Eur J Heart Fail 2008 Grewal 252 9

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BNP and NT-proBNP predict echocardiographicseverity of diastolic dysfunction

Jasmine Grewal a , Robert McKelvie a , Eva Lonn a , Peter Tait a , Jonas Carlsson d ,Monica Gianni e , Christina Jarnert c , Hans Persson b ,

a Population Health Research Institute and McMaster University, Hamilton, Ontario, Canada b Department of Clinical Sciences, Karolinska Institutet, Danderyd Hospital, Stockholm, Sweden

c Department of Cardiology, Karolinska University Hospital, Solna, Swedend Astra Zeneca, R&D, Mlndal, Sweden

e Department of Medicine, University of Insubria, Varese, Italy

Received 1 May 2007; received in revised form 6 November 2007; accepted 28 January 2008

Abstract

Aims: To evaluate the best combination of clinical parameters and brain natriuretic peptide (BNP) or N-terminal pro-BNP (NT-proBNP), to predict diastolic dysfunction (DD) in heart failure with preserved left ventricular ejection fraction (HF-PLEF) as determined by Doppler-echocardiography. Methods and Results: HF patients with EF N 40% in the CHARM Echocardiographic Substudy were included and classified to have normaldiastolic function, or mild, moderate or severe diastolic dysfunction. Plasma BNP and NT-proBNP levels were measured and relevant clinicalcharacteristics recorded. 181 participants were included in this analysis, 72 (40%) had moderate to severe DD. A model including age, sex,BNP, body mass index, history of atrial fibrillation, coronary artery disease, diabetes mellitus, hypertension and left atrial volume was highly predictive of moderate to severe DD; AUC 0.81 (0.73 0.88; p b 0.0001). Similarly, substitution of BNP with NT-proBNP resulted in an AUC0.79 (0.72 0.87; p b 0.0001). In these models; BNP N 100 pg/ml (OR 6.24 CI 2.42 16.09, p =0.0002), history of diabetes (OR 3.52 CI 1.43 8.70, p=0.006) and NT-proBNP N 600 pg/ml (OR 5.93 CI 2.21 15.92, p=0.0004), history of diabetes mellitus (OR 2.75 CI 1.12 6.76, p=0.03) respectively remained independent predictors of DD in HF-PLEF.Conclusions: Natriuretic peptides were the strongest independent predictors of DD, as determined by Doppler-echocardiography, in HF-PLEF. 2008 European Society of Cardiology. Published by Elsevier B.V. All rights reserved.

Keywords: Natriuretic peptides; Diastolic dysfunction; Heart failure; Diagnosis

1. Introduction

Heart failure (HF) is a growing worldwide epidemic andis associated with substantial morbidity and mortality. Left ventricular systolic dysfunction is often considered to be themain abnormality in HF. However, up to 50% of patientswith HF have a preserved left ventricular ejection fraction

(HF-PLEF); suggesting that isolated diastolic dysfunction(DD) is the pathophysiological mechanism underlying theclinical syndrome of HF in these patients [1]. Recent datasuggest that mortality rates among individuals with HF-PLEF are similar to those with HF and systolic dysfunction[2]. Bhatia et al. recently found that among patients withheart failure, the one year mortality and hospitalisation for HF did not differ among those with an EF N 50% vs. EFb 40% [3]. Interestingly, Owan et al. observed that the pre-valence of HF-PLEF has increased over a 15 year period, andthe rate of death related to this entity has not decreased [4].

European Journal of Heart Failure 10 (2008) 252 259www.elsevier.com/locate/ejheart

Corresponding author. Department of Cardiology, Danderyd Hospital,SE-182 88 Stockholm, Sweden. Tel.: +46 8 6556849; fax: +46 8 6226810.

E-mail address: [email protected] (H. Persson).

1388-9842/$ - see front matter 2008 European Society of Cardiology. Published by Elsevier B.V. All rights reserved.doi:10.1016/j.ejheart.2008.01.017
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This is in contrast to HF secondary to depressed EF, where adecrease in mortality over time was observed. This is likelyrelated to evolving therapies for systolic heart failure, andunderscores the need for improved diagnosis and the de-velopment of new therapies for HF-PLEF. Furthermore, evenin the absence of clinical HF, DD is associated with increasedrates of future hospitalizations, development of HF, and all-cause mortality [5]. Worsening stages of DD on echocar-diography are associated with incremental risk of adverseoutcomes including the development of clinical HF [6].Accurately diagnosing DD could possibly lead to improvedtreatments and may have substantial health care implications, both from a clinical and resource utilization perspective.Moreover, reproducible, widely applicable and prognosti-cally meaningful approaches to defining DD are important for clinical trials where the use of complex methods toassess diastolic function may be difficult to standardize andimplement.

