Faculty of Medicine, Suez Canal

University, Ring Road, Ismailia 41522, Egypt. Tel: +20 100 507 8126;


Received 14 January 2019 Accepted 29 July 2019

IntroductionGlobally, tuberculosis (TB) is the ninth leading causeof death and the leading cause from a single infectiousagent, ranking above HIV/AIDS [1]. Most deathsfrom TB could be prevented with early diagnosisand appropriate treatment. However, death rates arestill high [2]. Delayed TB diagnosis and managementcan be attributed to patients as well as to the health-care system [3] and reported in both high-prevalenceand low-prevalence countries [4]. Owing to the limiteddata in Egypt, our aim was to identify the risk factor ofdelay in the management of TB cases for earlymanagement.






Patients and methodsDesignA cross-sectional analytic study was done from the firstof January to end of June, 2017 to measure theproportion and duration of delay in the diagnosisand management of TB cases. To assess thepredictors of delay, the sample was dichotomizedinto ‘delay’ and ‘nondelay’ groups taking the median

total delay (65 days) as a cutoff point [5,6]. Aninterview questionnaire was applied to collect data.

Study patientsAll new TB patients registered in Directly ObservedTreatment Program of TB who were smear positive orsmear negative (clinical diagnosis) and diagnosed ofextrapulmonary TB (pleural, lymph node, bone TB, orTB of other organs than the lungs) were included in thestudy. Cases who were too ill to be interviewed, refusedparticipation and retreatment cases were excluded fromthe study.

SettingThis study was conducted in the Suez Canal Area inthree chest hospitals of Ismailia, Suez, and Port SaidGovernorates from January to June 2017.




Sampling methodThe sample was taken using a comprehensive method.The total numbers of registered cases were 215 duringthe period of data collection. They were distributed asfollows: 112 cases in Ismailia, 48 cases in Port Said, and55 cases in Suez Governorate. In Ismailia governorate,11 cases were excluded (five cases were retreated and sixcases had refused participation in the study). In PortSaid Governorate, 10 cases of the total number ofregistered cases had refused participation in thestudy. In Suez governorate, 11 cases were excluded(two were in prison/difficult to reach, two were severelyill, two were retreated, and five refused to participate).

Table 1 Sociodemographic characteristics of studied patients(age, sex, city, and residence) (N=183)

Characteristics n (%)

Age (years)

15–30 67 (36.6)

>30–45 60 (32.8)

>45–60 38 (20.8)

>60 18 (9.8)

Mean±SD 39.1±14.7

Data collection toolsData were collected by using an interviewquestionnaire. It was standardized, adopted fromWHO [5], and it was ready in Arabic language,with scoring of variables and interpretation of theirvalues. Some items had been verified using the DOTSpatients’ records and laboratory data in the chesthospitals/TB clinics. The questionnaire includedinformation about: sociodemographic characteristicsof the patient, risk factors, present history of currentillness, health-care seeking behavior, TB stigma,satisfaction with health care, knowledge about TB,and date of diagnosis and treatment. This study hasthe approval of the ethics committee of Faculty ofMedicine, Suez Canal University, ethics committeenumber: 2894.

Definition of study variables: patient delay is the timeinterval between onset of symptoms and first visit to ahealth-care provider. Diagnostic delay is the timeinterval between presentation to a health-careprovider and diagnosis of TB. Treatment delay isthe time interval between diagnosis of TB andinitiation of anti-TB treatment. Health-care systemdelay is the time interval between presentation to ahealth-care provider and initiation of anti-TBtreatment (health-care system delay includesdiagnostic and treatment delay). Total delay is thetime interval between onset of symptoms andinitiation of anti-TB treatment (total delay includespatient delay and health-care system delay).

Sex

Male 124 (67.8)

Female 59 (32.2)

City

Ismailia 101 (55.2)

Suez 44 (24)

Port Said 38 (20.8)

Residence

Rural 24 (13.1)

Urban 159 (86.9)

Statistical analysisData entry and statistical analysis was performed usingthe statistical package for the social sciences (SPSS)software program, version 20 (IBM, Armonk, NewYork, USA). Descriptive statistics such as mean,median, and SD were calculated to show proportion,length of delay, and the distribution of the populationby sociodemographic characteristics. For assessing the

risk factors for delay, comparisons between groups weredone using the χ2 test or Fisher’s exact test asappropriate for qualitative variables, and usingMann–Whitney test for quantitative variables.Ninety-five percent confidence interval and oddsratio (OR) was used to assess the associated riskfactors of the different delays. Logistic regressionanalysis was performed to investigate the predictorsof total delay.

ResultsA total of 183 TB patients from three chest hospitals ofSuez Canal Area receiving TB treatment were enrolledin the study from January to June 2017. Thirty onepercent of patients were older than 45 years old, TBwas common in men with a male/female ratio of 2.1.The proportion of patients who could read and writewas the least 8%.More than half of patients (59%) wereemployed. The majority of patients (87%) were livingin urban areas and were mostly married (62%) (Tables 1and 2). Most of the patients (72%) had pulmonary TB,while only 28% had extrapulmonary TB (Fig. 1).

The types of delay were classified as the following:

(a) Patient delay, (b) diagnostic delay, (c) treatmentdelay (both b–c constitute health-care system delay),and (d) total delay. All patients had no treatment delayas they received treatment immediately after diagnosis(Table 3). Nearly half of patients (49.20%) hadunacceptable total TB delay of more than 65 days(median value) in the diagnosis and management ofTB (Fig. 2).

Table 3 Frequency of tuberculosis risk factors among studiedpatients (N=183)

Risk factors n (%)

Smoking status

Delayed TB diagnosis and treatment Elsaid et al. 525

Regarding determinants of total delay in the diagnosisand management of TB, patients who soughtpharmacies immediately after onset of symptomsinstead of visiting health-care providers hadincreased risk of total delay (OR=2.5) (Table 4).Patients who sought first consultation from health-care facility other than chest hospital/TB clinics,patients who initially had visited different specialtiesother than chest and patients with more than two visitsto health-care facility before initial diagnosis also hadincreased risk of total delay (Table 4). Patients withnegative sputum smear results (Table 5) had increasedrisk of total delay (OR=5.21). Old patients more than60 years old (OR=0.74) (Table 6) and patients withlow-degree perceived TB stigma (OR=0.43) (Table 5)were protective against total delay.

Table 2 Sociodemographic characteristics of studied patients(education, occupation, income, and marital status) (N=183)

Characteristics n (%)

Education

Illiterate/read and write 59 (32.3)

Primary middle/high school 104 (56.8)

University or higher 20 (10.9)

Occupation

Employed 107 (58.5)

Student 10 (5.5)

Unemployed 66 (36)

Income

Have savings 33 (18)

Income=expenses 105 (57.4)

In debt 45 (24.6)

Marital status

Married 113 (61.7)

Single 51 (27.9)

Divorced or separated 8 (4.4)

Widowed 11 (6)

Figure 1

Distribution of patients according to the type of TB (N=183). TB, tubercu

Not consulting a health-care provider as the firstbehavior and negative sputum or extrapulmonary TBwere significant risk factors of total delay on binarylogistic regression analysis. Low number of encounterswas significantly protective against total delay. Notseeking medical care from a health-care provider atthe onset of symptoms had the highest OR (5.04) fortotal delay compared with those who sought care froma healthcare provider (Tables 7–9).

losis.

Never 86 (47)

Current 76 (41.5)

Ex-smoker 21 (11.5)

Shisha smoker 20 (10.9)

Daily consumption of cigarettes among smoker

Median 10

Minimum–maximum 4–60

Duration of smoking among smokers

Median 6

Minimum–maximum 2–50

Previous exposure to TB patients

No 145 (79.2)

Yes 75 (20.8)

Chronic disease

No 138 (75.4)

Yes 45 (24.5)

HIV 2 (4.4)

DM 28 (62.2)

Othersd 15 (33.3)

DM, diabetes mellitus; TB, tuberculosis. dOthers chronic liverdisease, hypertension, renal disease, cardiac diseases, andthyroid disease.

Figure 2

Proportion of total delay among the studied patients (N=183).

Table 4 Risk factors for total delay among the studied patients (previous exposure to tuberculosis, cough first symptom, andtuberculosis type) (N=183)

Risk factors Delayed (>median) (N=90) [n (%)] Not delayed (≤ median) (N=93) [n (%)] OR and 95% CI P value

Previous exposure to TB

Yes@ 17 (44.7) 21 (55.3) 0.79 (0.39–1.64) 0.5381

No 73 (50.3) 72 (49.7)

Cough first symptom

Yes@ 65 (47.1) 73 (52.9) 0.71 (0.36–1.4) 0.3251

No 25 (55.6) 20 (44.4)

TB type

Pulmonary@ 59 (45) 72 (55) 0.56 (0.29–1.07) 0.0751

Extrapulmonary 31 (59.6%) 21 (40.4%)

CI, confidence interval; OR, odds ratio; TB, tuberculosis. 1χ2 test. @Reference group. *Statistically significant at P value less than 0.05.


DiscussionThis study was performed in Ismailia, Port Said, andSuez chest hospitals. One hundred and twenty TBcases were treated annually and 10 TB cases wereadmitted in Ismailia Chest Hospital. It contains 67inpatient beds, three ICU beds, chest radiography, anda laboratory. Sixty four cases were treated and 25 caseswere admitted in Port Said Chest Hospital. It contains42 beds, chest radiography, and a laboratory. Sixty onecases were treated in Suez Chest Hospital, no TB caseswere admitted. It contains 140 beds, chest radiographyand a laboratory.

In this study most of the TB patients were in theirmiddle age (15–35years) which could be explained bythe decreasing effectiveness of TB vaccine andincreased risk of getting infection. These findingswere consistent with previous studies in Ismailia [7]and Aswan [8]. Male TB infection to female ratio was2 : 1, probably because men are spending much moretime outdoors and contacting more people thanwomen. Moreover, Egyptian men have higher riskof TB infection due to the higher prevalence of

smoking among them (40%) as compared with thewomen (1.5%) [9], and smoking is an important riskfactor for TB infection [10].

In this study, only 10.9% of patients had finisheduniversity, all of them had statistically significanthigher level of good TB knowledge compared withothers and about 65% of them had not patient delay.Osei et al. [11], found that patients who attainedformal education to at least the primary level weremore likely to have previous knowledge of TBcompared with patients with no formal education.ElHadidy et al. [12] found that TB knowledge werepoor among tuberculous patients than amongnontuberculous individuals. About 36% of currentpatients were unemployed which is greater than theunemployment rate in the general population of Egypt(11.3%) [13]. Nearly quarter of patients (24.6%) hadbeen in debt. These findings on illiteracy,unemployment rates, and monthly income indicatethat TB patients were a disadvantaged group in thecommunity and the findings were consistent with theWHO’s labeling of TB as a disease of poverty [5].

Table 5 Risk factors for total delay among studied patients (N=183)

Risk factors Delayed (>median) (N=90) [n(%)]

Not delayed (≤ median) (N=93) [n(%)]

OR and 95% CI P value

Time to reach health facility

< ½ an hour@ 45 (45.9) 53 (54.1) 1 0.6361

½ an hour–1 hour 33 (53.2) 29 (46.8) 0.75 (0.39–1.41)

>1 h 12 (52.2) 11 (47.8) 0.78 (0.31–1.93)

Health-seeking behavior at onset of symptom

Health-care provider@ 50 (40.8) 71 (59.2) 1 0.0042*

Self-medication /traditionalmedicine

2 (100) 0 (0) 0

Pharmacies 38 (33.3) 22 (66.7) 2.45 (1.3–4.7464)2.5

First health facility patient sough for consultation

Chest hospital@ 13(32.5) 27 (67.5) 1 0.0192,*

Primary health-care unit 7 (77.8) 2 (22.2) 7.27 (1.32–39.99)

Public hospital outpatient clinic 18 (64.3) 10 (35.7) 3.74 (1.35–10.34)

Private practice (hospital orclinics

52 (49.1) 54 (50.9) 2 (0.93–4.29)

Specialty of health-care provider

Chest@ 37 (39.8) 56 (60.2) 1 <0.0431,**

Internist 28 (57.1) 21 (42.9) 2.02 (1–4.07)

General practitioner 9 (75) 3 (25) 4.54 (1.15–17.89)

Others 16 (55.2) 13 (44.8) 1.86(0.8–4.32)

Number of encounters

1–2 times@ 32 (29.6) 76 (70.4) 1 <0.0011,**

3–5 times 41 (73.2) 15 (26.8) 6.49 (3.16–13.36)

>5 times 17 (89.5) 2 (10.5) 20.19 (4.41–92.51)

Radiography

Positive@ 55 (43.7) 71 (56.3) 1 0.0841

Negative 19 (61.3) 12 (38.7) 2.04 (0.91–4.57)

Not performed 16 (61.5) 10 (38.5) 2.07 (0.87–4.91)

Sputum result

Positive@ 49 (40.2) 73 (59.8) 5.21 (1.04–26.15) 0.0012,**

Negative 7 (77.8) 2 (22.2)

Perceived stigma

Low degree@ 35 (38.9) 55 (61.1) 0.43 (0.24–0.79) 0.0061,*

High degree 55 (59.1) 38 (40.9)

TB knowledge level

Good knowledge@ 62 (50.4) 61 (49.6) 0.86 (0.46–1.6) 0.6351

Poor knowledge 28 (46.7) 32 (53.3)

CI, confidence interval; OR, odds ratio; TB, tuberculosis. 1χ2 test. 2Fisher’s exact test. @Reference group. *Statistically significant at Pvalue less than 0.05. **statistically significant at P value <0.01


In the present study, the proportion of patient delaywas 48.10% with median patient delay of 14 days.Longer median patient delay was observed inZimbabwe (28 days) [14], Northwest Ethiopia (21days) [6], Angola (30 days) [15], and in Ghana (59days) [11]. Better patient delay in this study could bedue to: better accessibility to health-care services,different proportion of patients ever heard about TBbefore diagnosis, good TB knowledge level, the lowlevel of perceived TB stigma, and differences insociodemographic characteristics between currentand other studies.

Results showed that being married was a significantrisk factor for patient delay. Also, all separated anddivorced patients were delayed, while single andwidowed had the least patient delay. This could bedue to the heavy family and financial responsibilitiesthat led to a delay in health-seeking advice on time.Separated and divorced patients could have social,psychological, and financial issues. Moreover, takingself-medication or traditional medicine and seekingfirst advice from pharmacies were found to leadsignificantly to patient delay. Previous studies foundsimilar findings [14,15].


Results showed that the median of health system delay(HSD) in Suez Canal Area was 20 days. The WHO,2006, has reported 18 days HSD duration in Egypt [5],which indicates that it did not improve over the past 12years and even it is slightly longer. Shorter HSDduration was reported in Zimbabwe (2 days) [14],Angola (7 days) [15], Uganda (9 days) [16], India (9days) [17], and Nigeria (14 days) [18]. Although

Table 6 Duration of different types of delay among studiedpatients (N=183)

Types of delay Duration (days)

Patient delay

Mean±SD 35.5±41.5

Median 14

Range 1–180

Health-care system delay

Mean±SD 39.9±46.4

Median 20

Range 0–190

Total delay

Mean±SD 75.5±56.8

Median 65

Range 6–244

Table 8 Risk factors for total delay among studied patients (age, s

Risk factors Delayed (>median) (N=90) [n (%)] Not delaye

Age (years)

15–35@ 27 (40.2)

>35–45 38 (63.3)

>45–60 19 (50)

>60 6 (33.3)

Sex

Male@ 60 (48.4)

Female 30 (50.8)

City

Ismailia@ 52 (51.5)

Suez 19 (43.2)

Port Said 19 (50)

Residence

Urban@ 74(46.6)

Rural 16 (69.6)

CI, confidence interval; OR, odds ratio. 1χ2 test. 2Fisher’s exact test. @R

Table 7 Binary logistic regression analysis for predictors of total donset of symptoms, first health facility visited, sputum type, numb

Predictors Î

First health-seeking behavior 1.616

First health facility visited 0.584

Sputum type 0.683

Number of encounters −0.540

Satisfaction score 0.005

Stigma score 0.021

Constant −5.628

Model χ2=70.505

CI, confidence interval; OR, odds ratio. **Statistically significant at P va

Zimbabwe, Uganda, and Ethiopia are categorized bythe World Bank as lower-income countries, whileIndia, Nigeria, and Angola as low middle incomecountries (LMICs) as Egypt. The average HSD inthis study was 39.9 days which is longer than reportedby systematic review conducted by Sreeramareddy et al.[21] who found that the average HSD was 28.4 days inthe LMICs. This may be due to the fact that thehealth-care sector initially consulted was mainly privatesector in more than half of patients (55.2%) and 50% ofpatients who had HSD were diagnosed in privatepractice. These finding is similar to other studies[19,20]. Patients need to repeat same diagnosticsteps in TB clinics to confirm diagnosis which leadto longer HSD duration. Most patients in this studypreferred private practitioners and were advised byfriends or relatives as the main reasons for seekinginitial care from them compared with their lack ofconfidence in the quality of services in the public sector,where 62.80% of patients were not satisfied with publichealth-care services and 71.1% of patients with HSDhad inadequate level of satisfaction with health care.Negative chest radiography results or not performingchest radiograph were significant risk factors for HSD.

ex, city, and residence) (N=183)

d (≤ median) (N=93) [n (%)] OR and 95% CI P value

40 (59.7) 1 0.0331,*

22 (36.6) 2.56 (1.25–5.24)

19 (50) 1.48 (0.66–3.33)

12 (66.6) 0.74 (0.25–2.21)

64 (51.6) 0.91 (0.49–1.68) 0.7561

29 (49.2)

49 (48.5) 1 0.6511

25 (56.8) 1.39 (0.68–2.85)

19 (50) 1.06 (0.5–2.2)

85 (53.4) 0.49 (0.19–1.27) 0.06602

8 (30.4)

eference group. *Statistically significant at P value less than 0.05.

elay (>median) with covariates (health-seeking behavior ater of encounters, satisfaction score, and stigma score

P value OR (95% CI)

< 0.001** 5.035 (2.175–11.654)

0.231 1.793 (0.690–4.658)

< 0.001** 1.980 (1.506–2.604)

0.024 0.583 (3.65–0.930)

0.582 1.005 (0.988–1.021)

0.076 1.021 (0.998–1.045)

< 0.001** 0.004

< 0.001**

lue < 0.01.

Table 9 Risk factors for total delay among studied patients (education, occupation, income, and marital status) (N=183)

Risk factors Delayed (>median) (N=90) [n (%)] Not delayed (≤ median) (N=93) [n (%)] OR and 95% CI P value

Education

University@ 12 (60) 8 (40) 1 0.7721

Illiterate 22 (48.9) 23 (51.1) 1.57 (0.5–4.57)

Read and write 7 (50) 7 (50) 1.5 (0.38–5.95)

Primary/middle/high school 49 (47.1) 55 (52.9) 1.68 (0.64–4.46)

Employment

Employed@ 55 (51.40) 52(48.5) 1 0.7982

Student 5 (50) 5 (50) 1.06 (0.29–3.87)

Unemployed 30 (45.5) 36 (54.5) 1.27 (0.69–2.35)

Income

Savings@ 20 (60.6) 13 (39.4) 1 0.2711

Income=expenses 47 (44.8) 58 (55.2) 1.9 (0.86–4.21)

In debt 23 (51.1) 22 (48.9) 1.47 (0.59–3.66)

Marital status

Married@ 61 (54) 52 (46) 1 0.0782

Single 19 (37.3) 32 (62.7) 1.98 (1–3.89)

Divorced or separated 6 (75) 2 (25) 0.39 (0.08–2.02)

Widowed 4 (36.4) 7 (63.6) 2.05 (0.57–7.41)1χ2 test. 2Fisher’s exact test. @Reference group.


