Exploring The Concordance Of Malnutrition Assessment Tools ......Exploring The Concordance Of...

Exploring The Concordance Of Malnutrition Assessment Tools With

The GLIM Criteria Among Hemodialysis Patients

Mirey Karavetian1, Nada Salhab2, Rana Rizk3,4, Kalliopi-Anna Poulia5

1Zayed University, Dubai, United Arab Emirates; 2Maastricht University, Maastricht, Netherlands 3Institut National de Santé Publique, d’Épidémiologie Clinique et de Toxicologie, The Lebanese University, Fanar, Lebanon 4Maastricht University, The Netherlands; 5Laiko General Hospital, Athens, Greece

Introduction

• Malnutrition in hemodialysis (HD) is a well-described condition, often co-existing with inflammation

• It affects 50 to 75% of HD, depending on the diagnostic tool used.

Ta Ikizler et al, Kidney International 2013

Etiology and consequences of PEW in CKD

MIS

ESPEN – GUIDELINES:Bioelectrical impedance analysis: Review of principles & methods. Clin Nutr 2004; 23: 1226-1243

Utilisation in clinical practice. Clin Nutr 2004; 23: 1430-1453

Bioelectrical Impedance Analysis

5

Correlated with

• Nutritional status

• Muscle mass

• Disease severity

Phase angle

g PhA

Reference values

Bioelectrical vector analysis, BIVA

Aim of the study

• Explore the prevalence of malnutrition using the malnutrition inflammation score (MIS) and Phase Angle (PhA)

• Compare their concordance with the new Global Initiative on Malnutrition (GLIM) criteria for the diagnosis of malnutrition.

Fig. 1

Clinical Nutrition DOI: (10.1016/j.clnu.2018.08.002)

Methods • Design and sample

– Cross sectional study

– Randomly recruited HD unit in United Arab Emirates

• Outcome variables

– MIS, malnourished >10

– Malnutrition assessment based on GLIM criteria

• FFMI for men <17kg/m2 , <15kg/m2

– Phase angle (PhA), derived from BIA analysis

• Statistical analysis

– Independent – test and Mann-Whitney U non parametric test, SPSS 21

– Receiver Operating Characteristic (ROC curves), Medcalc software

Results (1) N = 70 (100%)

Gender: Male 43 (61.4%)

Co morbidities

Diabetes

Hypertension

Cardiovascular Diseases

Others

46 (65.71%)

62 (88.57%)

28 (40.00%)

37 (52.86%)

Dialysis Vintage

< 1 year

1-4 years

> 4 years

5 (7.14%)

35 (50.00%)

30 (42.86%)

Results (2) Variable (N=70)

Mean (SD)

Age (years) 54.61 (12.79)

BMI (Kg/m2) 27.22 (6.48)

PhA ()¶ Phase Angle 4.66 (1.21)

MIS Malnutrition Inflammation Score 9.40 (3.07)

FMI (Kg/m2)Fat Mass Index 10.15 (5.00)

SMM Skeletal Muscle Mass(Kg) 19.67 (6.44)

Fat (Kg) 26.70 (12.57)

Median (IQR)

FFMI (Kg/m2)Fat Free Mass Index 17.09 (3.33)

FFM (Kg)Free Fat Mass 42.92 (15.14)

TBW (L)Total Body Water 32.15 (11.4)

ECW (L)Extracellular Water 15.20 (4.7)

Results (3) Prevalence of Malnutrition

Value N (% of pts)

GLIM criteria

•Stage 1, moderate

•Stage 2, severe

38 (54.29)

12 (31.58)

26 (68.62)

MIS (>10) 34 (48.57)

Concordance of GLIM with MIS (>10) and PhA (5.7o)

Criterion MIS>10 PhA5.7o

Sensitivity (%) 39.47 86.84

Specificity (%) 71.87 35.48

K value (p) 0.202

(0.089)

0.234

(0.029)

AUC 0.691 0.614

Results (4) ROC curve for PhA and MIS according to GLIM

Conclusions

• Malnutrition is highly prevalent in the HD patients studied.

• MIS performed slightly better than PhA in the diagnosis of malnutrition

when using GLIM as a reference

• Both tools may perform equally on a large sample.

• Prioritizing malnutrition screening in this population, and integrating cost-

effective, sensitive and specific tools within routine practice is essential for

this population

Thank you for your attention