In routine clinical practice Doppler echocardiography isthe method of choice to diagnose DD [6]. Numerous al-gorithms have been proposed, most based on transmitralDoppler patterns. However, transmitral Doppler derived in-dices of diastolic function are dependent on loading con-ditions, and accurate measurements are operator dependent.Tissue Doppler imaging is a newer technique that can beused in combination with transmitral Doppler to determinethe presence and severity of DD [6]. However, this assess-ment of DD is more complex and requires expert interpreta-tion. Many parameters have been shown to be associatedwith DD, including echocardiographic measurements, var-ious clinical characteristics, increased left atrial (LA) volumeand elevated levels of B-type natriuretic peptide (BNP) and N-terminal (NT)-proBNP [7 12]. Identifying simple clinicaland/or biochemical and/or echocardiographic measurementsthat can reliably identify the presence and severity of DD is particularly important for patients with HF-PLEF.

Therefore, we aimed to determine the best set of clinical parameters, LA volume and brain natriuretic peptide (BNP)or N-terminal pro-BNP (NT-proBNP) that could accurately predict DD, as evaluated by echocardiography. If indeed asimple set of such parameters could be shown to be stronglyassociated with DD on echocardiography, use of such pa-rameters could help circumvent the need for detailed, diffi-

cult, and costly echocardiographic assessments (in situationswhere echo is not readily available) to determine the pres-ence of prognostically important DD.

2. Methods

2.1. Study design

We conducted a cross-sectional study in patients with HF-PLEF (EF N 40%), in which LV diastolic function wasevaluated using Doppler echocardiography. In addition, simple clinical characteristics were recorded and LA volume andnatriuretic peptides were measured. The current report is part

of the multicenter echocardiographic substudy of the in-ternational multicenter randomised controlled CHARM-Preserved trial [13,14] .

2.2. Study organization

All investigators participating in the CHARM-Preservedstudy were invited to participate in the echocardiographicsubstudy. Danderyd University Hospital and HamiltonHealth Sciences were the Core Laboratories responsible for the protocol, training of sites, and reading study echocardio-grams. Echocardiograms were recorded at the investigator'ssite and shipped to one of the Core Laboratories, with onesingle reader at each site. Inter-reader variability for 10 dias-tolic function measurements was assessed in 25 patients be-tween the 2 core laboratories using intra-class correlationcoefficients (median ICC 0.784, range 0.667 0.954). All NT-proBNP and BNP measurements were performed at the

Western Infirmary, Glasgow, Scotland.

2.3. Ethical considerations

The echocardiographic substudy was approved by theethical review boards of all participating centres and all patients provided written informed consent. The study wasconducted according to the rules outlined in the Helsinkideclaration.

2.4. Patients

Patients participating in the CHARM-Preserved study[13] were asked to participate in the echocardiographicsubstudy (CHARMES) [14]. The inclusion (NYHA ClassII IV, EF N 40%) and exclusion criteria of the substudy werethe same as the main study. Additional exclusion criteriafor the CHARMES substudy were a poor quality echocar-diographic study, the presence of moderate to severe mitraland/or aortic regurgitation, and a prosthetic mitral valve.Those participants in the original CHARMES study who didnot have both BNP and NT-proBNP, pulmonary vein and E/ A Valsalva measurements were also excluded from thisanalysis.

2.5. Measurement of diastolic function

2.5.1. Doppler echocardiographyEchocardiographic assessment was defined as the gold

standard for the determination of DD in this study and themeasurements performed are described in detail in the orig-inal CHARMES paper [14].