This arouses attention to the importance of trainingphysicians on following the national algorithm for TBdiagnosis and developing high suspicion index whennegative chest radiograph are found while symptomspersist.The study found that multiple health-careprovider visits prior to diagnosis was significantlyassociated with HSD. This relationship may beexplained by poor clinical suspicions by healthcareproviders, failure for requesting for appropriateinvestigations, or refer patients to TB clinics/chesthospitals for further investigations. The study foundthat the median of total delay duration was 65 days(range, 6–244 days). The total delay duration isunacceptably too long. Total delay duration is longerthan previously reported in Egypt by WHO [5], thatfound a median total delay duration of 44 days (range,0–364 days). The average total delay duration in thisstudy was 75.5 days which is longer than reported bySreeramareddy et al. [21]; they found that the averagetotal delay was 67.8 days in LMICwhichmean that thesituation in the Suez Canal Area is deteriorating andmore efforts are needed to focus on this problem.

Limitations of the studyThe study depended on the recall but we tried to avoidthis by reascertainment of delay time and onset oftreatment by checking the DOTS record whichincluded all dates and ecruiting new TB cases in theperiod of data collection. There were several defintionsfor cut off point for delay in previous studies but weused the most common one and which was used byWHO. The study included only patients who wereregistered and patients aged 18 years and over, it did

not include children because no enough data aboutthem were available. The study excluded retreatmentcases which may affect the period of delay.

Point of strength: the study included pulmonary andextrapulmonary TB cases.

ConclusionNearly half of patients (49.20%) had unacceptable totalTB delay. The main determinants of total delay in thediagnosis and management of TB were seekingpharmacies instead of visiting health-care providersafter onset of symptoms, not visiting initially chesthospital/TB clinics, seeking consultation fromdifferent specialties other than chest, negativesputum smear results, and more than two healthvisits before initial diagnosis.

AcknowledgementsThe authors acknowledge the Chest Hospitals ofIsmailia, Port Said, and Suez and all the participantsof the study.



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Effect of adding inhalation of sodium bicarbonate 8.4% to theusual treatment on smear-positive pulmonary tuberculosis:a prospective controlled studyMohammad K. El-Badrawya, Eman O. Arrama, Dina A. Abdallaa,Dina Al-Sagheerc, Alaa Zahranc, Mohammad A. AboElElad,Adel El-Badrawyb, Wagdy Amine

Background Pulmonary tuberculosis (TB) lesion is acidic,and changing this acidic pH may affect growth of TB bacilliand response to therapy. We aimed to assess the effect ofadjuvant inhalation of sodium bicarbonate (SB) 8.4% onclinical, radiological, and microbiological responses inpatients with sputum-positive drug-sensitive pulmonary TB.

Patients and methods One hundred and three patients withpulmonary TB completed the study, and they were classifiedinto two groups: group I included 55 patients who receivedstandard anti-TB regimen plus SB inhalation, and group IIincluded 48 patients who received anti-TB regimen only. Theresponses in both groups were evaluated clinically,microbiologically, and radiologically.

Results There was no statistically significant differencebetween both groups in baseline bacillary load, clinicalpicture, and radiology. Both groups improved clinically 1month after start of therapy. In group I only, there was astatistically significant improvement in chest radiograph after1 month (P<0.001). The median duration of smearconversion for group I was 3 weeks (1–8) compared with 9.5(2–17) in group II, with a statistically significant difference(P<0.001). Moreover, the median duration of cultureconversion for group I was 1 month (1–3) compared with 3


months (1–4) in group II, with a statistically significantdifference (P<0.001).

Conclusion Adjuvant inhalation of SB in smear-positivepulmonary TB to standard anti-TB drugs accelerates smearconversion, culture conversion, and clinical and radiologicalimprovement.Egypt J Bronchol 2019 13:531–538© 2019 Egyptian Journal of Bronchology


Keywords: drug sensitive, smear positive, sodium bicarbonate, tuberculosis

Departments of, aChest Medicine, bRadiology, Mansoura University

Hospital, cChest Medicine Department, Mansoura Chest Hospital, dMedical

Microbiology and Immunology Department, Mansoura University,

Mansoura, eNational TB Control Program, Cairo, Egypt

Correspondence to Dina A. Abdalla, MD, PhD, Chest Medicine

Department, Mansoura University Hospital, Elgomhoria Street, 35511

Mansoura, Dakahliya, Egypt. Tel: +20 100 997 6012; fax: +20 502 260 138;

e-mails: [email protected], [email protected]

resented-atIt was presented as poster discussion in ATS Conference 2017

on 21 May.

Received: 24 February 2019 Accepted: 27 August 2019






IntroductionIn 2014, tuberculosis (TB) infected nine millionspeople and killed 1.5 million [1]. Pulmonary TBinfection occurs via inhalation of droplet nuclei fromopen pulmonary TB cases [2].

Drug-sensitivepulmonaryTBis treatedwith the standardtreatment regimen. Infectious patients become lessinfectious within 10–14 days from the start of treatment,and most patients with sputum smear-positive TB willbecome smear negative within 2 months [3]. Poorcompliance to anti-TB treatment regimens is a majorbarrier to effective management of TB, increasingmultidrug resistance and treatment failure [4].

Mycobacterium tuberculosis (MTB) bacilli are protectedby the acidic media within human body as its classicallocation inside phagosomes of alveolar macrophagesand the centers of caseating granulomas found in rabbitmodels of TB [5,6].

Sodium bicarbonate (SB) is commonly used as a pHbuffering agent. It is used in patients with renal tubular

acidosis syndromes, diarrhea, acute lactic acidosis, andketoacidosis [7]. Bronchoalveolar lavage (BAL) withSB 8.4% affects staining of TB bacilli withZiehl–Neelsen stain and is inhibitory for TB bacilliin culture [8].

Rationale and aimApplication of SB to the infected lobe or lung with TBwill temporally change the acidic medium within theTB lesion into neutral or alkaline. This may beinhibitory to MTB bacilli and may affect thestructure of its cell wall as well as its response toanti-TB drugs. So, the aim of this study was toassess the effect of adjuvant inhalation of SB 8.4%to the standard anti-TB drugs on clinical, radiological,and microbiological responses in patients with sputum-positive drug-sensitive pulmonary TB.



[email protected]


Patients and methodsThis study included 111 patients with smear-positivedrug-sensitive pulmonary TB who were admitted to/ortreated as outpatients during the period from June 2014to June 2016. The study was carried out chronologicallyin two successive periods (Fig. 1):

(1)

Figu

Sche

Control group: between June 2014 and June 2015,52 consecutive patients with drug-sensitive smear-positive pulmonary TB were treated with thestandard anti-TB regimen according to WHO,alone [9]. However, four patients were excluded asthey died owing to pulmonary TB or associatedchronic obstructive pulmonary disease at the initialphase of treatment. So, the data of 48 patients onlycompleted the study.

(2)
Study group: between June 2015 to June 2016, 59consecutive patients with drug-sensitive smear-positive pulmonary TB received the standardanti-TB regimen according to WHO [9] inaddition to SB 8.4% inhalation in a dose of5ml/6 h using electric nebulizer for 1 monthafter sputum smear conversion into negativewith continuation of the anti-TB drugs till theend of its course. However, two patients were lostto follow-up and two patients died owing toassociated lymphoma, so 55 patients completedthe study.
Inclusion criteria
Patients with smear-positive drug-sensitivepulmonary TB with or without extrapulmonary TBwere included.
re 1

matic representation for the studied cases.

Exclusion criteriaThe following were the exclusion criteria:

(1)
Patients with isolated extrapulmonary TB or drug-resistant pulmonary TB.
(2)
Patients refused to be included in the study. (3) Patients with known contraindications of SB as
chronic heart failure, severe renal impairment,visible water retention, kidney problems causinga decreased amount of urine to be passed, or knownallergy to the compound.

Ethics approval had been obtained from InstitutionalResearch Board, Mansoura University, code numberMS/15.06.32, and the research unit in Ministry ofHealth, Egypt, code number 19-2015/8. It was alsoregistered on PACTR with unique identificationnumber for the registry PACTR201508001234317.All patients signed their written informed consentforms to be included in the study.

Both groups were subjected to the following:

(1)
Clinical evaluation with stress on smoking, drugabuse, respiratory symptoms, fever, and clinicalexamination, which were evaluated monthly tillthe end of treatment.
(2)
Laboratory investigations such as complete bloodcount, blood glucose, liver function tests, and serumcreatinine, which were also evaluated monthly.
(3)
Microbiological examination:(a) For both groups, threemorning sputum samples
on 3 successive days for Ziehl–Neelsen stainingthat was repeated weekly after the start of anti-TB treatment till smear conversion thenmonthly to the end of the recommended anti-TBregimen.Bacillary loadwasgradedaccordingto Lohmann et al. [10].

(b) TB culture on Lowenstein–Jensen medium atthe start of the study and then monthly to theend of treatment.

(c) GeneXpert was done before the start oftreatment, and the patients with rifampicinresistance were excluded.

Radiological investigations:
(4) (a) Chest radiograph: posteroanterior view was
repeated monthly. The disease extent wasclassified radiologically into either minimal,moderately advanced, or far advancedaccording to the National TuberculosisAssociation of the USA [11].

(b) CT chest was done at the start of treatmentand repeated if there was no response orreported complications.

Tabl

Vari

Dem

Ag

Sex

M

Fe

Wei

Diab

Hyp

Smo

Clin

C

Ex

Hem

Bl

Fr

W

N

N

W

An

MMR

Bac

CXR

MMR

Inhalation of SB in pulmonary TB El-Badrawy et al. 533

Treatment regimens for TB: For patients in both
(5) groups, they received standard first-line anti-TBregimen for 6 months according to WHO [9].
(6)
SB 8.4%: Patients in study group only (group I)received SB 8.4% inhalation using electricnebulizer in a dose of 5ml/6 h until sputumsmear conversion into negative.
Monitoring of the patients was done for the adverseeffects of the prescribed anti-TB drugs or the inhaledSB. Both groups were followed up for 1 year for the TBrelapse.

Outcomes of the treatment were cured, treatmentcompleted, treatment failure, or default according toWHO definitions [9].

Statistical analysisStatistical analysis of data was done using Exceland SPSS program version 16.0. The normality ofdata was first tested with one-sampleKolmogorov–Smirnov test. Categorical data werepresented as numbers (percentage). For data withnormal distribution, data were presented as mean±SD; independent-samples t-test was used tocompare the results between two groups. For datawithout normal distribution, data were presented asmedian (minimum–maximum); nonparametric two-related-samples test (Wilcoxon type) was used tocompare the results in the same group, and

e 1 Baseline characteristics of both groups

ables Study group

ographics

e 40.72±1

ale 47 (85

male 8 (14

ght (kg) (mean±SD) 63.77±1

etes mellitus 12 (21

ertension 2 (3.

king 23 (41

ical presentation and MMRC

ough 55 (10

pectoration 55 (10

optysis

ood tinged 9 (16

ank 10 (18

heezes 5 (9.

ight fever 55 (10

ight sweating 55 (10

eight loss 55 (10

orexia 55 (10

C [median (minimum–maximum)] 3 (1–

illary load [median (minimum–maximum)] 2 (1–

score [median (minimum–maximum)] 3 (1–

C, modified medical research council. aFisher exact test.

Mann–Whitney U-test was used to compare theresults between two groups. χ2-Test was used tocompare paired proportions (or Fisher exact testwhen needed). Statistical significance was defined asP value less than 0.05.

ResultsDemographic data and comorbidities of both groupsare illustrated in Table 1. With the exception of sex,there was no statistically significant difference betweenboth groups.

Table 1 shows also the baseline bacillary load. Themedian baseline bacillary load was higher in studygroup [2 (1–5)] compared with the control group [1(1–3)], but this difference was statistically insignificant(P=0.282).

Before the start of treatment, there was no statisticallysignificant difference in clinical symptoms betweenboth groups. Both groups showed significantimprovement in symptoms without statisticallysignificant difference between them (Table 1).

Regarding CXR, there was no statistically significantdifference between both groups at the start of therapyor after 1 month (P=0.437 and 0.888, respectively).However, within the group, only group I showed

(n=55) Control group (n=48) P value

5.06 43.81±13.85 0.285

.5) 48 (100) 0.007a

.5)

2.44 63.53±11.31 0.924

.8) 9 (18.8) 0.665

6) 3 (6.2) 0.664a

.8) 30 (62.5) 0.055

0) 48 (100) –

0) 48 (100) –

.4) 4 (8.3) 0.394

.2) 12 (25)

1) 6 (12.5) 0.576

0) 48 (100) –

0) 48 (100) –

0) 46 (95.8) 0.215a

0) 46 (95.8) 0.078a

4) 3 (2–4) 0.983

5) 1 (1–3) 0.282

3) 3 (1–3) 0.437

Table 2 Follow-up characteristics of both groups’ clinical presentation and radiology

Variables Study group (n=55) Control group (n=48) P value

Clinical presentation and MMRC

Cough 1 (1.8) 0 –

Expectoration 0 0 –

Hemoptysis

Blood tinged 0 3 (6.2) 0.092*

Frank – –

Wheezes 2 (3.6) 3 (6.2) 0.660

Night fever 0 0 –

Night sweating 0 0 –

Weight loss 0 1 (2.1) 0.463

Anorexia 0 2 (4.2) 0.077*

MMRC [median (minimum–maximum)] 2 (2–2) 2 (0–3) 0.097

CXR score [median (minimum–maximum)] 1 (0–3) 1 (1–2) 0.888

MMRC, modified medical research council. *Means Fisher exact test.

Figure 2

Time for smear conversion in weeks [the median duration of smear conversion for group I was 3 weeks (1–8) compared with 9.5 weeks (2–17) ingroup II (P<0.001)].


statistically significant improvement in CXR after 1month of treatment (P<0.001; Tables 1 and 2).

The median duration for smear conversion in weeks instudy group was 3 (1–8) and 9.5 (2–17) in controlgroup, with a statistically significant difference betweenboth groups (P<0.001; Fig. 2).

Moreover, the median duration of culture conversionfor group I was 1 month (1–3) compared with 3months (1–4) in group II, with statistically

significant difference between both groups(P<0.001;Fig. 3).

Regarding the adverse effects of SB inhalation, allpatients after SB inhalation developed mild coughand salty taste for 5–10min after SB inhalationsession. No serious adverse effects were reported inall patients.

On follow-up of all patients for one year after the endof the study, no relapse was reported in all cases.

Figure 3

Time for culture conversion in months [the median duration of culture conversion for group I was 1 month (1–3) compared with 3 months (1–4) ingroup II (P<0.001)].


DiscussionIn healthy individuals, airway surface liquid lines theconducting airways of the lungs, and it has an acidicpH (about 6.6) [12,13]. Local pH is one of theeffective host innate immunity lines againstmicroorganisms. Pulmonary infections lead to localacidic pH, which promotes growth of bacteria andincreases bacterial resistance [14]. It also causesinactivation of antibiotics and impairs function ofalveolar macrophages [15,16].

Pulmonary TB is transmitted through inhalation ofdroplet nuclei of infected patients and spread throughdroplet nuclei which carry infectious bacilli. So, theyare considered to be the most significant source ofinfection for TB [17]. Agarwal and Chauhan [18]reported that one untreated patient with infectiousTB is likely to infect 10–15 persons annually.

Sputum conversion usually occurs in ∼80–90% ofpatients within 2–3 months of treatment [19].Factors that lead to delay in smear and cultureconversion include high initial bacillary load, TBcavities, diabetes mellitus, old age, multidrugresistance-TB, initial treatment with less than fouranti-TB drugs, and non-rifampicin-based treatmentregimens [15,20].

Exhaled breath condensate pH in patients with activepulmonary TB was significantly lower than control.The authors speculated that airway epithelium acidifiesin response to various insults [21]. Therefore, theytheorized that changing the acidic pH of airwayfluid secretions into alkaline or neutral pH maychange the acidic medium in the TB lesions, whichmay disturb the structure and/or multiplication ofMTB and enhance its response to anti-TBtreatment. The target was to augment the effect ofanti-TB drugs to convert the sputum-positive patientsinto smear negative in shorter periods and decrease theinflammatory response in the lungs that may be usefulin prevention of drug resistance and abortion of TBtransmission to other susceptible persons.

We selected for the study patients with sputum-positive drug-sensitive pulmonary TB because theyrepresent a worldwide health problem as the chanceof transmission of TB to the contacts is high, and tillnow, there are no new effective drugs that can shortenthe duration of TB treatment, and the incidence of TBresistance to any newly introduced drug is high.

Most MTB bacilli are rapidly metabolizing and killedwithin the first 8 weeks of treatment; however, thereare semidormant bacilli that require longer durations of

Figure 5

A 34-year-old male patient with pulmonary tuberculosis from group II. (a) CXR before starting treatment showing bilateral alveolar infiltrates withcavitation (far advanced). (b) CXR after one month of treatment showing no improvement.

Figure 4

A 45-years-old male patient with pulmonary tuberculosis from group I. (a) CXR before starting treatment showing alveolar infiltrates in all rightlung zones (far advanced). (b) CXR after one month of treatment showing marked improvement.


treatment and the intracellular bacilli that require drugsthat act in the acidic medium [3]. If treatment is notcontinued for a long enough duration, the survivingbacteria may cause the patient to become ill andinfectious again, potentially with drug-resistantdisease [22].

We selected SB 8.4% in this study owing to its safety tohuman and its high alkaline pH. It is commonly used asa pH buffering agent. Chronic bicarbonatereplacement is used safely for patients with renaltubular acidosis syndromes or diarrhea. In patientswith acute lactic acidosis and ketoacidosis, however,bicarbonate therapy must be individualized [7]. It hasalso an inhibitory effect to the respiratory pathogensincluding TB in-vitro as reported in a study conductedby Abdalla et al. [8], as they found a statisticallysignificant inhibitory effect of BAL with SB on TB

cultures when compared with BAL with saline(P=0.031).

We used nebulizer for delivery of SB as it isnoninvasive, can be repeated many times per dayat home or hospital, and can transform the fluidSB into inhalable particles. The given dose was5ml, because with the use of nebulizer for deliveryof medication, part of drug remains inside, calleddead volume and ranges from 1 to 3ml. So, anebulizer fill volume of 4–6ml is recommendedaccording to Hess et al. [23]. All patients in groupI received SB inhalation for 1 month after sputumconversion.

Regarding symptoms, there was no significantdifference between groups in respiratory symptomsor toxemic manifestations (Table 1).


Only patients in group I showed a significantimprovement in follow-up CXR (P<0.001) 1 monthafter start of treatment (Table 2 and Figs 1 and 2).

The median duration of smear conversion for group Iwas 3 weeks (1–8) compared with 9.5 weeks (2–17) ingroup II (P<0.001; Fig. 2), and the median duration ofculture conversion for group I was 1 month (1–3)compared with 3 months (1–4) in group II, withstatistically significant difference between bothgroups (P<0.001; Fig. 3).

Iivanainen et al. [24] studied the occurrence ofMTB inaerobic brook sediment. They found that the culturablecounts of MTB correlated negatively with water andsediment pH and with alkalinity of water and thatacidity increases the count of MTB. Moreover,Parashar et al. [25] in their research aboutneutralization of the gastric aspirate with SB dividedthe gastric aspirate in two containers, and one of themwas neutralized with SB with a concentration of 1%.After doing smear and culture examinations for theaspirate, there were no differences in smear positivity.However, the culture result was significantly lower inthe neutralized samples [16.3% (38/232)] than in thenon-neutralized samples [21.5% (50/232); P=0.023].

MTB bacilli can live in acidic media insidemacrophages. The estimated pH of the macrophagecompartment, in which MTB resides, ranges from pH6.2 to 4.5, depending on the activation state of themacrophage [26–28]. Optimal growth of MTB inenriched liquid medium was observed at a slightlyacidic pH, between 5.8 and 6.7 [29].

Jackett et al. [30] reported that a variety of strains ofMTB are resistant to killing at a pH of 4.5 inphosphate-citrate buffer. The bacilli are also able tomaintain a near-neutral intrabacterial pH when placedin phosphate-citrate buffer at pH 4.5, indicating thatthey are able to counter the entry of protons [31].Moreover, other different studies had addressedsurvival of MTB in acidic media [32,33]. In theearly 1900s, Metchnikoff [34] speculated that thewaxy MTB cell wall serves as an important guardagainst acid stress present in phagocytes. MTB has alipid-rich cell wall that consists of a typical bilayeredplasma membrane followed by a layer ofpeptidoglycan-arabinogalactan covalently linked tomycolic acids. In 2008, researchers demonstrated theexistence of an additional outer lipid bilayersurrounding MTB [35,36]. This complex cellenvelope acts as a major permeability barrier forantibacterial effectors, including protons. Indeed,

studies examining the physiology of mycobacteria atlow pH reported that the cell wall plays a critical role inresistance to acid [29].

From the aforementioned data, we speculated thatchanging this acidic media in which MTB live maylead to changes in the wall of TB bacilli leading to itsdestruction, which may explain the shorter time ofsmear conversion in study group compared withcontrol group.

Regarding radiological changes 1 month after startinganti-TB drugs in this study, in group I, there was asignificant improvement in disease extent in chestradiograph regarding parenchymal infiltration andcavitation. This may be explained by rapid smearconversion and regression of the inflammatoryresponse to TB bacilli. Mesquita et al. [37] studiedthe relation between bacillary load, inflammatorymarkers, and radiological findings before treatmentand after 2 months. They found that individualspresenting with pretreatment AFB smear less than 2+ and CRP levels less than 4.7mg/l were 10 times morelikely to exhibit radiographic improvement of lungdisease compared with those with higher values ofthose parameters (P=0.002). In group II, there wereno significant radiological changes in the diseaseextent. By increasing the local bronchial pH, SBweakens the bonds between the side chains of themucus molecule, which decreases mucus viscosityand elasticity [38].

Finally, this study has some limitations. First, we didnot address changes in airway fluid pH after inhalationof SB. Second, we did not examine structural changesof the MTB bacilli that may explain the results.Moreover, titration of the most suitable dose of SB,frequency of its use, and the most suitable mode ofadministration were not evaluated. Application of SBinhalation all over the course of anti-TB drugs may bemore effective.

ConclusionInhalation of SB 8.4% in drug-sensitive pulmonary TBas adjunctive therapy to standard anti-TB drugsshortens the duration required for smear and TBculture conversion and achieves rapid clinical andradiologic improvements.

AcknowledgementsConceptualization: Mohammad K. El-Badrawy.Methodology: Mohammad K. El-Badrawy, DinaAl-Sagheer, Alaa Zahran, Mohammad A.


AboElEla, and Adel El-Badrawy. Software: Dina A.Abdalla. Validation: Wagdy Amin. Formal analysis:Dina A. Abdalla. Investigation: Dina A. Abdalla. Datacuration: Dina Al-Sagheer. Original draft preparation:Dina A. Abdalla. Review and editing: Mohammad K.El-Badrawy and Eman O. Arram. Approval of thefinal manuscript: all authors.


Conflicts if interestThere are no conflicts of interest.

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Role of lactate dehydrogenase and other biomarkers inpredicting prognosis of community-acquired pneumoniaRasha M. Hendya, Mona A. Elawadyb, Heba M. Abd EL Kareemc

Background An increase in serum lactate dehydrogenase(LDH) activity is commonly taken to support the presumptivediagnosis of some lung diseases and a variety ofextrapulmonary disorders, but the role of LDH as an earlyprognostic factor in detecting outcome in patients withcommunity acquired pneumonia (CAP) was not well studiedbefore.

Aim To assess the prognostic value of LDH and otherlaboratorymarkers [C-reactive protein (CRP), serumalbumin,and neutrophil percentage] in patients with CAP.

Patients and methods We compared levels of LDH andother laboratory markers (CRP, serum albumin, andneutrophil percentage) with each other and with CURB65score, length of hospital stay, and worse outcomes (ICUadmission, mechanical ventilation, and mortality) in 62 (33males and 29 females) patients with CAP who were admittedto Pulmonology Department, Benha University Hospital,between March 2016 and March 2017 after ethical committeeapproval.


Results Most of the patients with worse outcomes showedsignificant high levels of LDH, CRP, albumin, and neutrophilpercentage early on admission.

Conclusion LDH was a highly sensitive biomarker for earlyprediction of worse outcomes in patients with CAP.Egypt J Bronchol 2019 13:539–544© 2019 Egyptian Journal of Bronchology


Keywords: community-acquired pneumonia, lactate dehydrogenase,prognosis

Departments of, aChest Diseases and Tuberculosis, bPublic Health,

Community Medicine, cBiochemistry, Benha Faculty of Medicine, Benha

University, Banha, Egypt

Correspondence to Rasha M. Hendy, Dectorate and lecturer of chest

Disease in Benha University, Chest Department, Benha University Hospital,

Banha, 13518, Egypt. Mob: 01028340748;


Received 6 March 2019 Accepted 16 July 2019






IntroductionCommunity-acquired pneumonia (CAP) is a verycommon cause for hospital admission, withpotentially life-threatening complications, which mayoccur particularly in the elderly and those withunderlying health problems. These complicationsmay include, empyema, lung abscess, acuterespiratory distress syndrome, sepsis, and worseningof underlying health problems. Despite advances indiagnosis and treatment, CAP remains a common,potentially fatal disease associated with significantmorbidity, mortality, and health care expenditure[1]. Overall annual incidence is ∼1600/100 000 inthe USA and 1100/100 000 in Europe, with ∼250of 100 000 patients requiring hospitalization [2].Although mortality associated with CAP is below5% among outpatients, it can be as high as 10%among inpatients and can exceed 30% amongpatients admitted to the ICU [3].

In patients with CAP requiring ICU admission,mortality may involve more than half of thesepatients compared with 4–18% mortality in wardadmission and only less than 1% who do not needhospitalization [4]. Identifying patients at high risk ofmortality could substantially improve their treatmentand management [5]. The stratification of the severityand prognosis of CAP is a vital feature as it is one of themost common causes of mortality among otherinfectious diseases in the developed countries [6]. To

improve the outcomes in the management of CAP,there has recently been a significant attention to the useof evidence-based scoring systems and biologicalmarkers to predict treatment failure, justify hospitaladmission in either acute medical settings or ICU, andalso to classify the disease severity, which will help inpredicting the mortality rate [7].

AimTo detect role of lactate dehydrogenase (LDH), C-reactive protein (CRP), serum albumin, and neutrophilpercentage in predicting prognosis of CAP.

Patients and methodsThis cross-sectional study was conducted on 62 adultpatients who were admitted in PulmonologyDepartment, Benha University Hospital, fromMarch 2016 to March 2017, diagnosed as havingCAP based on clinical and radiological evidence. Atfirst, consents from patients with diagnosis of CAP toparticipate in the study were taken. Patients havingother conditions that can cause increase in LDH like



Table 1 Characteristic features of the studied group

N=62

Age [mean±SD (range)] 53.42±12.57 (29–86)

Sex [n (%)]

Male 33 (53.2)

Female 29 (46.8)

Smoking

Yes 33 (53.2)

No 29 (46.8)

Comorbidities [9]

DM 8 (40.0)

HTN 11 (55.0)

Asthma 4 (20.0)

LVH 1 (5.0)

PHTN 2 (10.0)

MS 2 (10.0)

Comorbidities


myocardial infarction, liver disease, lymphoma, HIV,pancreatitis, and hemolytic anemia were excluded.

A bundle of measures were performed within the first3 h of admission, including serum LAH (done by semi-automated analyzer photometer 4010; RIELE, RobertRiele and CO, Berlin, Germany), CRP (done by latexagglutination; CRP-Latex cromatest, Biotrax Testing,Biotrax testing Laboratory INC, Cheektowaga,Newyork, USA), serum albumin, kidney and liverfunctions (done by Biosystems A15 auto-analyzer,Barcelona, Spain), and CBC with differential (doneby automated hematology system; Sysmex XE-5000-Analyzer; Sysmex America Inc., SYSMEX, Ramsey,MN, USA). CURB65 score (which is a well validatedseverity score for predicting severity and mortality frompneumonia) was assessed for all studied patientsaccording to the following criteria: confusion, bloodurea nitrogen (BUN), respiratory rate, blood pressure,and age of at least 65 years old [7]. Initiation ofappropriate empirical broad-spectrum intravenousantibiotics was done. Treatment with intravenouscrystalloids was given if indicated.

Patients with the following criteria were excluded fromthis study: tuberculosis, bronchiectasis, HIV infection,and solid organ or hematological malignancies.Patients admitted to hospital in the previous 14 daysor with nursing home residents were also excluded.

The following criteria were considered as indicators forworse outcome in our studied group of patients:admission in the ICU, mechanical ventilation (MV),and death. All data were collected after ethicalcommittee approval.

DM and HTN 4 (6.5)

Asthma 3 (4.8)

DM 3 (4.8)

HTN and LVH 1 (1.6)

DM and asthma 1 (1.6)

HTN 6 (9.7)

PHTN and MS 2 (3.2)

No 42 (67.7)

Hospital stay [median (IQR)] 4 (3–5)

ICU admission [n (%)]

Yes 10 (16.1)

No 52 (83.9)

MV

Yes 3 (4.8)

No 59 (95.2)

Statistical analysis [8]The collected data tabulated and analyzed using SPSSversion 16 software (SPSS for Windows, version 16.0.;SPSSInc.,Chicago, Illinois,USA).Datawerepresentedusing χ2-test to analyze them. Correlation coefficientwas used to find relation between LDH and othervariables. Quantitative data were presented as a mean±SD. Students t-test andMann–Whitney test were usedto compare means of different groups of parametric andnonparametric data, assuming normality at Pmore than0.05. P value less than 0.05 is significant, P value morethan 0.05 is nonsignificant, and P value up to 0.001 ishighly significant.

Mortality

Yes 2 (3.2)

No 60 (96.8)

DM, diabetes mellitus; HTN, hypertension; IQR, interquartilerange; LVH, left ventricular hypertrophy; MS, mitral stenosis; MV,mechanical ventilation; PHTN, pulmonary hypertension.

ResultsThis study was carried out on 62 (33 males and 29females) patients with CAP who were admitted to

Pulmonology Department, Benha UniversityHospital, between March 2016 to March 2017.Their ages were 29–86 years (mean 53.4±12.57), and33 patients were smokers. More than half of thestudied group experienced hypertension (55.0%),40.0% had DM, and 20.0% had asthma. Ten(16.1%) patients were admitted to ICU owing tosevere condition; three of them were MV and twodied. However, the other 52 (83.9%) patients weretreated in the ordinary ward. The median hospital staywas 4 days, and interquartile range (IQR) ranged from3 to 5 days (Table 1).

Serum LDH ranged from 266 to 1424U/l, with mean±SD of 598.1±286.79U/l. Serum CRP in patients hadIQR from 22.75 to 202.25mg/l, with a median value of53.5mg/l. Neutrophils% ranged from 49.4 to 84.5%,with a mean±SD value of 63.23±15.99%, whereas

Table 5 Validity of neutrophil%, lactate dehydrogenase, C-reactive protein and serum albumin in predicting worseoutcomes (mortality, ICU admission and mechanicalventilation)

LDH and CAP prognosis Hendy et al. 541

albumin ranged from 1.8 to 6.0mg/dl, with a mean±SD value of 3.64±0.67 (Table 2).

Table 3 illustrated significant direct correlationbetween neutrophils LDH and CRP with each otherand with CURB65 and hospital stay, whereas theyshowed significant negative correlation with serumalbumin. Moreover, serum albumin showedsignificant negative correlation with CURB65 andhospital stay.

Table 4 illustrates a significant relation betweenneutrophil% (P=0.003), serum LDH (P=0.001),CRP (P=0.008), and low albumin (P=0.001)] withworse outcomes (ICU admission, MV, and mortality).

ROC analysis showed that cutoff value of neutrophil%in predicting worse outcomes (ICU admission, MV,andmortality) was 66.4%, with sensitivity of 76.9% andspecificity of 63.3%. Cutoff value of LDH in predictingthe worse outcomes was 511U/l, with sensitivity of

Table 4 Relation between laboratory markers (neutrophils, lactateoutcomes (ICU admission, mortality, and mechanical ventilation)

Neutrophil LDH

Mean±SD

Studentt-test

Pvalue

Mean±SD

Studentt-test

Pvalue

Worse outcomes

Yes(13)

73.56±5.75

3.13 0.003** 862.08±337.0

Z=3.31 0.001**

No(49)

63.5±11.17

528.06±228.59

CRP, C-reactive protein; LDH, lactate dehydrogenase.*Significant.**High

Table 3 Correlation between neutrophils, lactate dehydrogenase, CCURB 56 and hospital stay

Neutrophils LDH

R P value r P va

Neutrophils 0.533 <0.00

LDH 0.533 <0.001**

CRP 0.518 <0.001** 0.711 <0.00

Albumin −0.549 <0.001** −0.52 <0.00

CURB 56 0.606 <0.001** 0.859 <0.00

Hospital stay 0.467 <0.001** 0.855 <0.00

CRP, C-reactive protein; LDH, lactate dehydrogenase.*Significant.**High

Table 2 Mean values of laboratory markers in the studiedpatients

Laboratory markers Values

LDH [mean±SD (range)] 598.1±286.79 (266–1424)

CRP [median (IQR)] 53.5 (22.75–202.25)

Albumin [mean±SD (range)] 3.64±0.67 (1.8–6.0)

Neutrophils% [mean±SD (range)] 65.61±11.03 (49.4–84.5)

CRP, C-reactive protein; IQR, interquartile range; LDH, lactatedehydrogenase.

76.9% and specificity of 65.3%. Cutoff value of CRP inpredicting worse outcomes was 30mg/l, withsensitivity of 76.9% and specificity of 36.7%, and thecutoff value of serum albumin in predicting worseoutcomes was 3.55mg/dl, with sensitivity of 76.9%and specificity of 51% (Table 5).

Logistic regression showed that LDH was the mostclosely associated variable with poor outcomes (ICUadmission, MV, and mortality) (Table 6).

DiscussionLDH has been studied in many pulmonary andnonpulmonary diseases, and high level of thismarker was used as presumptive diagnosis of manypulmonary diseases, for example, tuberculosis,

dehydrogenase, C-reactive protein and albumin) and worse

CRP Albumin

Mean±SD

Mann–Whitneytest

Pvalue

Mean±SD

Studentt-test

Pvalue

213.95±143.07

Z=2.64 0.008** 3.08±0.61

3.65 0.001**

83.06±87.16

3.78±0.61

ly significant.

-reactive protein, and serum albumin with each other and with

CRP Albumin

lue r P value r P value

1** 0.518 <0.001** −0.299 0.018*

0.711 <0.001** −0.524 <0.001**

1** −0.449 <0.001**

1** −0.449 <0.001**

1** 0.824 <0.001** −0.549 <0.001**

1** 0.632 <0.001** −0.475 <0.001**

ly significant.

Neutrophil% LDH CRP Albumin

AUC 0.755 0.801 0.739 0.792

Cutoff point 66.4 511 30 3.55

Sensitivity 76.9 76.9 76.9 76.9

Specificity 63.3 65.3 36.7 51.0

PPV 35.7 37.0 24.4 29.4

NPV 92.1 91.4 85.7 89.3

Accuracy 66.1 67.7 45.2 56.5

AUC, area under the curve; CRP, C-reactive protein; LDH, lactatedehydrogenase; NPV, negative predictive value; PPV, positivepredictive value.

Table 6 Logestic regression to predict worse outcome

Exp (B) P value 95% CI for Exp(B)

Lower Upper

LDH 1.906 0.007 1.002 2.010

CRP 0.074 0.101 0.003 1.663

Hypoalbuminemia 1.026 0.439 0.367 10.119

Neutrophil% 0.195 0.169 0.019 2.004

CI, confidence interval of odd ratio; CRP, C-reactive protein; Exp(B), exponentiation of the B coefficient, which is an odd ratio;LDH,lactate dehydrogenase.


Pneumocystis carinii pneumonia, and CAP [9], and alsostudied in interstitial lung diseases, acute respiratorydistress, and obstructive lung diseases [10].

LDH is a cytoplasmic enzyme expressed in nearly alltypes of cells of the body. It is released into blood whenthe cells experience injury or death caused bydehydration, ischemia, bacterial toxins, drugs, andchemical poisonings. Because it is expressed invarious organs/tissues by high concentration, theleakage of LDH from even a small scale of injuredtissue can result in a significantly elevated serum level.It has been used as an indicator of cellular injuryinduced by various etiologies [11].

Reliable prediction of patients with CAP maysubstantially improve patient management, timelyanti-infectious therapy, and nurse intervention [12].

In a study done by Lim et al. [7], mean age was 64years, with male percentage constituted 51.5%. Inanother study done by Brogly et al. [11], males andfemales were equal. Bertsias et al. [13] found thatsmokers represented 41% of CAP cases. Smoking isan established risk factor for CAP, probably owing toits adverse effects on respiratory epithelium and theclearance of bacteria from the respiratory tract [14].Calle et al. [15] showed that mean age was 60.5 years,with 51.7 of them being males. Moreover, Yang et al.[16] found that median age was 61 years, with malepercentage being 66.35%. Berstials et al. [13] foundthat 42% of patients with CAP had multimorbidity,with most frequent chronic conditions being heartdiseases followed by COPD and type 2 DM.Comorbid conditions associated with an increasedrisk of CAP, including diabetes mellitus andimpaired immune function, have previously beenidentified as risk factors for CAP [14].

In a study done by Ewig et al. [15], which included 92patients with CAP, the mean age of patients was 51±19years (range from 15–87 years); males constituted67.4% of them, and 45% of the patients had at least

one underlying chronic disease as a risk factor foracquiring CAP [15]. Liu et al. [17] found that meanage of their studied patients with CAP was 64±19years, males were 59.6%, and overall 37.6% of thepatients were accompanied by one or more co-existing diseases (e.g. COPD and CHF).

Liu et al. [17] found in their study, median length ofhospital stay was 10 days (IQR 7–15) and mortality was8.2%. Ewig et al. [15] found that among 92 patientswith CAP, 34.8% were admitted in ICU, 14.1% wereMV, andmortality was 22%. Severity of disease and co-morbidity may be responsible for this wide range ofreported mortality.