2.5.2. Echocardiographic classification of diastolic functionThe classification of diastolic function on echocardio-

graphy was defined a priori using the algorithm outlined inFig. 1 adapted from Redfield et al [6]. The classificationincluded the following categories: 1) normal; 2) relaxation

253 J. Grewal et al. / European Journal of Heart Failure 10 (2008) 252 259


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abnormality (mild dysfunction); 3) pseudonormal (moderatedysfunction); and 4) restrictive abnormality (severe dysfunc-tion). Two investigators (HP, JG) blinded to patients' clinicalcharacteristics performed this assessment. Relaxation and re-strictive abnormalities were assessed by mitral inflow parame-ters and the classification was based on the E/A abnormality.To distinguish pseudo normal from normal diastolic func-tion, two of the measures outlined in the algorithm had to be abnormal. In patients with atrial fibrillation, decelerationtime was used for classification to abnormal relaxation or restrictive diastolic dysfunction, whereas pulmonary systolic/ diastolic peak velocity ratio [15,16] was used to assess for pseu-donormal diastolic dysfunction.

2.6. Left atrial volume

LA volume was calculated using the area/length method as previously described [17]. The area was traced and length

measured in two orthogonal planes, the apical4 chamber and 2chamber views, and was indexed to body surface area (LAVI).Abnormal and enlarged LAVI was defined as N 28 ml/m 2 [17].

2.7. NT-proBNP and BNP

Blood for NT-proBNP and BNP was obtained at the timeof echocardiography. Plasma NT-proBNP was determinedusing the Elecsys proBNP sandwich immunoassay on anElecsys 2010 (Roche Diagnostics, Basel, Switzerland). Twocut offs for NT-proBNP were selected before the analyses,300 pg/ml and 600 pg/ml, with a value of less than 300 pg/mlshown to be optimal for ruling out HF [18]. BNP was

determined using the Shionoria immunoradiometric assay kit [19]. These cut off values determined to be optimal for rulingout HF were selected to test the most conservativeassociation with the presence of HF-PLEF.

2.8. Clinical variables

Simple clinical variables were selected for inclusion in themodels to assess prediction of severity of DD on echocardiog-raphy: age, sex, body mass index (BMI), heart rate, creatinine,medical history of atrial fibrillation, coronary artery disease,diabetes and hypertension. These clinical factors have all beenshown to be associated with DD [7 12,20,21] .

2.9. Statistical analysis

For the assessment of clinical parameters associated withechocardiographic DD, diastolic function on echocardiogra-

phy was grouped into normal/mild DD and moderate/severeDD. This was done as moderate and severe DD were pre-dictive of adverse outcomes (death and hospitalization for HF) in CHARMES [14], while mild DD and normal diastolicfunction were not. Similarly, other studies have found overallgood outcomes in individuals with normal diastolic functionand in those with mild DD [6].

The model building process proceeded in three steps.First, we did a univariate screen of the predictor variables toexamine their relationship with the outcome. Second, weused best subset selection with Mallow's Cp as the selectioncriteria, to choose the minimal predicting combination of predictors. This ensured that the impact of each predictor

Fig. 1. Algorithm for echocardiographic classification of diastolic function (adapted from [6] E , peak early diastolic transmitral flow velocity; A, peak latediastolic transmitral flow velocity; E/A reversal , E/A E/A during valsalva 0.5; AR, pulmonary venous atrial reversal peak flow velocity; Adur , duration of Awave; ARdur , peak pulmonary venous atrial reversal flow velocity duration; S , peak systolic pulmonary venous flow velocity; D , peak diastolic pulmonaryvenous flow velocity.



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was evaluated individually as well as in multivariate in-teractions with the other predictors. Age and sex were alsokept throughout in the models as they were considered to beimportant control variables on the basis of subject matter.The results of the logistic regression models were reported asodds ratios and 95% confidence intervals (CI). The modelswere compared in terms of discriminatory ability, using com- puted Areas under the Receiver Operating Curve (AUC).Third, we used the likelihood ratio testing to evaluate clin-ically important hypotheses while keeping the results of thesecond step in mind (model 7 contains all the predictorschosen in the second step). The relationship between con-tinuous predictors was examined with the Pearson correla-tion coefficient and between a categorical and a continuous predictor with t -test analysis. These analyses were thenredone after exclusion of patients with atrial fibrillation. A p -value b 0.05 was considered statistically significant.