This study showed that serum LDH had a significantpositive correlation with CURB65 score and length ofhospital stay. It also significantly increased among ICUadmitted, died, andMVpatients. This agreeswithEwiget al. [15], who reported that increased serum LDHvalues were associated with increased mortality in 92patients with CAP. They showed that higher serumLDH level indicated more severe complications andworse prognosis [15]. Moreover, previous studies doneby Hoffman and Rogers [18], Schultze et al. [19], Quistand Hill [9], and Padilla et al. [20] have demonstratedthat the elevatedLDHin serum,bronchoalveolar lavage,and pleural fluid can help determine the extent of lungtissue damage and inflammation, such as pulmonaryembolism, P. carinii pneumonia, tuberculosis,bacterial pneumonia, and influenza A.

Liu et al. [17] found that the expandedCURB-65 score,which extends independent risk factors to eight variables(including serum LDH, albumin and platelets) inassessing CAP severity, significantly improvesidentifying high-risk patients than CURB-65 andother assessment tool, through decreasing the relativeweight of age and blood pressure and eliminating the useof imaging and comorbid illnesses in the calculation.They concluded that expanded CURB-65 is a relativelysimpler and more effective marker in assessing theseverity of hospitalized patients with CAP.

This study showed that albumin had a significantnegative correlation with CURB65 and length ofhospital stay, and it was significantly decreased amongICU admitted, died patients, and patients subjected toMV. Hypoalbuminemia, which can be caused bymalnutrition, liver cirrhosis, or infection process,contributes to an increased mortality in hospitalizedpatients [21]. There is a close correlation between lowserum albumin concentration and mortality in patientswith CAP [22]. A study done by Liu et al. [17] showed

LDH and CAP prognosis Hendy et al. 543

that serum albumin less than 3.5 g/l was significantlyassociated with 30-day mortality in patients with CAP.Lee et al. [22] concluded that low serum albumin wassignificantly different between survivors andnonsurvivors and was associated with 28-day mortalityin hospitalized patient with CAP.

This study showed that CRP had a significant positivecorrelation with CURB65 score and length of hospitalstay, and it was increased significantly among diedpatients and patients subjected to MV. Similarly,Lee et al. [22] found that elevated CRP wassignificantly different between survivors andnonsurvivors, and that increased CRP was associatedwith 28-day mortality. Li et al. [23] concluded thatCRP of at least four times the mean or median for thepatient center was an independent predictive risk factorthat correlated with adverse outcomes in elderlypatients. Hohenthal et al. [24] found that high CRPlevels more than 100mg/l on day 4 after the admissionwere significantly associated with complications(P<0.01). There was a trend for an associationbetween the level of CRP on admission and thetime to reach clinical stability (P<0.01). Theyconcluded that CRP may be valuable for revealingthe development of complications in CAP. It mayalso be useful to assess the disease severity.

This study found that serum neutrophils increasedsignificantly among patients admitted to ICU, and itshowed significant increase among died patients andpatients subjected to MV. In agreement with thisstudy, Jose et al. found that increased neutrophil cellpercentage was associated with substantial increase inthe risk of mortality. They found that unlike most ofthe predictive markers used in clinical practice, basicblood count could provide parameters with thepotential of high predictive capacity for mortality inpatients with CAP, which are easy to handle and costeffective [25]. Li et al. [23] found that higherneutrophil percentage was an independent predictiverisk factor that correlated with adverse outcomes.

Ewig et al. [15] showed that serum levels of LDH (withcutoff value≥260 μ/l, with P=0.0154), heart rate (withcutoff value≥90 beats/min and P=0.03), and systolicblood pressure (with cutoff value≤80 and P=0.142)were the variables most closely associated with fataloutcome in multivariate analysis. They found adiscriminant role of these three variables to achievehigh predictive value [15]. Lim et al. [7] found thatserum albumin less than 30 g/dl (P=0.001) and age ofat least 65 years (P=0.003) were both independablyassociated with 30-day mortality. Liu and colleagues

found that elevated serum LDH level (>230 μ/l),thrombocytopenia (platelet count<105/ml), andhypoalbuminemia (albumin level<3.5 g/dl) wereindependent risk factors for death on multivariateanalysis [16].

Limitations of this study included relatively small sizeof the studied group, and blood samples were collectedas soon as patients were admitted, so there werevariations in the time of collecting them.

ConclusionLDH, albumin, CRP, and neutrophils% are importantserum markers in determining CAP prognosis. Theyshould be performed on admission to predict the courseand probable complications in patients with CAP.

AcknowledgementsRasha Hendy wrote the paper. Mona-Elawady didstatistics of the paper. Heba Abdelkareem collectedsamples and performed laboratory investigations.

The manuscript has been read and approved by all theauthors. Each author believes that the manuscriptrepresents honest work.

The manuscript has been read and approved by all theauthors, and the requirements for authorship have beenmet, and each author believes that the manuscriptrepresents honest work.



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Study of the prevalence and pattern of fungal pneumonias inrespiratory intensive care unitsMona M. Ahmeda, Ayman A. Farghalyb, Riham H. Raafata,Waleed M. Abd Elsattarc

Background Fungal pneumonia is an infectious process inthe lung caused by one or more endemic or opportunisticfungi. Fungal infection occurs following the inhalation ofspores, after the inhalation of conidia, or by the reactivation ofa latent infection. Hematogenous dissemination frequentlyoccurs, especially in an immunocompromised host.

Aim of the work To assess the prevalence of fungalpneumonias in a group of respiratory ICUs and identify theirpattern.

Patients and methods This study was carried out on 60patients who were admitted in respiratory ICUs of differenthospitals: Ain Shams University and Military Hospitals fromMarch 2018 till February 2019 to assess the prevalence offungal chest infection in that group of patients andfurthermore to identify their pattern. All patients weresubjected to the following: history, clinical examination,radiology (plain chest radiograph and computed tomographyof the chest), routine laboratory investigations and finallymycological analysis including direct microscopicexamination and culture examination of the collectedrespiratory samples.

Results The mean age of all patients was 55.43 years.Regarding sex of the patients, the majority (76.67%) ofpatients were men, while 23.33% were women. Forty(66.67%) patients out of 60 patients with respiratory diseases


had been culture positive for fungus and 20 (33.33%) patientshad been culture-negative. The major fungal speciesencountered in this study were Candida species in 23 (57.5%)cases followed by Aspergillus species in 17 (42.5%) cases.Candida albicanswas isolated in 23.33% of patients followedby Aspergillus nonfumigatus (18.33%) then Candidanonalbicans (15%), and finally Aspergillus fumigatus (10%).

Conclusion From the current study, we can conclude thatpulmonary fungal infection appears to be an importantproblem in patients with respiratory diseases especiallypatients who are admitted in respiratory ICUs regardless oftheir age or sex.Egypt J Bronchol 2019 13:545–550



Keywords: fungal pneumonia, prevalence, respiratory intensive care units

aDepartment of Chest Diseases, Faculty of Medicine, Ain Shams University,

Cairo, Egypt, bDepartment of Chest Diseases, Military Medical Academy,

Cairo, Egypt, cResident, Kobri El kobba Military Hospital, Cairo, Egypt

Correspondence to Waleed M. Abd Elsattar, Quweisna, Menoufia, MBBS,

Egypt. Tel: +20 106 977 4573; 2902769;








IntroductionFungal pneumonia is an infectious process inthe lungs caused by one or more endemic oropportunistic fungi. Fungal infection occursfollowing the inhalation of spores, inhalation ofconidia, or by the reactivation of a latent infection.Hematogenous dissemination frequently occurs,especially in an immunocompromised host [1].

Endemic fungal pathogens (e.g.Histoplasma capsulatum,Coccidioides immitis, Blastomyces dermatitidis,Paracoccidioides brasiliensis, Sporothrix schenckii,Cryptococcus neoformans) cause infection in healthyhosts and in immunocompromised persons [2].

Opportunistic fungal organisms (e.g. Candida species,Aspergillus species, Mucor species) tend to causepneumonia in patients with congenital or acquireddefects in host immune defenses [2].

Among yeasts and molds, Candida and Aspergillusspecies are the most frequent nosocomial fungalpathogens included in the critical care setting [3].

The diagnosis of fungal pneumonias is difficult toprove and is often made on a presumptive basis. Itrelies on a combination of clinical, radiological, andmicrobiological factors [4].

Aim of the workThe aim of this study was to assess the prevalence offungal pneumonias in a group of respiratory ICUs andto identify their pattern.

Patients and methodsPatientsThis prospective cross-sectional analytical study wasconducted on 60 patients who were admitted inrespiratory ICUs of different hospitals: Ain ShamsUniversity and Military Hospitals (after written




consents from patients or their family) from March2018 till February 2019; ethics committee approval wastaken.

Inclusion criteria

(1)
Any patients with history of preexisting lungdisease and immunosuppression fromcorticosteroids or other underlying conditions(e.g. diabetes, malnutrition and liver cirrhosis).
(2)
One of the following symptoms of lowerrespiratory tract infection (newly developedsputum or secretions, dyspnea, hemoptysis, orpleuritic chest pain).
(3)
Refractory fever after at least 3 days of appropriateantibiotics.
(4)
Development of new pulmonary infiltrates onchest radiograph.
(5)
Any of the following new infiltrates on computedtomography (CT) imaging: halo sign, air-crescentsign, or cavity within area of consolidation.
Exclusion criteria

(1)
Patients who received systemic antifungal therapywithin 3 days prior to sample collection.
(2)
Patients who refused to participate in the study.
All patients were subjected to the following: fullhistory taking, clinical examination, laboratoryinvestigations, radiology (chest radiograph and CTchest) and mycological analysis of the collectedsamples as follows:

Collection of clinical samples

(1)
Collection of sputum samples from the patients:sputum samples was collected in 39 patients byinstructing them to cough and expectorate about5–10ml in a sterile container usually early in themorning.
(2)

Table 1 Specimen analyzed and final diagnosis

Specimen analyzed and final diagnosis N=60 [n (%)]

Specimen

Bronchoalveolar lavage (BAL): BAL samples werecollected only from 21 patients who weremechanically ventilated patients with the help ofa white light flexible bronchoscope attached to alight source and digital camera.

BAL 21 (35)

Sputum 39 (65)

Result

Aspergillus fumigates 6 (10)

Aspergillus nonfumigates 11 (18.33)

Candida albicans 14 (23.33)

Candida nonalbicans 9 (15)

Nil 20 (33.33)

BAL, bronchoalveolar lavage.

Mycological analysis of clinical samplesDirect microscopic examinationDirect smears from sputum samples and BAL sampleswere prepared and examined by direct microscopyusing Lactophenol Cotton Blue stain. The slideswere examined under a light microscope on power×40 and ×100, magnification.

Culturing of samplesThe samples were streaked on Sabouraud’s glucose agarmedium and examined.

Statistical analysisThe data collected were tabulated and statisticallyanalyzed using the following methods:

(1)
Descriptive statistics: continuous data wasrepresented as mean and SD, whilenonnumerical data as number and percentage.
(2)
Analytic statistics:(a) One-way analysis of variance test for rank
(Kruskal–Wallis): this test was used tocompare between more than two groups ofnumerical origin.

(b) Person’s χ2 test: the test was used to test theassociation between categorical variables.

statistical tests were two sided, P considered
Allsignificant if less than 0.05.
Ethics committee approvalStudy was done after ethics committee approval andwritten consents were taken from the patients.

ResultsThe examined specimens had been collected frompatients by two methods: sputum (65%) and BAL(35%), and the results showed that the prevalence offungal infection was 66.67%. The most commonfungus present was Candida albicans (23.33%) andAspergillus nonfumigates (18.33%) (Table 1).

There was an insignificant statistical differencebetween the median age of patients infected byCandida or Aspergillus in comparison with otherswithout fungal infection, P=0.4. Also, there wereinsignificant association of specific sex type orsmoking activity with fungal infection, P=0.3 and0.5, respectively (Table 2).

Table 2 Demographic characteristics in correlation withfungal infection

Variables Without fungalinfection(N=20)

With Candidainfection(N=23)

WithAspergillusinfection(N=17)

P

Age(years)

56.5(49.25–58.75)

59 (44–66) 56 (52–62) 0.4a

Sex(male)

13 (65) 19 (82.61) 46 (76.67) 0.3b

Smoking(yes)

12 (60) 8 (47.06) 30 (50) 0.5b

MV 7 (35) 12 (52.17) 2 (11.76) 0.02

Continues; none normally distributed data represented as medianand IQR, categorical data as n (%). MV, mechanical ventilation.aKruskal–Wallis test. bχ2 test. P value considered significant if lessthan 0.05.

Table 3 Comorbidity impact in patients with fungal infections

Comorbidities Withoutfungalinfection(N=20)

WithCandidainfection(N=23)


P

DM 6 (30) 9 (39.13) 1 (5.88) 0.03a

Renaldisease

1 (5) 0 (0) 2 (11.76) b

Liver disease 3 (15) 2 (8.7) 2 (11.76) 0.8a

Malignancy 1 (5) 1 (4.35) 1 (5.88) b

CVD 3 (15) 2 (8.7) 1 (5.88) 0.6a

Blooddisease

0 (0) 2 (8.7) 0 (0) b

aχ2 test. bNo test of significant. CVD, cardiovascular disease; DM,diabetes mellitus. P value considered significant if less than 0.05.

Table 4 Impact of medication used with fungal infection

Medication risks Withoutfungalinfection(N=20)



P

Antibiotics 18 (90) 18(78.26)

14 (82.35) 0.5a

Steroids 6 (30) 16(69.57)

3 (17.65) 0.002a

Immunosuppressivedrugs

1 (5) 2 (8.7) 0 (0) b

aχ2 test. bNo test of significance. P value considered significant if lessthan 0.05.

Table 5 Length of stay in ICU and mechanical ventilation usein correlation with fungus infection

Variables Withoutfungal

infection(N=20)



P

LOS (days) 7 (5–8) 5 (4–8) 7 (4.5–8) 0.3a

MV 7 (35) 12 (52.17) 2 (11.76) 0.02b

Neutropenia 2 (10) 1 (4.35) 0 (0) c

Continues; non-normally distributed data represented as medianand IQR, categorical data as n (%). aKruskal–Wallis test. bχ2 test.LOS, length of stay; MV, mechanical ventilation. P valueconsidered significant if less than 0.05. cNo test of significance.

Table 6 Effect of the underling chest disease on fungalinfection

Variables Withoutfungalinfection(N=20)



P

Asthma 2 (10) 6 (26.09) 4 (23.53)

Bronchiectasis 1 (5) 3 (13.04) 1 (5.88)

COPD 12 (60) 10 (43.48) 7 (41.18)

ILD 2 (10) 1 (4.35) 2 (11.76) *

Pneumonia 2 (10) 2 (8.7) 2 (11.76)

TB 1 (5) 1 (4.35) 1 (5.88)

Categorical data as n (%). *No test of significance. COPD, chronicobstructive pulmonary diseases; ILD, interstitial lung disease; TB,tuberculosis. P value considered significant if less than 0.05.

Table 7 Clinical presentation of patients

Clinical presentation N=60

Cough 60 (100)

Expectoration 56 (93.33)

Dyspnea 60 (100)

Chest pain 13 (21.67)

Hemoptysis 9 (15)

Wheeze 39 (65)

Constitutional symptoms 50 (83.33)

Fungal pneumonias in respiratory ICUs Mansour et al. 547

There was significant association of diabetes mellitus(DM) with Candida infection, P=0.03, while the othercomorbidity showed an insignificant association withCandida or Aspergillus infection (Table 3).

Also, therewas significant association of steroid usewithCandida infection (P=0.002), while neither antibioticsnor immunosuppressive drugs were significantlyassociated with fungal infection, P=0.5 (Table 4).

Our results showed insignificant statistical differencebetween patients with fungal infection (Candida and

Aspergillus) as regards the median length of stay inICU, P=0.3, while mechanical ventilation (MV) usewas significantly associated with Candida infection(P=0.02) (Table 5).

Statistically there was an insignificant association ofspecific underling chest disease with fungalinfection either by Candida or Aspergillus(Table 6).

All of the patients presented by cough and dyspnea(100%), 93.33% of the patients presented byexpectoration, and 83.33% with constitutionalsymptoms. Finally, the less frequent symptoms werehemoptysis and chest pain (15 and 21.67%,respectively) (Table 7).

Table 8 Radiological aspect of fungal infection

Radiologicalfinding

Without fungalinfection (N=20)

With Candidainfection (N=23)

With Aspergillus infection (N=17) P

Radiography

Bronchiectaticchanges

1 (5) 3 (13.04) Any patients with a history of preexisting lung disease andimmunosuppression from corticosteroids or other underlying conditions

(e.g. diabetes, malnutrition, and liver cirrhosis). 0 (0)

Cavities 1 (5) 1 (4.35) 1 (5.88) *

Consolidation 2 (10) 2 (8.7) 2 (11.76)

Hyperinflation 12 (60) 11 (47.83) 7 (41.18)

Normal 2 (10) 4 (17.39) 7 (41.18)

Reticulonodular 2 (10) 2 (8.7) 0 (0)

CT

Air crescent 1 (5) 2 (8.7) 0 (0)

Bronchiectaticchanges

1 (5) 3 (13.04) 1 (5.88)

Cavities 1 (5) 1 (4.35) 1 (5.88) *

Consolidation 2 (10) 2 (8.7) 2 (11.76)

Halo sign 1 (5) 1 (4.35) 1 (5.88)

Hyperinflation 12 (60) 9 (39.13) 7 (41.18)

Normal 0 (0) 3 (13.04) 3 (17.65)

Reticulonodular 2 (10) 2 (8.7) 2 (11.76)

Categorical data as n (%). *No test of significance. bχ2 test. P value considered significant if less than 0.05.


Hyperinflation was the most common radiologicalfeatures in both radiograph and CT in the threegroups of patients (Table 8).

DiscussionThe incidence of pulmonary mycosis has increased overthe past few decades due to the wide use of broad-spectrum antibiotics, immunosuppressive andchemotherapy agents as well as the increasedincidence of respiratory diseases, including chronicobstructive pulmonary disease (COPD), lung cancer,and tuberculosis [5].

This cross-sectional study was done to assessthe prevalence of fungal chest infection inpatients at different RICUs and to identifytheir pattern.

The demographic features of the studied 60patients demonstrated that the majority ofpatients were men presenting 76.67% whilewomen were 23.33%. The mean age of theseparticipants was 55 years. Furthermore, 50% ofpatients in our study had positive smokinghistory. There were insignificant statisticaldifference between the median age of patientsinfected by Candida or Aspergillus incomparison with others without fungal infection,P=0.4. Also, there were insignificant associationsof specific sex type or smoking activity with fungalinfection, P=0.3 and 0.5, respectively.

The current study showed that 66.67% (40 out of 60patients) of the patients with respiratory diseaseswere culture positive for fungal agents and 20(33.33%) patients only were negative. The majorfungal species encountered in this study wereCandida species in 23 (57.5%) cases, followed byAspergillus species in 17 (42.5%) cases. C. albicanswas isolated in 23.33% of patients, followed byAspergillus nonfumigatus (18.33%), then Candidanonalbicans (15%), and finally Aspergillus fumigatus(10%).

Our results came in accordance with the Farghaly andcolleagues study in which the major fungal speciesencountered in 114 (46.3%) cases were Candidaspecies followed by Aspergillus species in 82 (33.3%)cases, pencillium species in 10 (4.1%) cases, andfusarium spp. in seven (2.8%) cases, while combinedCandida and Aspergillus infection was present in eight(3.3%) cases. C. albicans was the most predominantversus Candida nonalbicans. However, Aspergillusnonfumigatus percentage exceeded that of A.fumigatus [6].

Again, our results came in accordance with anotherstudy of fungal culture of 60 patients with respiratorydiseases, by Biswas et al. [7] which reported Aspergillussp. in 13 patients (including Aspergillus flavus in sixpatients, A. fumigatus in four patients, and Aspergillusniger in three patients) and Candida sp. in 14 (C.albicans and C. tropicalis being isolated from 12 andtwo patients, respectively). One patient, with chronic

Fungal pneumonias in respiratory ICUs Mansour et al. 549

interstitial lung disease, showed growth of Cryptococcusneoformans.

The most common comorbidity of respiratory ICUpatients at risk of pulmonary fungal infections wereuncontrolled DM (26.67%) and it was also found to bethe most common comorbid disease associated withhigh fungal recovery rates; 10 (62.5%) out of 16diabetic patients had positive fungal culture. DMwas followed by liver disease and cardiovasculardisease (11.67 and 10%, respectively); finally, theleast common comorbidity was blood disease (3.33%).

There was a significant association of DM withCandida infection, P=0.03, while the othercomorbidities including cardiovascular, liver, renaldiseases, and malignancies showed insignificantassociation with either Candida or Aspergillusinfection.

Regarding the drugs used 83.33% of patients studiedwere on antibiotic therapy, 41.67% on steroid therapy,and only 5% of patients used immunosuppressivedrugs. About 35% of RICU patients were on MVsupport with a mean length of stay in ICU of about6 days. Finally, only 5% of patients were suffering fromneutropenia.

Statistically, this study demonstrated significantassociation of steroid use with Candida infection(P=0.002), while neither antibiotics norimmunosuppressive drugs were significantlyassociated with pulmonary fungal infection, P=0.5.Also, in mechanically ventilated patients, there was asignificant association between MV and Candidainfection, P=0.02. Furthermore, there wereinsignificant statistical differences between patientswith fungal infection (Candida and Aspergillus) asregards the median length of stay in ICU, P=0.3.

Our study results came in agreement with the results ofBiswas et al. [7] who screened 60 patients with chronicrespiratory diseases for the presence of pulmonary fungalinfections which reported that among the differentcomorbid conditions and risk factors, the commonassociations included DM, severe anemia (hemoglobin<9%), use of steroids, alcoholism, and chronic carrierstate of HBV. Moreover, this study also reported thatDM was found to be the most common risk factorassociated with isolation of Candida albicans [7].

Our study results also came in accordance with theresults of Schnabel et al. [8] which revealed that the useof steroids and prior antibiotic use have been linked to

increased colonization with Candida species at variousanatomical sites.

Our study revealed that 29 (48.33%) patients of a total60 patients were COPD, 17 (42.5% out of the positivecases) of them had positive culture for fungal infection,10 (43.48%) of themwith Candida infection, and seven(41.18%) with Aspergillus infection. Also, there were20% with bronchial asthma; 10 (25% out of the positivecases) of them had positive culture for fungus, six(26.09%) of them with Candida infection, and four(23.53%) with Aspergillus infection. Moreover, thestudy group included bronchiectasis in 8.33%,pneumonia in 10%, interstitial lung diseases in8.33%, and tuberculosis in 5% of patients.

On comparing culture positivity for fungal infectionamong different pulmonary diseases we found thatculture-positivity rate was statistically significantlyhigher in asthma and bronchiectasis than otherpulmonary diseases (P<0.05).

The Egyptian study of Farghaly and colleagues showedthat 177 (45.5%) of a total of 389 patients were COPDand114 (46.3% out of the positive cases) of them hadpositive culture for fungal infection. Also, there were38 (9.8%) patients with bronchial asthma; 26 (10.6%out of the positive cases) of them had positive culturefor fungus. Moreover, the study group included 36(9.3%) patients, 35 (9.0%) patients and 34 (8.7%)patients with tuberculosis, suppurative lung diseasesand pneumonia, respectively. On comparing culturepositivity for fungal infection among differentpleuropulmonary diseases, culture-positivity rate wasstatistically significantly higher in COPD patientscompared with pneumonia (P<0.05). Also, culture-positive rate was statistically significantly higher inbronchial asthma patients in comparison withpneumonia patients (P<0.05). However, the culture-positive rate was statistically significantly lower inpneumonia patients compared with patients withpleural diseases (P<0.05). On the other hand, therewas no statistically significant difference betweenCOPD in comparison with asthma, tuberculosis, orsuppurative lung diseases (P>0.05) [6].

All of patients presented by cough and dyspnea (100%),93.33% of patients presented by expectoration and83.33% with constitutional symptoms. Finally, theless frequent symptoms were hemoptysis and chestpain (15 and 21.67%, respectively).

Luo and colleagues in their study on pulmonarymycosis among 68 patients in China showed that


the main symptoms of the patients were as follows:cough in 51 (75.0%) cases; expectoration in 38(55.9%) cases, with blood in sputum in 18 cases,white phlegm in 12 cases, and purulent sputum ineight cases; hemoptysis in 25 (36.8%) cases; fever in20 (29.4%) cases; chest pain and shortness of breathin five cases; headache, nausea, and vomiting in threecases. Only six (11.1%) patients were asymptomatic[9].The present study revealed that the most frequentradiographic manifestations in patients at risk ofpulmonary fungal infection in chest radiograph andCT, respectively, were hyperinflation (50 and46.67%). Other radiological presentations wereconsolidation (10 and 10%), bronchiectasis (6.67and 8.33%), reticulonodular infiltrations (6.67 and10%), and cavities (5 and 5%). Moreover, CT chestrevealed halo sign (5%) and air crescent (5%) inpatients with Aspergillus infections. Interestingly,normal chest radiograph and CT chest were foundin some patients at risk of pulmonary fungalinfections (21.67 and 10%). Our study reportedthat there was no significant association betweenany radiological presentation and Candida orAspergillus infection.

This results came in accordance with those of Biswaset al. [7] in which none of the radiological patternsshowed significantly higher association with fungalrecovery (P=0.7) and normal radiography wasassociated with fungal growth in five of 10 patients[7].

Luo et al. [9] showed that radiographic characteristicswere masses or nodule lesions in 52 (76.5%) cases,patchy lesions in 10 (14.7%) cases, cavity formation in15 (22.0%) cases, and diffuse miliary nodules in onecase.

Limitations of the study were the small number of casescollected, thus our findings may not be representativeof the entire population of Egypt, relatively shortperiod during which patients selected from differentrespiratory ICUs and finally making nonmolecular

diagnosis only, thus recommending moreinformative studies in the future.

ConclusionFrom the current study, we can conclude thatpulmonary fungal infection appears to be animportant problem in patients with respiratorydiseases especially in patients who are admitted tothe medical respiratory ICU regardless of their ageor sex.

Among different respiratory diseases, COPD is themost common disease among patients at risk ofpulmonary fungal infection. DM is the mostcommon comorbid disease associated with highfungal recovery rates especially Candida.



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3 Dimopoulos G, Frantzeskaki F, Poulakou G, Armaganidis A. Invasiveaspergillosis in the intensive care unit. Ann N Y Acad Sci 2012; 1272:31–39.

4 Limper AH. The changing spectrum of fungal infections in pulmonary andcritical care practice: clinical approach to diagnosis. Proc Am Thorac Soc2010; 7:163–168.

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7 Biswas D, Agarwal S, Sindhwani G, Rawat J. Fungal colonization in patientswith chronic respiratory diseases from Himalayan region of India. Ann ClinMicrobiol Antimicrob 2010; 9:28.

8 Schnabel RM, Linssen CF, Guion N, van Mook WN, Dennis CB. Candidapneumonia in intensive care units. PMC 2014; 1:ofu026.

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The impact of admission blood glucose level on patients withcommunity-acquired pneumoniaTamer M. Ali, Hala M. Salem, Dina R. Sultan

Background Pneumonia is one of the most commondiseases with a high hospitalization rate. Many studies havesuggested that there is a correlation between pre-existingdiabetes and the alterations in serum glucose levels inpatients with community-acquired pneumonia (CAP) and highdeath rate.

Aim To study the impact of admission blood glucose level onpatients’ outcomes with CAP.

Patients and methods Sixty (30 nondiabetic and 30 diabeticpatients) consecutive hospitalized adult patients with CAPwere recruited over a 1-year period. Data on patients’outcomes including duration of hospital stay, duration ofantibiotic treatment, increase in oxygen requirements,increase in antibiotics coverage, ICU admission, mechanicalventilation, and in-hospital mortality were collected.

Results Admission blood glucose level was elevated indiabetic patients (the mean plasma glucose level was 258.86±116.15mg/dl in diabetics and 151.13±51.23mg/dl innondiabetics). There were statistically significant increases inthe duration of hospital stay (7.633±3.567 nondiabetic vs.11.267±4.291 diabetic patients in days), duration of antibiotictreatment (7.633±3.567 nondiabetic vs. 11.267±4.291diabetic patients in days), increase in O2 requirements (33.33


vs.70%), increase in antibiotics coverage (16.67 vs. 63.33%),and ICU admission (30 vs. 63.33%) in the diabetic group oncomparing nondiabetic versus diabetic patients with CAP.Also, the previously mentioned outcomes increasedsignificantly with increasing blood glucose levels among theentire study population.

Conclusion On admission, CAP patients with increasedblood glucose level, either diabetic or nondiabetic, areexpected to have poor outcomes.Egypt J Bronchol 2019 13:551–555



Keywords: antibiotics, blood glucose, community-acquired pneumonia,hospital stay, hyperglycemia

Department of Chest Diseases, Faculty of Medicine, Ain Shams University,

Cairo, Egypt

Correspondence to Dina R. Sultan, MD, Lecturer of Chest Medicine in

Faculty of Medicine in Ain Shams University, 38 Abbasia Next Nour

Mosque, Cairo, 11517, Egypt. Tel: +20 110 208 4652;


Received 14 July 2019 Accepted 10 September 2019






IntroductionCommunity-acquired pneumonia (CAP) is a commondisease that leads to significant morbidity andmortality, and its annual incidence varies from 5 to11/1000 population; 20% of these patients requirehospitalization, which places a significant economicburden on the society [1].

Hyperglycemia has a major influence on the function ofthe immune system; it combines in-vitro withingredients of the innate immune system [2] andalso combines with the adaptive immune system,causing direct inhibition of T-lymphocyte,immunoglobulin, and complement functions [3].

Hyperglycemia can occur because of diabetes mellitusor stress. Stress induced by hyperglycemia resultedfrom increase in catecholamine, growth hormone,glucagon, proinflammatory cytokines [4].

Hyperglycemia on admission is considered an absoluterisk factor for higher complication rates and highermortality in patients with CAP with or withoutdiabetes [5]. Most studies have found that there isan association between hyperglycemia and long-termmortality after each CAP episode [6]. Thus, it is crucial

to study the impact of admission blood glucose levelson outcomes in patients with CAP in our hospital.

Patients and methodsStudy design and settingThis prospective study was carried out at the ChestDepartment, Ain Shams University Hospitals fromJanuary 2018 to January 2019 after approval by theEthics Committee of the Chest Department and onobtaining verbal informed consent from all patients.

Study sampleSixty consecutive adult patients (>18 years) admittedfor at least 24 h in the ward during the study with CAP(symptoms of cough, purulent expectoration, fever, anew abnormality on chest radiograph with no recenthospital admission) were recruited by conveniencesampling. The diagnosis of CAP was made on thebasis of ATS/IDSA guidelines [7]; the patients were




divided according to information collected aboutdiabetes into 30 nondiabetic patients and 30 diabeticpatients (diabetes was defined as a doctor’s diagnosisthat had been made before the current pneumoniaepisode, and verified from the patient files, or if thepatient was receiving medication for diabetes).

Patients with aspiration pneumonia, those who hadalready received antibiotic treatment before admission,patients with recent tuberculosis infection, patientsunder treatment with corticosteroids (≥20mgprednisolone-equivalent/day>14 days),immunocompromised patients (HIV infection,receiving chemotherapy in the last month beforeinclusion in the study), pregnant women, andpatients admitted in the ward less than 24 h wereexcluded.

Study toolAll patients selected in the study were subjected to a fullassessment of medical history (age, sex, smoking status,comorbidities, and history of recent hospital admissionand history of drug intake), complete clinicalexamination, radiological investigations [chest x ray(CXR) posterior anterior (PA) view and computedtomography (CT) chest whenever needed],laboratory investigations, arterial blood gases onroom air, and bacteriological investigations (sputumculture, sputum Ziehl–Neelsen); the CURB-65 scoreon admission was used to assess CAP severity as statedpreviously in the literature [8].

Blood glucose level on admission was measured in bothgroups using an Accu-chek blood glucose meter model(Accu-chek Active) manufactured by Roche DiabetesCare (Basel, Switzerland) (after washing our hands, thetest strip was inserted into the device and we placed the

Table 1 Demographic data and comorbidities in both nondiabetic a

Nondiabetic

Age (mean±SD) (years) 60.300±18.859

Sex [n (%)]

Male 22 (73.33)

Female 8 (26.67)

Pack/year (mean±SD) 32.737±15.726

Comorbidities [n (%)]

Pulmonary disease 6 (20)

Cardiac disease 6 (20)

Liver disease 3 (10)

Malignancy 2 (6.67)

Chronic renal disease 2 (6.67)

Neurological disease 3 (10)

device on the side of the patient’s fingertip to obtain theblood sample; then touching and holding the edge ofthe test strip to the drop of blood then the resultappeared) [9].

Data related to hospitalization were collected, includedduration of hospital stay, duration of antibiotic coverage,and complications such as increase in oxygen (O2)requirement, increase antibiotic coverage (defined asincrease the dose and/or the number of antibioticsthan that prescribed on admission), sepsis, admissionto the ICU with or without mechanical ventilation, andin-hospital mortality.

Statistical analysisQuantitative variables were presented as mean and SD,comparison between two means was performed usingthe independent t-test. Categorical data werepresented as count and percentages. Comparisonbetween proportions was performed using the χ2-test. The analysis of variance (ANOVA) test wasused to compare more than two groups as regardquantitative variables. P values equal to or less than0.05 were considered statistically significant. SPSSversion 20 (IBM, American MultinationalInformation Technology Company, Armonk, NewYork) was used for data analysis.

ResultsThe majority of the studied population included men;73.33 and 53.33% of the patients in the nondiabeticand the diabetic group, respectively, were men, with amean age 60 years among nondiabetics and 62 yearsamong diabetics. There were no statistically significantdifferences between nondiabetic and diabetic patientsin the sociodemographic data and comorbidities inTable 1.

nd diabetic patients

Groups t-Test or χ2

Diabetic P value

62.067±12.868 0.673

16 (53.33) 0.108

14 (46.67)

30.000±19.640 0.655

11 (36.67) 0.152

9 (30) 0.371

0 0.076

3 (10) 0.640

5 (16.67) 0.228

5 (16.67) 0.448

Blood glucose and CAP Ali et al. 553

In Table 2, the mean plasma glucose level on admissionwas elevated in diabetics in comparison withnondiabetics as the mean blood glucose level was258.867±116.154mg/dl in diabetics and 151.133±51.235mg/dl in nondiabetics. Also, on classifyingblood glucose, it was found that the majority ofnondiabetics (76.6%) had blood glucose levelsranging from 110 to 199mg/dl, whereas in morethan half the diabetic patients, the blood glucoselevel was above 250mg/dl; thus, there was astatistically significant difference betweennondiabetic and diabetic patients.

There was no statistically significant differencebetween both groups in the different parameters ofthe CURB-65 score (Table 3).

There was no statistically significant differencebetween nondiabetics and diabetics in the arterialblood gas parameters [potential hydrogen (PH),PaO2: partial pressure of oxygen in arterial blood

Table 2 Glucose levels at admission in nondiabetic and diabetic pa

Gro

Nondiabetic

Glucose level at admission

Mean±SD 151.133±51.235

<110mg/dl 1 (3.33)

110–199mg/dl 23 (76.67)

200–250mg/dl 4 (13.33)

>250mg/dl 2 (6.67)

**P<0.01, highly significant.

Table 3 CURB-65 score in nondiabetic and diabetic patients

Nondiabetic

Confusion 6 (20.00)

Urea>7mmol/l 13 (43.33)

RR≥30 9 (30.00)

SBP<90 mmHg or DBP≤60 mmHg 0

Age≥65 years 14 (46.67)

DBP, diastolic blood pressure; RR, respiratory rate; SBP, systolic blood

Table 4 Outcomes in nondiabetic and diabetic patients with comm

Non

Duration of hospital stay (mean±SD) (days) 7.63

Duration of antibiotic treatment (mean±SD) (days) 7.63

Increase O2 requirements 10

Increase antibiotics coverage 5 (

Sepsis 1

ICU admission 9

Mechanical ventilation 7 (

In-hospital mortality 1

O2, oxygen. *P≤0.05, significant. **P<0.01, highly significant.

and oxygen saturation; P values were 0.434, 0.878,and 0.896, respectively].

Therewere statistically significant increases in thedurationof hospital stay (7.633±3.567 nondiabetic vs. 11.267±4.291 diabetic patients in days), duration of antibiotictreatment (7.633±3.567 nondiabetic vs. 11.267±4.291diabetic patients in days), increase in O2 requirements(33.33 vs. 70%), increase antibiotics coverage (16.67 vs.63.33%), and ICU admission (30 vs. 63.33%) among thediabetic group on comparing nondiabetic versus diabeticpatients with CAP. However, there was no statisticallysignificant difference between nondiabetic and diabeticpatients with CAP in terms of sepsis, mechanicalventilation, and in-hospital mortality (Table 4).

On comparing between the different admission bloodglucose levels in the entire study population (nondiabeticand diabetic patients) and outcomes of CAP patients,there was a statistically significant increase in theduration of hospital stay (13.211±3.614 days),

tients

ups t-Test or χ2

Diabetic P value

258.867±116.154 <0.001**

3 (10.00) <0.001**

8 (26.67)

2 (6.67)

17 (56.67)

Groups χ2

Diabetic P value

7 (23.33) 0.754

14 (46.67) 0.795

14 (46.67) 0.184

0 0.00

16 (53.33) 0.606

pressure.

unity-acquired pneumonia

Groups t-Test or χ2

diabetic Diabetic P value

3±3.567 11.267±4.291 0.001*

3±3.567 11.267±4.291 0.001*

(33.33) 21 (70) 0.004*

16.67) 19 (63.33) <0.001**

(3.33) 1 (3.33) 1.000

(30) 19 (63.33) 0.010*

23.33) 12 (40) 0.165

(3.33) 2 (6.67) 0.554

Table 5 Comparison between different levels of admission blood glucose and community-acquired pneumonia patients’outcomes

Different levels of admission blood glucose ANOVA orχ2

<110mg/dl(N=4)

110–199mg/dl(N=31)

200–250mg/dl(N=6)

>250mg/dl(N=19)

P value

Duration of hospital stay (mean±SD) (days) 6.613±2.076 6.613±2.076 10.667±4.967 13.211±3.614 <0.001**

Duration of antibiotic treatment (mean±SD)(days)

6.613±2.076 6.613±2.076 10.667±4.967 13.211±3.614 <0.001**

Increase in O2 requirements 0 11 (35.48) 3 (50.00) 17 (89.47) <0.001**

Increaseantibiotics coverage 0 3 (9.68) 4 (66.67) 17 (89.47) <0.001**

Sepsis 0 1 (3.23) 0 1 (5.26) 0.904

ICU admission 0 10 (32.25) 3 (50.00) 15 (78.95) 0.001*

Mechanical ventilation 0 6 (19.35) 3 (50.00) 10 (52.63) 0. 945

In-hospital mortality 0 0 1 (16.67) 2 (10.53) 0.188

ANOVA, analysis of variance; O2, oxygen. *P≤0.05, significant. **P<0.01, highly significant.


duration of antibiotic treatment (13.211±3.614 days),increase in O2 requirements, increase antibioticcoverage, and ICU admission with an increase inCAP patients’ blood glucose levels as shown in Table 5.

DiscussionDiabetes mellitus is a highly prevalent chronicmetabolic disorder that is present in ∼5–10% ofthe elderly population, and it may be considered arisk factor for pneumonia [10]. Several investigationshave found that CAP leads to an increase in themortality rate among patients with diabetes morethan that in patients without diabetes [11],whereas others have shown no association [12].Thus, some studies have found that hyperglycemiaon admission predicts poor outcomes in CAP [13],whereas others have not [14].

On the basis of the results of this study, onadmission, CAP patients with increased bloodglucose levels, either diabetic or nondiabetic, haveadverse outcomes (Tables 4 and 5) even afteradjusting for the other associated factors that mayaffect the prognosis for CAP such as age, sex,smoking habit, comorbidities (Table 1), andCURB-65 (Table 3).

Jensen et al. [15] studied the impact of admission bloodglucose levels on the outcome of CAP in a hospital intheir retrospective study, which was carried out on1320 patients, and they found that that duration ofhospital stay and antibiotic duration for diabeticpatients were longer (6 days in diabetic patients and5 days in nondiabetic patients), which was consistentwith this study, as the duration of hospital stay andantibiotic duration for diabetic patients was 11 versus 7days in nondiabetic patients.

Jensen et al. [15] also reported that the clinical coursewas more severe in nondiabetics and they explainedthat because diabetic patients were more adapted tohave blood glucose levels elevated so they had lesssevere clinical course than nondiabetic patients, butthis was not in agreement with this study and otherprevious studies [13,16,17], which found that theincrease in admission blood glucose was associatedwith a severe clinical course in diabetics.

McAlister et al. [5] also found that hyperglycemia onadmission in patients with CAP mostly predictsadverse outcomes as for each 1-mmol/l increase inthe blood glucose level, there was a 3% increase inthe risk of in-hospital complications, and this findingwas in agreement with ours.

Hyperglycemia on admission has been associated withincreased mortality in various studies [5,13,17], butthis was not in agreement with this study as the in-hospital mortality was insignificant (only two diabeticpatients and one nondiabetic patient), and this can beexplained by the small sample size and assessment ofin-hospital mortality only without follow-up of thepatients after discharge.

This study has its limitations. The sample was toosmall and hence we could not detect the difference inthe mortality rate between diabetic and nondiabeticpatients and we did not follow-up the patients afterdischarge to detect long-term mortality as we focusedon in-hospital mortality.

ConclusionOn admission, CAP patients with increased bloodglucose levels, either diabetic or nondiabetic, areexpected to have poor outcomes.

Blood glucose and CAP Ali et al. 555



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Prepolysomnography evaluation can predict obstructive sleepapnea and is correlated to its severityHend M. Esmaeela, Hamdy A. Mohammadienb, Abd-Elbaset M. Salehb,Fatma H. Mohameda

Background Obstructive sleep apnea (OSA) is increasinglyidentified as a disease with major health consequences. Thelimited availability of the gold standard diagnostic test,polysomnography (PSG), mandates careful clinicalevaluation of suspected patients. This can allow better patientselection for referral for confirmatory diagnostic test.

Objective The study aimed at identifying the importance ofpre-PSG evaluation in prediction of obstructive sleep apneaand its relation to disease severity.

Patients and methods A total of 170 patients were included.Detailed demographic characteristics, anthropometricmeasures, and comorbid conditions were recorded.Correlation to PSG results was done, and multivariateanalysis was used to identify predictors of disease.

ResultsOSAwas diagnosed in 58.8% of our studied patients.The patients with OSA and notably the severe subgroup wereof older age, predominantly male, and current or ex-smoker.Mean BMI was highest in the patients with severe OSA (41.99±8.92) and same for mean neck and waist circumference,both were significantly higher in patients with severe OSA. In


multivariate logistic regression analysis, significant predictivefactors for OSA were older age, male sex, beingnonemployed, having hypertension, and larger tonsillar size.

Conclusion Patient demographics, anthropometriccharacteristics, and presence of comorbid conditions such ashypertension are strong predictors of having OSA and justifyreferral for diagnostic sleep study.Egypt J Bronchol 2019 13:556–562© 2019 Egyptian Journal of Bronchology


Keywords: anthropometric measures, hypertension, obstructive sleep apnea

aDepartment of Chest Diseases, Sohag Faculty of Medicine, Sohag

University, Sohag, bDepartment of Chest Diseases, Faculty of Medicine,

Mansoura University, Mansoura, Egypt

Correspondence to Hend M. Esmaeel, MD, Department of Chest Disease,

Sohag University Hospital, Sohag 82524, Egypt. Tel: +20 112 222 19134;

fax: +20934702693;


Received 25 February 2019 Accepted 13 May 2019

IntroductionIn the past two to three decades, obstructive sleepapnea (OSA) has been identified as an importantcause of public health concern [1].

OSA remained underdiagnosed partly because of thecostly nature and limited availability of the goldstandard diagnostic test, polysomnography (PSG)[2,3].

Careful clinical evaluation may identify patients at highrisk of OSA who strongly need referral to diagnosticPSG.

Study objectiveThe aim was to analyze the role of demographiccharacteristics, anthropometric measures, andcomorbid conditions as predictors of OSA and theirrelation to disease severity in our studied populationsample.






Patients and methodsStudy designThis was a cross-sectional study. It was conducted atsleep disordered breathing unit of the ChestDepartment.

The study was approved by the faculty research ethicscommittee.

An informed written consent was obtained from allstudy patients.

Study patientsAdult patients aged more than 18 years attending theoutpatient sleep clinic were consecutively recruited,with total inclusion of 170 patients.

MethodsAll participants were subjected to the following:

(1)

Medk

History taking.
(2) Thorough clinical examination. (3) Anthropometric measurement:
(a) Weight (kg) and standing height (m) [4].(b) BMI which is calculated by the following

formula:.

BMI ¼ weight kgð Þ=height m2� �

now
DOI: 10.4103/ejb.ejb_19_19

Pre PSG evaluation predicts OSA Esmaeel et al 557

Classification was made according tointernational WHO classification as follows[5].Normal range: BMI 18.50–24.99.Overweight: BMI more than or equal to25.00.Preobese: BMI 25.00–29.99.Obese: BMI more than or equal to 30.00.Obese class I: BMI 30.00–34.99.Obese class II: BMI 35.00–39.99.Obese class III: BMI more than or equal to40.00.

(c) Neck circumference (NC):NC was measured in the midway of the neckbelow the laryngeal prominence at the level ofthe cricothyroid membrane [6].

(d) Waist circumference (WC):Follow the procedure according to thereference [7].
sillar size score [8]: Ton (4)
Tonsil size was graded from 0 to 4. It consists offour classes representing the following criteria:(a) Tonsil size 0: tonsils are absent (tonsillectomy

or atrophied).(b) Tonsil size 1: tonsils hidden within the pillars.(c) Tonsil size 2: tonsil extending to the pillars.(d) Tonsil size 3: tonsils are beyond the pillars but

not to the midline.(e) Tonsil size 4: implies tonsils that extend to the

midline.
Friedman tongue position (FTP) [9]: (5) It was used for palatal-tongue position with tonguein neutral position without tongue depressor orprotrusion. The procedure is repeated five times sothat the observer can assign the most accurate level.It consists of five classes: FTP-I, FTP-IIa, FTP-IIb, FTP-III, and FTP-IV. Friedman obstructive sleep apnea hypoventilation (6) syndrome (OSAHS) score [10]:It was used as a screening system for OSAHSbased on palate position (which is explained inFTP), tonsil size, and BMI. The BMI is graded asfollows: grade 0=less than 20 kg/m2, grade1=20–25 kg/m2, grade 2=25–30 kg/m2, grade3=30–40 kg/m2, and grade 4=more than 40 kg/m2. Then we calculate the OSAHS score by thenumerical values of these findings using thefollowing formula:.
OSAHS ¼ FTP 0� IVð Þ þ tonsil sizeþ BMI value 0� 4ð Þ

Interpretation: any value more than 8 is consideredas a positive OSAHS score, whereas any value less

than 4 is considered a negative OSAHS score.Values in between are borderline and need moreinvestigations.
The additional investigations were as follows: (7) (a) Finger Pulse Oximeter.(b) Arterial blood gases.(c) Routine laboratory investigation.(d) Pulmonary function test.(e) ECG.(f) Chest radiography. Full-night PSG: (8)
All patients had attended full-night PSG usingSOMNOscreen TMplus, version 2.2.0(SOMNOmedics PSG, Somnomedocs GmbH.,Randersacker, Germany). Data interpretation was doneusing the last available scoringmanual [11].Classificationof patients intoOSA and non-OSA and subclassificationof OSA severity group was done using the internationalclassification of sleep disorders [12].

Statistical analysisSTATA intercooled, version 14.2, was used for dataanalysis.

Multivariate regression analysis was used foridentification of significant predictors for patientswith OSA. Graphs were produced by STATAprogram (Stata Corp, California, USA). P value wasconsidered significant if it was less than 0.05.

ResultsThe study participants (n=170) after completing theirclinical evaluation were classified according to PSGresults as follows: with no OSA (n=70), mild OSA(n=21), moderate OSA (n=11), and severe OSA(n=68). Table 1 shows the sociodemographiccharacteristic of the studied population with pairwisecomparison between non-OSA group andOSA groupsof various severities. All factors show significantdifference except for residence.

Comparison between non-OSA andOSAwith variousseverity groups showed that mean BMI, NC, and WCwere significantly higher in severe OSA in comparisonwith non-OSA and mild OSA groups. Almost 56% ofpatients with severe OSA were obese grade III(Table 2).

More than half of patients with OSA (54%) hadFTP grade III. Highest mean Friedman OSASscoring value was measured in patients withsevere OSA, and the difference in Friedman

Table 1 Sociodemographic characteristic of studied population in relation to obstructive sleep apnea and its severity

Variables Non-OSA (N=70) Patients with OSA (N=100) Mild (N=21) Moderate (N=11) Severe (N=68) P value

Age (years)

Mean±SD 41.74±15.00 48.82±11.2 40.24±11.83 50.82±11.79 51.06±9.99 <0.0001*

Median (range) 39 (25 : 73) 50 (19 : 79) 40 (19 : 60) 53 (32 : 70) 52 (31 : 79)

P1<0.0001*, P2=0.59, P3=0.02*, P4=0.0001*, P5=0.04*, P6=0.0009*, P7=1.00

Sex [n (%)]

Females 65 (92.86) 47 (47.00) 11 (52.38) 6 (54.55) 30 (44.12) <0.0001*

Males 5 (7.14) 53 (53.00) 10 (47.62) 5 (45.45) 38 (68.00)

P1<0.0001, P2<0.0001*, P3<0.0001*, P4<0.0001*, P5=0.91, P6=0.51, P7=0.52

Occupation [n (%)]

Employed 32 (45.71) 27 (27.00) 3 (14.29) 2 (18.18) 22 (32.35) <0.0001*

Not employed 38 (54.29) 73 (73) 18 (85.71) 9 (81.2) 46 (67.65)

P1<0.0001, P2<0.0001*, P3<0.0001*, P4<0.0001*, P5=0.62, P6=0.006*, P7=0.40

Smoking [n (%)]

Current smoking 5 (7.14) 22 (22.0) 2 (9.52) 2 (18.18) 18 (26.47) <0.0001*

Nonsmoker 65 (92.86) 66 (66.00) 17 (80.95) 9 (81.82) 40 (58.82)

Ex-smoker 0 12 (12.00) 2 (9.52) 0 10 (14.71)

P1<0.0001*, P2=0.03*, P3=0.23, P4 <0.0001*, P5=0.48, P6=0.17, P7=0.26

Residence [n (%)]

Rural 43 (61.43) 70 (70.00) 12 (57.14) 6 (54.55) 52 (76.47) 0.15

Urban 27 (38.57) 30 (30.00) 9 (42.86) 5 (45.45) 16 (23.53)

OSA, obstructive sleep apnea. Analysis of variance and χ2 test. *Significant P value. P1 compared OSA with non-OSA, P2 compared non-OSA and mild, P3 non-OSA and moderate, P4 non-OSA and severe, P5 mild and moderate, P6 mild and severe, P7 moderate andsevere.

Table 2 Comparison between severity of obstructive sleep apnea and anthropometric measures of studied populations

Variables Non-OSA (N=70) OSA (N=100) Mild (N=21) Moderate (N=11) Severe (N=68) P value

BMI

Mean±SD 37.59±6.24 40.02±9.42 34.51±9.63 38.31±8.19 41.99±8.92 0.0005*

Median (range) 35.9 (29.3 : 48.46) 38.55 (18.5 : 66.95) 34.3 (18.5 : 48.46) 34.3 (27.75 : 55.5) 40.9 (26 : 66.95)

P1=0.06, P2=0.74, P3=1.00, P4=0.008*, P5=1.00, P6=0.001*, P7=0.94

BMI [n (%)]

Normal 0 3 (3.00) 3 (14.29) 0 0 <0.0001*

Overweight 10 (14.29) 9 (9.00) 5 (23.81) 1 (9.09) 3 (4.41)

Obesity type I 16 (22.86) 19 (19.00) 3 (14.29) 4 (36.36) 12 (17.65)

Obesity type II 21 (30.00) 23 (23.00) 7 (33.33) 1 (9.09) 15 (22.06)

Obesity type III 23 (32.86) 46 (46.00) 3 (14.29) 5 (45.45) 38 (55.88)

P1=0.20, P2=0.009*, P3=0.41, P4=0.03*, P5=0.07, P6<0.0001*, P7=0.39

Neck circumference

Mean±SD 40.74±2.88 43.22±4.16 41.14±3.71 42.18±4.05 44.02±4.10 <0.0001*

Median (range) 39 (37 : 46) 43 (33 : 54) 42 (33 : 50) 44 (35 : 48) 44 (35 : 54)

P1<0.0001, P2=1.00, P3=1.00, P4<0.0001*, P5=1.00, P6=0.01*, P7=0.70

Waist circumference

Mean±SD 112.86±17.83 118.26±19.19 104.86±17.83 114.45±16.20 123.24±16.85 0.0001*

Median (range) 105 (90 : 140) 120 (70 : 155) 105 (70 : 142) 120 (95 : 141) 122 (85 : 155)

P1=0.06, P2=0.30, P3=1.00, P4=0.005*, P5=0.72, P6<0.0001*, P7=0.78

OSA, obstructive sleep apnea. Analysis of variance and χ2 test. *Significant P value. P1compared OSA with non-OSA, P2 compared non-OSA and mild, P3 non-OSA and moderate, P4 non-OSA and severe, P5 mild and moderate, P6 mild and severe, P7 moderate andsevere.


OSAS score was significant between mild andsevere case (Table 3).

Regardingcomorbidities,diabetesmellitus,hypertension,and coronary artery disease were significantly morecommon in OSA than non-OSA patients (Fig. 1).

In this study, factors that showed significance in theunivariate analysis were subjected to multivariateregression analysis with calculation of adjusted oddsratio with 95% confidence interval. Identification ofindependent variables for OSA prediction was done byfinal model.

Table 3 Comparison between obstructive sleep apnea and Friedman tongue position, tonsillar size score, and Friedmanobstructive sleep apnea syndrome scoring system of the studied populations

Variables Non-OSA (N=70) OSA (N=100) Mild (N=21) Moderate (N=11) Severe (N=68) P value

FTP [n (%)]

FTP-I 0 11 (11.00) 3 (14.29) 0 8 (11.76) 0.09

FTP-IIa 10 (14.29) 10 (10.00) 2 (9.52) 2 (18.18) 6 (8.82)

FTP-IIb 25 (35.71) 22 (22.00) 6 (28.57) 2 (18.18) 14 (20.59)

FTP-III 34 (48.57) 54 (54.00) 9 (42.86) 6 (54.55) 39 (57.35)

FTP-IV 1 (1.43) 3 (3.00) 1 (4.76) 1 (9.09) 1 (1.47)

P1=0.02*, P2=0.02*, P3=0.35, P4=0.01*, P5=0.60, P6=0.75, P7=0.36

Tonsillar size score [n (%)]

Tonsil size 0 16 (22.86) 13 (13.00) 4 (19.05) 1 (9.09) 8 (11.76) 0.009*

Tonsil size 1 20 (28.57) 47 (47.00) 13 (61.90) 6 (54.55) 28 (41.18)

Tonsil size 2 29 (41.43) 18 (18.00) 2 (9.52) 2 (18.18) 14 (20.59)

Tonsil size 3 5 (7.14) 21 (21.00) 2 (9.52) 2 (18.18) 17 (25.00)

Tonsil size 4 0 1 (1.00) 0 0 1 (1.47)

P1=0.001*, P2=0.02*, P3=0.14, P4=0.002*, P5=0.71, P6=0.25, P7=0.94

Friedman OSAS scoring system

Mean±SD 7.17±1.67 7.29±1.77 6.14±1.59 7.55±1.67 7.61±1.73 0.006*

Median (range) 7 (5 : 11) 7 (3 : 11) 6 (4 : 10) 8 (4 : 9) 8 (3 : 11)

P1=0.65, P2=0.09, P3=1.00, P4=0.76, P5=0.15, P6=0.004*, P7=1.00

Friedman OSAS scoring system [n (%)]

Negative 0 1 (1.00) 0 0 1 (1.47) 0.37

Borderline 52 (74.29) 71 (71.00) 19 (90.48) 7 (63.64) 45 (66.18)

Positive 18 (25.71) 28 (28.00) 2 (9.52) 4 (36.36) 22 (32.35)

P1=0.66, P2=0.12, P3=0.46, P4=0.37, P5=0.07, P6=0.09, P7=0.89

FTP, Friedman tongue position; OSA, obstructive sleep apnea. Student t test and χ2 test. *Significant P value. P1 compared OSA withnon-OSA, P2 compared non-OSA and mild, P3 non-OSA and moderate, P4 non-OSA and severe, P5 mild and moderate, P6 mild andsevere, P7 moderate and severe.

Figure 1

Comorbidities in the studied population.


Age, sex, hypertension, occupation, and tonsillar sizescore remained predictive of OSA in the final model(Tables 4 and 5).

DiscussionThis study was conducted to detect the value ofdemographic, clinical, and comorbid characteristics

Table 4 Multivariate analysis of predictive factors forobstructive sleep apnea

Variables Odds ratio (95% CI) P value

Age (years) 1.06 (1.02 : 1.10) 0.006*

Sex

Females 1 0.05*

Males 2.83 (1.00 : 8.01)

Occupation

Employed 1 0.01*

Not employed 18.56 (1.90 : 81.57)

Smoking

Nonsmoker or ex-smoker 1 0.04*

Current smoking 7.61 (1.09 : 52.95)

Urban residence 2.99 (0.40 : 40.02) 0.17

DM 1.81 (0.50 : 6.50) 0.37

Hypertension 6.12 (1.57 : 23.90) 0.009*

Coronary artery disease 1.56 (0.14 : 16.99) 0.72

BMI 1.02 (0.92 : 1.12) 0.76

Neck circumference 1.16 (0.96 : 1.39) 0.13

Waist circumference 0.98 (0.94 : 1.03) 0.48

FTP

FTP-I/FTP-IIa 1 0.06

FTP-IIb 0.23 (0.05–1.06) 0.11

FTP-III 0.29 (0.06:1.33) 0.50

FTP-IV 5.32 (0.06:291.68)

Tonsillar size score

Tonsil size 0 1 0.07

Tonsil size 1 3.73 (0.90 : 15.50) 0.05

Tonsil size 2 0.14 (0.03 : 0.65) 0.08

Tonsil size 3/4 4.16 (0.81 : 21.27)

Friedman OSAS scoring system

Negative/borderline 1 0.09

Positive 0.07 (0.002 : 1.54)

CI, confidence interval; DM, diabetes mellitus; FTP, Friedmantongue position; OR, odds ratio; OSAS, obstructive sleep apneasyndrome. *Significant P value.

Table 5 Final model of predictive factors for obstructive sleepapnea

Variables Odds ratio (95% CI) P value

Age (years) 1.07 (1.03 : 1.11) 0.001*

Sex

Females 1 0.02*

Males 3.23 (1.25 : 8.31)

Occupation

Employed 1 0.03*

Not employed 8.20 (2.09 : 32.17)

Hypertension 6.98 (2.15 : 22.64) 0.006*

Tonsillar size score

Tonsil size 0 1

Tonsil size 1 2.48 (0.82 : 7.51) 0.11

Tonsil size 2 0.12 (0.03: 0.48) 0.00*

Tonsil size 3/4 4.17 (1.04 : 16.83) 0.04*

*Significant P value.


in prediction of OSA and its severity, which wasdiagnosed by the PSG.

The study recruited 170 patients.

OSA was diagnosed in 100 (58.9%) of our studiedindividuals. Other nationally published studiesreported that 80% and 87.1 of their recruitedpatients were diagnosed as patients with OSA,respectively [13,14].

The mean age of patients with OSA was significantlyhigher than non-OSA patients. Higher age showed1.07-fold increased risk of having OSA. In agreementwith our results,Martins et al. [15] proved that age is anindependent risk factor for OSA with the assumptionthat this can be explained by the age-related reductionin muscle tone that results in decrease diameters ofupper airway lumen. Researcher proved that the ageeffect was obesity independent [16].

Nearly two-thirds of the studied patients were female(65.88%). Male sex was associated with a 3.23-foldhigher risk of havingOSA and increased risk of severityof the disease in the present study. Consistent reportingof higher OSA prevalence in men and association ofOSA with male sex was found in the literature [17].

Explanation for this was made by different adiposetissue distribution, anatomical feature of upper airways,and different muscle function in men.Moreover, leptinand sex hormones exert their own endocrine effects inmen [18].

Most of the patients in OSA in this study were notemployed (73%), with the risk of OSA being 8.20higher than those who were employed. This may reflectthe sequence of unhealthy sedentary life withsubsequent weight gain. However, workers atstressful jobs were proved to be also at risk of OSAin a published research [19].

We found more current smokers in the OSA group(22%) than in the non-OSA group (7.14) and thedifference was statistically significant. However,smoking status did not show significance in the finalmodel of regression analysis. Controversy was found inthe literature about relation of smoking and OSA.Some researcher found it a nonsignificant risk factorfor reporting sleep disordered breathing symptoms[20], whereas others reported that current and pastsmokers were significantly at higher risk of OSA [21].

Obesity is known as a major risk factor for OSA. Thepathogenesis may be mediated by the primary effect ofobesity in the form of fat deposition subcutaneouslyand intraluminal in the upper airway. This changed theupper airway compliance and predispose to its collapse[22]. The secondary effects of obesity on lung functions


also mediate its pathogenic role in OSA [23]. Thisstudy used three measurement tools of bodymorphology: BMI, NC, and WC.

The study results showed that mean BMI was higher,and percentage of obese type II and III was higher inOSA than non-OSA participants. The difference inthese parameters showed significance in relation todisease severity. This finding was in in accordancewith many studies in the literature that documentedthe association between higher BMI and increased riskof OSA [24,25].

Our results revealed that higher tonsil size scoreaccording to Friedman classification was associatedwith nearly four-fold increased risk of having OSAin the final model of multivariate analysis. Thesefindings are in accordance with the evidence in theliterature that there is good correlation between theFriedman classification and the apnea–hypopnea index[10].

Regarding comorbidities, diabetes mellitus was foundto be more prevalent in the OSA group than in non-OSA group and in particular in the severe OSAsubgroup, and the difference was statisticallysignificant. There is accumulating evidence in theliterature about the association between OSA anddiabetes mellitus. Some researchers reached to aconclusion in their review about this patient thatsleep duration and quality are linked to the glycemiccontrol in patients with type 2 diabetes [26].

In the final analysis model, hypertension was associatedwith 6.98-fold increased risk of having OSA.Hypertension and cardiovascular consequences areknown to have important implications in thecoexistence of obesity and OSA [27]. It is assumedthat OSA is an essential differential diagnosticconsideration in obese hypertensive patients [28].

In this study, factors identified by the final model ofregression analysis as predictors for OSA were age,male sex, hypertension, and tonsillar size. These resultsare close to the published research work [29], as theresearchers in this work concluded that sex, age,hypertension, and obesity are independent riskfactors for OSA in their studied Saudi population,which is similar to results from western studies.

Study strengthOSA was confirmed in our study group by PSG, whichadds reliability to our study results. Many publishedstudies assessed the risk factors of OSA while utilizing

validated simple questionnaires only withoutperforming PSG [20,30].

Study limitationOne of the study limitations was that radiographicmeasure to directly quantify fat deposition in the upperairway was not done. However, this can be argued thatwe aimed at utilizing clinical evaluation and noncostlyinvestigation to define their predictive ability. Otherlimitation was that it is a single-center study.

ConclusionDetailed clinical evaluation, anthropometric measures,and presence of comorbidity could identify predictivefactors for OSA and help in patient selection forreferral for PSG diagnostic sleep study, which isrelatively expensive and not widely available.



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Study of serum cystatin C levels in patients with obstructivesleep apneaEman Elfekya, Ayman Abd El-Zahera, Amal Elbendaryb, Salwa Gannaa

Background Serum cystatin C is a sensitive indicator forearly renal impairment in patients with obstructive sleepapnea (OSA). In patients with OSA without known chronickidney disease (CKD), serum cystatin C was found to beelevated, indicating latent renal disease, which may becaused by recurrent episodes of hypoxemia andreoxygenation that trigger inflammatory process.

Objective To evaluate serum levels of cystatin C in otherwisehealthy patients with OSA to predict the future risk of latentrenal impairment in these patients.

Patients and methods A total of 30 patients were classifiedinto two groups: group I included 10 healthy controls, andgroup II included 20 patients with OSA.The following were done: full history taking, clinical examination,pulmonary function tests, subjective evaluation of daytimesleepiness using the Epworth Sleepiness Scale, and overnightpolysomnography. Finally, serum cystatin C levels at the end ofpolysomnography and glomerular filtration rate was calculatedusing modification of diet in renal disease formula and CKD-Epidemiology Collaboration cystatin equation.

Results In patients with OSA, the mean level of serumcystatin C was statistically significantly higher than that incontrols. These higher levels were sex, age, and BMIindependently. Moreover, serum levels of cystatin C inpatients with OSA were significantly correlated with OSA


severity (apnea-hypopnea index) and degree of nocturnalhypoxia through positive correlation with total sleep time lessthan 90%, total sleep time less than 80%, and respiratorydisturbance index and negative correlation with averageSPO2%, lowest SPO2%, and estimated glomerular filtrationrate by CKD-Epidemiology Collaboration cystatin equation.

ConclusionsHigher serum levels of cystatin C were detectedin patients with OSA without known comorbidities, whichindicates that these patients are at a higher risk of developingCKD. Intermittent hypoxia seems to have the main role in theprogression of this process.Egypt J Bronchol 2019 13:563–569© 2019 Egyptian Journal of Bronchology


Keywords: chronic kidney disease, chronic kidney disease cystatin C,obstructive sleep apnea,

aChest Department, bClinical Pathology Department, Faculty of Medicine,

Tanta University, Egypt

Correspondence to Salwa A Ganna, MD, Chest Department, Faculty of

Medicine, Tanta University, 31527 Egypt. Tel: +20403337544;

fax: +20403407734.


Received 9 April 2019 Accepted 16 August 2019






IntroductionObstructive sleep apnea (OSA) is characterized byrecurrent attacks of either partial or complete upperairway collapse, resulting in hypopnea or apneaduring sleep [1]. Several studies reported thatchronic kidney disease (CKD) prevalence increasesin patients with OSA, which is a known risk factorfor cardiovascular disease (CVD), even in mild OSA[2,3]. Cystatin C, a protein with a low molecularweight, is an inhibitor of lysosomal cysteine protease.It is mainly used as a biomarker for kidney function,as cystatin C is filtered by glomeruli and thenreabsorbed and catabolized in the proximalconvoluted tubules, but it is not secreted by thetubules [4]. In contrast to serum creatinine,cystatin C is not influenced by either muscle mass,age, or sex. So, it is a more sensitive indicator of mildand early kidney impairment and is a faster and abetter estimate of glomerular filtration rate (GFR)than serum creatinine [5]. This work aimed atevaluation of serum levels of cystatin C inotherwise healthy patients with OSA to predictthe future risk of latent renal impairment in thesepatients.

Patients and methodsThis prospective analytic observational cross sectionalstudy was conducted in the Sleep Laboratory Unit inChest Department, Tanta University Hospitals,between July 2017 and February 2018. A total of101 patients were evaluated for OSA; 42 patientswere hypertensive, 29 were diabetic, 21 patients werecardiac, and nine patients had CKD. So, these 71patients were excluded from the study, and only 30patients were included. The local ethics committee ofour center has approved the research protocol, andwritten informed consents were obtained from allparticipants.

The 30 patients were classified into two groups: group Iincluded 10 healthy controls, matched to patients withOSA by sex, age, and BMI. All control persons areapparently healthy individuals with no history of any




diseases, had normal physical examinations, and hadapnea-hypopnea index (AHI) less than 5 event/h.Group II included 20 patients with OSA asconfirmed by AHI more than or equal to 5 event/h.Patients were diagnosed with OSA if their AHI rangesfrom 5 events/h to less than 15 event/h and presented atleast one of the following: loud snoring, daytimesleeping, fatigue, and breathing interruption duringsleep [6]. Patients with AHI more than or equal to15 events/h were diagnosed with OSA regardless ofwhether they had any additional compliant [6]. OSAwas considered mild if AHI ranges from 5 events/h toless than 15 events/h, moderate if AHI ranges from 15events/h to less than 30, and severe if AHI more thanor equal to 30 events/h. If AHI less than 5 events/h, theperson was taken as healthy control. Patients withcentral sleep apnea diagnosed by polysomnography(PSG), patients with systemic hypertension, diabeticpatients, previously diagnosed patients with OSA,patients who started treatment with either CPAP ororal appliances, patients with cardiac or hepaticdiseases, and patients with known renal diseaseswere excluded from this study.

All 30 persons were subjected to the following:

(1)
History taking, including personal history; medicalhistory; and subjective evaluation of daytimesleepiness using the Epworth Sleepiness Scale,which is a short questionnaire aiming at evaluationof the possibility of falling asleep in differentsituations [7].
(2)
Physical examination,which includedmeasurementof height; weight; neck, hip, and waistcircumferences; and waist/hip circumference ratio.BMI was calculated using the following formula:BMI=weight (kg)/height2 (m).
(3)
Arterial blood gas analysis. (4) Plain chest x-ray (posteroanterior view) (5) ECG. (6) Pulmonary function testing. (7) Overnight PSG was conducted in the Sleep
Laboratory Unit in Chest Department, TantaUniversity Hospitals, at night during sleep for atleast 6 h (started at 10 pm). Participants wereconnected to SOMNO screenTM plus PSG+

(SOMNO Medics GmbH, Germany), whichincluded the following channels: pressure andflow (cannula and thermistor), snore (cannulaand/or microphone), thoracic effort, abdominaleffort, pulse oximetry, ECG, periodic legmovement, electrooculogram, electroence-phalogram (10 electrodes) to detect total sleeptime (TST), and sleep efficiency. Respiratory

event analysis included AHI and respiratorydisturbance index (RDI). It identified differenttypes of apneas (obstructive, central, or mixed)and calculated their absolute values and indices.It estimated respiratory effort related arousal indexand absolute value. It measured the snoring andarousal index. Oxygen saturation analysisdetermined the baseline, average, and minimaloxygen saturation. It estimated TST less than90% (time of sleep with oxygen saturation below90%) and TST less than 80%, as well as oxygendesaturation index which is the number of timesper hour of sleep that the blood’s oxygen leveldrops by 3% or more from baseline. Heart rateanalysis measured maximal, minimal, and averageheart rate. The PSG analysis was doneautomatically for all participants by DOMNOsoftware and then revised manually. Sleep stageand respiratory event scoring was done accordingto the standard criteria of the American Academyof Sleep Medicine (AASM), version 2.3 [8].

(8)
Laboratory investigations: fasting venous bloodsamples were collected under complete asepticconditions the morning after the study. Sampleswere divided correspondingly for the following:(a) Routine laboratory investigations: fasting
blood glucose considering diabetic rangestarting from 126mg/dl [9], triglyceride,total cholesterol, urea, creatinine, and C-reactive protein.

(b) Cystatin C serum concentration: 2ml of thevenous sample is used after centrifugationwithin 1 h of collection. Serum samples werestored at −25°C for cystatin C analysis at theend of the study. Cystatin C was measured byusing a particle-enhanced turbidimetricimmunoassay.

Estimated glomerular filtration rate (eGFR)
(9) was measured by modification of diet in renaldisease (MDRD) formula [10] and theCKD-Epidemiology Collaboration (EPI)cystatin C equation [11] adjusted for age, sex,and race.
Statistical analysisExpression of quantitative data as mean±SD was done.Data were tested for normal distribution usingKruskal–Wallis test and Mann–Whitney test toevaluate the significance of difference among bothgroups using SPSS, version 20 (Statistical Packagefor Social Sciences, version 20, IBM). A P value lessthan 0.05 was considered to be statistically significant.In addition, Pearson correlations were performedbetween serum cystatin C levels and different variables.

Serum cystatin C in obstructive sleep apnea Elfeky et al. 565

ResultsThis study included 30 patients: 20 were diagnosed ashave OSA by PSG (AHI≥5 event/h), and 10 apparenthealthy control persons.

Demographic and anthropometric characteristics ofboth patients with OSA and controls are shown inTable 1. No statistically significant difference wasdetected between both groups regarding age, sex,percent of smokers, and BMI.

Statistical comparison between the two studied groupsregarding different PSGparameters is shown inTable 2.There was no statistically significant difference betweenboth groups regardingTST and baseline SPO2, whereasaverage SPO2% and minimal SPO2% were found to bestatistically significant lower in OSA group than incontrol group, with P value of 0.001 for allparameters. TST less than 90%, TST less than 80%,RDI, snoring index, and arousal index were found to bestatistically significant higher in OSA group than incontrol group, with P value of 0.001 for all parameters,except arousal index, with P value of 0.028.

Regarding classification of OSA severity regardingAHI (Fig. 1), OSA was mild in four patients,moderate in four patients, and severe in 12 patients.

Table 1 Comparison of anthropometric characteristics between ob

Control

Age (years)

Range 33–67

Mean±SD 49.20±9.78 5

Sex [n (%)]

Male 4 (40)

Female 6 (60)

Smoking [n (%)]

Yes 3 (30)

Ex-smoker 1 (10)

Never smoke 6 (60)

Neck circumference (cm)

Range 30–43

Mean±SD 36.60±3.66 4

Hip circumference (cm)

Range 100–131

Mean±SD 112.60±10.15 13

Waist circumference (cm)

Range 97–122

Mean±SD 107.80±9.73 12

Waist/hip ratio

Range 0.9–0.98

Mean±SD 0.95±0.03 0

BMI (kg/m2)

Range 24.8–41.4 2

Mean±SD 35.1±4.6*Statistically significant at P value less than or equal to 0.05.

AHI ranged from 7.0 to 99.3 event/h, with mean±SDof 48.94±29.90 event/h.

Statistical comparison between the two studied groupsregarding different laboratory investigation is shown inTable 3. No significant differences were detectedbetween both groups regarding ABG, fasting bloodglucose, triglyceride, total cholesterol, blood urea,serum creatinine, and eGFR by MDRD formula.However, eGFR by CKD-EPI cystatin C equationwas found to be statistically significant lower in OSApatients than control group. Moreover, serum cystatinC levels were statistically significant higher in OSAgroup than control group.

Receiver operating characteristic curve analysis(Table 4, Fig. 2) showed that cutoff value of eGFRby CKD-EPI equation in predicting renal injury was60mlmin 1.73 m2, with a sensitivity of 95%, specificityof 100%, positive predictive value of 100%, negativepredictive value of 91%, and accuracy of 97%.

Statistical correlation between serum levels of cystatin Cand some studied parameters in patients with OSA isshown in Table 5. The OSA group revealed that therewas no statistically significant correlationbetween serumcystatin C levels and either anthropometric parameters,

structive sleep apnea patients and controls

Patients Test P value

40–74 t=1.687 0.205

3.60±8.21

8 (40) χ2=0.0 1.0

12 (60)

7 (35) χ2=0.752 0.687

4 (20)

9 (45)

37–49 t=12.554 0.001*

0.95±2.91

119–149 t=35.208 0.001*

3.15±8.31

111–134 t=28.269 0.001*

3.00±5.96

0.8–1.02 t=4.014 0.055

.92±0.05

7.3–46.3 t=1.524 0.227

37.7±5.7

Table 2 Statistical comparison between the two studiedgroups as regard different polysomnographic parameters

Control Patients t test P value

AHI

Range 2–4.7 7–99.3 22.457 0.001*

Mean±SD 3.72±0.92 48.94±29.90

Average SPO2 (mmHg)

Range 95–98 80–95 36.901 0.001*

Mean±SD 96.20±1.14 85.60±5.41

Minimal O2 saturation(mmHg)

Range 93–96 62–92 26.437 0.001*

Mean±SD 93.90±1.10 77.45±8.80

Baseline O2 saturation (mmHg)

Range 95–98 93–98 2.041 0.164

Mean±SD 96.10±0.99 95.35±1.50

TST (min)

Range 245.4–372.0 242.4–376.8 0.000 0.990

Mean±SD 302.80±45.67 303.0±38.79

TST<90% (min)

Range 0–0 3–569 20.343 0.001*

Mean±SD 0±0 209.05±145.28

TST<80% (min)

Range 0–0 0–372 14.813 0.001*

Mean±SD 0±0 126.10±102.70

RDI (event/h)

Range 3.4–4.7 7–99.3 20.581 0.001*

Mean±SD 4.06±0.41 49.74±29.76

Arousal index (event/h)

Range 0.5–32.9 0.8–118 5.398 0.028*

Mean±SD 15.73±9.41 38.94±30.63

Snore index (event/h)

Range 0.2–10.5 1.5–74.3 37.712 0.001*

Mean±SD 3.37±3.19 43.03±20.12

AHI, apnea-hypopnea index; RDI, respiratory disturbance index;TST less than 90%, total sleep time with oxyhemoglobin saturationless than 90%; TST, total sleep time; TST less than 80%, totalsleep time with oxyhemoglobin saturation less than 80%.*Statistically significant at P value less than or equal to 0.05.

Figure 1

Classification of OSA severity as regard apnea-hypopnea index (AHI)showed that OSA was mild in four (20%) patients, moderate in four(20%) patients and severe in 12 (60%) patients. OSA, obstructivesleep apnea.

Table 3 Statistical comparison between the two studiedgroups regarding different laboratory investigations

Control Patients t test P value

pH

Range 7.36–7.42 7.35–7.44 2.425 0.131

Mean±SD 7.38±0.02 7.40±0.03

PaCO2 (mmHg)

Range 37.1–43.2 36.3–44.1 0.961 0.335

Mean±SD 39.27±1.89 40.15±2.48

PaO2 (mmHg)

Range 80.6–101 80.1–102 0.499 0.486

Mean±SD 87.93±5.51 86.27±6.34

Glucose (mg/dl)

Range 88–112 58–123 0.295 0.591

Mean±SD 101.30±7.86 98.25±16.74

Triglycerides (mg/dl)

Range 110–239 107–260 0.202 0.656

Mean±SD 184.60±41.76 177.47±40.52

Cholesterol (mg/dl)

Range 193–240 129–251 0.020 0.889

Mean±SD 207.00±14.23 205.34±35.75

Urea (mg/dl)

Range 23–37 20–42 1.431 0.242

Mean±SD 29.00±4.81 31.64±6.06

Creatinine (mg/dl)

Range 0.6–0.9 0.6–1.1 3.224 0.083

Mean±SD 0.75±0.13 0.83±0.12

eGFR by MDRD formula (mlmin 1.73 m2)

Range 65–146 49–143 1.764 0.195

Mean±SD 102.00±32.82 88.55±22.30

eGFR by CKD-EPI equation (mlmin 1.73 m2)

Range 80–113 28–62 69.933 0.001*

Mean±SD 95.80±10.53 50.75±8.05

Cystatin C (mmol/l)

Range 0.60–0.90 0.89–2.81 10.054 0.004*

Mean±SD 0.73±0.11 1.17±0.44

eGFR by CKD-EPI equation, estimated glomerular filtration rate bychronic kidney disease-Epidemiology Collaboration cystatin Cequation; eGFR by MDRD formula, estimated glomerular filtrationrate by modification of diet in renal disease; PaCO2, partialpressure of CO2; PaO2, partial pressure of O2. *Statisticallysignificant at P value less than or equal to 0.05.

Table 4 Receiver operating characteristic curve of estimatedglomerular filtration rate by chronic kidney disease-Epidemiology Collaboration cystatin C equation

Cutoff Sensitivity Specificity PPV NPV Accuracy

eGFR byCKD-EPIequation(mlmin1.73 m2)

60 95 100 100 91 97

eGFR by CKD-EPI equation, estimated glomerular filtration rate bychronic kidney disease; Epidemiology Collaboration cystatin Cequation; NPV, negative predictive value; PPV, positive predictivevalue.


age, baseline O2 saturation, serum creatinine, or eGFRby MDRD formula. A significant positive correlationwas detected between serum cystatin C level and AHI,

TST less than 90%, andTST less than 80%.Therewas asignificant negative correlation between serum cystatinC and average SPO2%, minimal SPO2%, and eGFR byCKD-EPI equation.

Figure 2

Receiver operating characteristic (ROC) curve analysis showed that cutoff value of eGFR by CKD-EPI equation in predicting renal injury was60mlmin 1.73 m2 with a sensitivity of 95%, specificity 100%, PPV 100%, NPV 91% and accuracy 97%. CKD, chronic kidney disease; eGFR,estimated glomerular filtration rate; EPI, Epidemiology Collaboration; NPV, negative predictive value; PPV, positive predictive value.

Table 5 Statistical correlation between serum levels of cystatin C and some studied parameters in obstructive sleep apneapatients

Cystatin C

Variables r P

RDI (event/h) 0.697 0.022*

Baseline O2 saturation (%) −0.253 0.512

Minimal O2 saturation (%) −0.553 0.031*

AHI (event/h) 0.786 0.008*

Age (years) 0.131 0.718

Neck circumference (cm) 0.050 0.891

Hip circumference (cm) −0.164 0.651

Waist circumference (cm) −0.204 0.572

Waist/hip ratio −0.181 0.616

eGFR by MDRD formula (mlmin 1.73 m2) 0.424 0.222

eGFR by CKD-EPI cystatin C equation (mlmin 1.73 m2) −0.842 0.002*

Creatinine (mg/dl) −0.329 0.354

TST<90% 0.652 0.024*

TST<80% 0.587 0.032*

Average SPO2 0.754 0.012*

AHI, apnea-hypopnea index; eGFR by CKD-EPI equation, estimated glomerular filtration rate by chronic kidney disease-EpidemiologyCollaboration cystatin C equation; eGFR by MDRD formula, estimated glomerular filtration rate by Modification of diet in renal disease;RDI, respiratory disturbance index; TST less than 90%, total sleep time with oxyhemoglobin saturation less than 90%; TST less than 80%,total sleep time with oxyhemoglobin saturation less than 80%. *Statistically significant at P value less than or equal to 0.05.



DiscussionIn this work, there was a statistically significant increasein serum cystatin C levels in OSA group than in controlone. The key feature in OSA is intermittent hypoxiawhich appeared to have themain role in the pathologicalmechanismofCKDdevelopment in patientswithOSA.This was proved by the significant positive correlationbetween cystatin C level and AHI, TST less than 90%,TST less than 80%, and RDI. However, there was asignificant negative correlation between cystatin C andaverage SPO2%,minimal SPO2%, and eGFR byCKD-EPI cystatin C equation.

In a previous study by Kato et al. [12], they found thatserum cystatin C levels were increased in patients withsevere OSA without CKD, and they considered theserum cystatin C as a biomarker for both clinicallylatent renal disease and cardiovascular risk, which is anexpected complication of OSA.

Zhang et al. [13] showed in across-sectional study thatserum cystatin C levels were relatively higher inyounger men with severe OSA and in younger menwithout known CKD and CVD.Moreover, they foundthat serum levels of cystatin C were correlated withoxygen desaturation index, high sensitivity C-reactiveprotein, serum creatinine, and eGFR. In addition,AHI was positively correlated with serum levels ofcystatin C. Serum levels of cystatin C weresignificantly and negatively correlated with eGFR.

A study done by Archontogeorgis et al. [14] showed thathigher serum levels of serum cystatin C were detected inmiddle-aged otherwise healthy patients with OSAcompared with nonapneic who were matched for ageand BMI, indicating that these patients are moresusceptible to develop chronic kidney and CVD, andserum cystatin C levels were positively correlated withRDI, TST less than 90%, and average SPO2% duringsleep. Another study done by Chuang et al. [15]concluded that albuminuria and serum cystatin C levelswere positively correlated with OSA severity. Moreover,Chung et al. [16], in a study conducted on 433 patientswith OSA of different severity and 54 healthy controls,found that serum cystatin C levels in the moderate OSAgroup and the extremely severe OSA group were highercompared with the control group and the eGFR levelswere significantly lower in the moderate and extremelysevere OSA group than that in the control group.

In the present work, there was no statisticallysignificant difference in both groups regardingeGFR by MDRD formula, whereas on comparingthe mean value of eGFR by CKD-EPI cystatin C

equation between the two groups, it was found to bestatistically significant lower in patients withOSA thancontrol group.

The present study depends upon CKD-EPI-cysequation but not CKD-EPIcreat-cys equation, asthere is no difference in reclassification between thetwo equations according to KDIGO guidelines [17].

A study of Chou et al. [18] showed that high prevalenceof CKD is present in patients with severe OSAwithouthypertension or diabetes. Significantly positivecorrelations were found between severity of OSAand renal function impairment.

Matsushita et al. [19] found that although CKD-EPIcystatin equation detected fewer individuals as havingCKD, it was more accurate in categorization of the riskfor mortality and the development of ESRD than theMDRD study equation across a great number ofparticipants.

Shlipak et al. [20] found that eGFR based on cystatin Calone or in combination with creatinine strengthens theassociation between the eGFR and the risks of deathand end-stage renal disease across diverse populationsmore than eGFR based on creatinine.

These findings are inpartial discrepancywithdata fromacohort study by Rogacev et al. [21], where application ofCKD-EPIcreat-cys, compared with the MDRD andCKD-EPIcreat equations, allows better prediction ofCKDprogression and death among patients withCKD.In a study of Zalucky et al. [22], it was found that OSAandnocturnal hypoxemia are closely related toCKDandup-regulation of the renin–angiotensin system, which isdeleterious to renal function.

Doganer et al. [23] believed that the eGFR by CKD-EPI-cys C equation or the combined equation withcreatinine has superiority to GFR equations based oncreatinine alone.

Uyar et al. [24] showed that renal dysfunction seen inpatients with OSA could be regarded as an additive oreven synergistic result of both direct effects ofdisordered sleep itself and indirect effect by way ofmetabolic derangements.

In contrast to the recent study, Shardlow et al. [25]didnotrecommend the use of cystatin C for routine diagnosis ofCKD, and they recommended its use only in conditionswhere the use of serum creatinine is known to beunreliable. Moreover, they concluded that the use of


cystatin C did not improve the prediction of the risk formortality or deterioration of CKD. The differencebetween their observation and ours can be explained bythe differences in the aim of both studies, as they usedcystatinCas amarker fordetectionof the severity ofCKDin already diagnosed cases, but in the present study, theaim was to use cystatin C in early detection of renalimpairment in apparently healthy individuals, inaddition to the big difference in the number of targetedpopulations in both studies.

In the present study, eGFR by MDRD formula in allparticipants was not significantly affected because itdepends mainly on creatinine level, which was notalso significantly affected, as participants had not yetdeveloped CKD, in contrast to eGFR by CKD-EPIcystatin equation, where it was statistically significantlower in patients with OSA than control group, as itdepends mainly on cystatin C levels, which werestatistically significant higher in patients with OSA,and it is a predictor of early and latent renal dysfunction.

Our study had several limitations: limited number ofthe study population and limited time of the study.Moreover, it is a case-controlled study, so follow-up isneeded to determine the probable future prevalence ofchronic renal disease in patients with OSA with highserum cyst C levels. Moreover, there is a probabilitythat this study included patients having CKD despitetheir normal eGFR because proteins and albumin inurine had not been measured.Most of participants whowere included in this analysis were elderly and had highprevalence of major chronic diseases, which wereassociated with a higher risk of CKD.

In conclusion, detection of higher levels of serumcystatin C in otherwise healthy patients with OSAcould indicate an increased future risk of developingCKD in these patients. Intermittent hypoxia seems toplay a central role in the progression of this process.



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utilization in women with obstructive sleep apnea syndrome 2 years afterdiagnosis and treatment. Sleep 2006; 29:1307–1311.

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4 Blaydon DC, Nitoiu D, Eckl KM, Cabra RM, Bland P, Hausser I, et al.Mutations in CSTA, encoding cystatin A, underlie exfoliative ichthyosis andreveal a role for this protease inhibitor in cell-cell adhesion. Am J HumanGenet 2011; 89;564–571.

5 Dharnidharka VR, Kwon C, Stevens G. Serum cystatin C is superior toserum creatinine as a marker of kidney function: a meta-analysis. Am JKidney Dis 2002 40;221–226.

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7 JohnsMW. A newmethod for measuring daytime sleepiness: The EpworthSleepiness Scale. Sleep 1991; 14:540–545.

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Case report 570

Mediastinal abscess complicating esophageal dilatation: a casereportDoaa M. Magdy, Shereen Farghaly, Ahmed Metwally

Mediastinal abscess is a rare yet emergent infectiouscomplication of the thoracic cavity following balloondilatation of the esophagus. Early diagnosis andmanagement could avoid its poor outcome. A 20-year-oldman with esophageal stricture underwent balloon dilatation. Amediastinal abscess developed 2 weeks after procedure.Computed tomographic chest helped in diagnosis andguiding approach of management. Surgical drainage anddebridement of the abscess were performed. Surgicaltreatment combined with systemic antibiotics was effective,leading to remission of the abscess. Mediastinal abscessshould be considered as a possible infectious complicationafter upper endoscopy. Computed tomographic scan is amandatory imaging modality to enable early diagnosis.Aggressive treatment including surgical drainage combined


with medical management is the treatment of choice that mayprevent catastrophic outcome.Egypt J Bronchol 2019 13:570–573© 2019 Egyptian Journal of Bronchology


Keywords: balloon dilatation, mediastinal abscess, mediastinoscope

Department of Chest, Faculty of Medicine, Assiut University, Assiut, Egypt

Correspondence to Doaa Magdy, MD, Chest Diseases and Sleep

Medicine, Chest Department, Faculty of Medicine, Assiut University

Hospital, Assiut, 71111, Egypt. Tel; 01006261010;


Received 27 January 2019 Accepted 12 May 2019

IntroductionBenign esophageal strictures had been reported owingto several causes, for example, gastro-esophageal reflux,radiation therapy, esophageal resection, ablativetherapy, or the ingestion of a corrosive substance[1]. Treatment of most strictures typically involvesendoscopic dilation using bougies or balloons.Despite advances in endoscopic equipment anddilators which have improved the safety ofesophageal dilation, infectious complications stillhave been reported even in the most experiencedhands [2]. The most dreadful complicationassociated with esophageal dilatation is esophagealperforation [3]. The problem is that symptoms andsigns of early esophageal injury are vague andnonspecific [4], which in its part can lead tocatastrophic presentation of the patient. Thus,awareness of the complications associated withdilation permits early recognition and reducesmorbidity and mortality [2].

We reported a case of mediastinal abscess complicatingupper gastrointestinal (GI) endoscopy for treatment ofesophageal stricture, which was successfully treatedwith prompt surgical drainage.






Case reportA 20-year-old male had a history of dysphagia sincechildhood and diagnosed using upper GI endoscopy ascongenital esophageal stricture for whom repeatedballoon dilatation was done. The patient underwentan uneventful esophageal dilatation via a balloon(Olympus GIF-H260) to relieve symptoms. The

patient was discharged without complications 1 dayafter the procedure.

Two weeks after upper GI endoscopy, the patientpresented to the local emergency departmentcomplaining of shortness of breath, cough withyellowish scanty sputum, and fever. A chestradiography was done for him (Fig. 1a), and anonspecific antibiotic was prescribed for him. Threedays later, the patient presented with progressivedyspnea and persistent continuous fever notresponding to antibiotic or antipyretic. Onadmission, the patient was evaluated. The patientlooked pale and toxic, had a body weight of 60 kg,blood pressure of 100/60 mmHg, heart rate of 110beats/min, respiration rate of 35 breath/min, and bodytemperature of 40°C. Normal chest examination wasreported.

Laboratory tests showed white blood cell count of20×104/ml, hemoglobin concentration of 10 g/l,hematocrit of 30.2%, and platelet count of136×1011/l. Chest X- ray was repeated showingbroadening of the upper mediastinum (Figure 1B).A computed tomographic (CT) scan of the chestrevealed a multiple air spaces in the mediastinum inretrosternal space and around the esophagus (a picturecompatible with a mediastinal abscess) Figure 2.



Figure 1

(a) Chest radiography before the esophageal dilatation, and (b) chest radiography 2 weeks after the procedure, showing wide mediastinum withhomogenous opacity in the right chest obscuring the right border of heart and trachea.

Figure 2

Computed axial tomographic scan of the chest showing a well-defined mass with air collection behind the anterior chest wall.

Mediastinal abscess complicating esophageal dilatation Magdy et al. 571

On the second day of admission, cardiothoracicsurgeons were consulted and mediastinoscopicdebridement and drainage of abscess cavity wasperformed (Fig. 3). Drainage tube was placedin the cavity of the anterior mediastinal abscess.Gram staining of the pus aspirated from the anteriormediastinal abscess revealed streptococcus pneumoniaand Diphtheroids. The patient’s general condition

gradually improved with antibiotic treatment anddrainage, and he was discharged on antibiotics after15 days following the procedure.

EthicsThis study was approved by local ethicsreview committees, and the patient gave informedconsent.

Figure 3

Chest radiography showing the opaque drainage tube in the abscess cavity.


ConsentWritten informed consent was obtained from thepatient for publication of this case report andaccompanying images.

DiscussionThe mediastinum is a rare site for infection, butsometimes extension of infection from nearbystructures, rupture of the tracheobronchial tree, oresophageal perforation can cause acute mediastinitisor mediastinal abscess. Mediastinal abscess has beenrecorded in ∼1% of patients presenting withesophageal perforation [5]. Infection was possiblycaused by direct contamination of the mediastinumwith the esophageal bacterial flora. Thus, anaerobessuch as Streptococcus viridans and fusiform rods werefrequently isolated from cases of acute mediastinitissecondary to esophageal perforation [6], and that wasclearly demonstrated in our case. Early diagnosis ismandatory as mediastinal abscess can lead to rapid andfatal deterioration of the patient’s condition.Unfortunately, the clinical symptoms are oftenatypical and misleading [5].

Radiological investigations could be helpful.Conventional chest radiography may show theboarding of upper mediastinum and loss of itsnormal contours. Pneumomediastinum might be alsoobserved. However, definite air spaces collection in themediastinum could be detected by CT of the chest. CTchest could also identify the extent of soft-tissueinfiltration and the optimum site for drainage of themediastinal abscess [7].

Surgical drainage and appropriate antibiotic therapyare considered the cornerstone of management of a caseof mediastinal abscess [8]. Both the location and extentof abscess are considered as important factors indetermination of management. In this case,mediastinal abscess was in anterior rand uppermediastinum, thus transcervical drainage had beendone. However, thoracotomy or subxipoid incision isrecommended for extensive subcarinal abscesses [9].Recently, several investigators had shown video-assisted thoracoscopic surgery to be a successfulalternative to open methods in mediastinitismanagement from multiple etiologies. Suchtherapies may be preferable to more aggressivemethods owing to reduced risk of complication, lesspain, and more rapid recovery [10].In conclusion,mediastinal abscess should be considered a possibleeven if rare complication after upper endoscopy. Closemonitoring for the development of signs and symptomsof infection following endoscopy is essential. CT scanis a mandatory imaging modality to enable earlydiagnosis. Aggressive treatment including surgicaldrainage combined with medical management is thetreatment of choice that may prevent catastrophicoutcome.

AcknowledgementsThe authors wish to express their gratitude to thepatient who kindly agreed to participate in thisresearch.


Mediastinal abscess complicating esophageal dilatation Magdy et al. 573


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Documents

· Board of Th e Egyptian Journal of Bronchology (EJB) Editor-in-Chief Tarek Safwat Ain Shams University, Egypt Associate Editors Amr Shoukri Ain Shams University, Egypt Ashraf Madko