3. Results

3.1. Study participants

A total of 312 patients were included in the echocardio-graphic substudy of CHARM. Of these, 131 were excluded,as they did not have both BNP and NT-proBNP measure-ments and a full echocardiographic study, therefore, 181 patients were included in this analysis. This represents 6% of the patients in the CHARM-Preserved trial [13] and 58% of participants in the original CHARMES substudy [14]. Thecharacteristics of the participants in the normal/mild DD andmoderate/severe DD groups are shown in Table 1 . Those participants with moderate/severe diastolic dysfunctiontended to be older with higher rates of diabetes, coronaryartery disease, and atrial fibrillation. The most commonaetiologies of HF were ischaemic heart disease, hypertensionand idiopathic cardiomyopathy. The proportions of pa-tients with of NYHA Class II IV HF were similar betweennormal/mild and moderate/severe DD groups. Participantswith moderate/severe DD had higher levels of BNP/NT- proBNP and increased LAVI.

3.2. Systolic and diastolic function

The echocardiogram was performed 547 days (median)after randomisation into the original CHARM-Preservedtrial. There were 56 patients classified as having normal di-astolic function on echocardiography, and DD was found in125 (69%). Mild DD (impaired relaxation) was present in53 (30%), moderate DD in 55 (30%) and severe DD in 17(9%) patients. The mean LVEF measured in the study was55%. Ejection fractions (EF) in the mild, moderate andsevere DD groups were 54 8%, 57 10% and 55 9%respectively (p=NS). Similarly, EF was 547% in thoseidentified as having normal diastolic function. Therefore, it is unlikely that symptoms could be attributed to systolicdysfunction.

3.3. Predictors of diastolic dysfunction

Age and sex adjusted univariate variables associated withmoderate/severe DD are shown in Table 2. NT-proBNPN 300 pg/ml, NT-proBNP N 600 pg/ml, BNP N 100 pg/ml,LAVI, history of atrial fibrillation, and diabetes were allsignificant predictors of moderate/severe DD. NT-proBNPN 600 pg/ml and BNP N 100 pg/ml were the strongest pre-dictors. Creatinine was also significantly associated withmoderate/severe DD in univariate analysis and had a modest but significant association with BNP ( r =0.30, p =0.0004)

Table 1Characteristics of HF-PLEF Patients with normal diastolic function or milddiastolic dysfunction vs. those with moderate or severe diastolic dysfunction

Characteristic Normal/milddiastolic dysfunction N (%) n =109

Moderate/severediastolic dysfunction N (%) n = 72

SexMale 71 (65) 47 (65)Female 38 (35) 25 (35)

History of hypertension 75 (69) 43 (60)History of diabetes

mellitus28 (26) 29 (40)

History coronaryartery disease

83 (76) 59 (82)

History myocardialinfarction

56 (51) 41 (57)

History of atrialfibrillation

18 (16) 27 (38)

Atrial fibrillationat time of echo

7 (6) 16 (22)

Medications at baseline

ACE-Inhibitors 21 (19) 15 (21)Beta Blockers 70 (64) 41 (57)Aspirin 76 (70) 51 (71)Statins 68 (62) 37 (51)Diuretics 73 (67) 55 (76)

Aetiology of heart failureIschaemic heart disease 67 (62) 51 (71)Idiopathic 12 (11) 4 (6)Hypertension 19 (17) 9 (12)Aortic valve disease 2 (2) 1 (1)Excess alcohol intake 1 (1) 0 (0)Atrial fibrillation 5 (5) 6 (8)

NYHA classII 65 (60) 43 (60)III 43 (39) 28 (39)

IV 1 (1) 1 (1)

Characteristic Mean (SD) Mean (SD)

Age (years) 65 (12) 70 (10)Baseline systol ic BP 134.5 (19.4) 134.6 (19.2)Baseline diastolic BP 77 (11.2) 74 (11.2)Heart rate (beats/min) 68 (12) 67 (13)BMI (kg/m 2) 30 (5) 29 (6)Ejection fraction (%) 54 (7) 57 (8)LA volume index (ml/m2) 36 (10) 44 (13)BNP (pg/ml) 60 (84) 165 (227) NT-proBNP (pg/ml) 376 (638) 1419 (3423)Creatinine (umol/L) 93 (30) 110 (50)

NYHA = New York Heart Association Class of heart failure, BMI = body

mass index, LA = left atrium.



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and NT-proBNP ( r =0.20, p = 0.02). We did not find animportant association of medications or heart rate with BNP(r = 0.07 p =0.3) or NT-proBNP ( r =0.01 p =0.9), and as aresult these were not included in the final models.

The multivariate models (discussed below) were evalu-ated with and without creatinine (in addition to other listedvariables) and yielded similar results. Creatinine was not significantly related to moderate and severe diastolic dys-function in these complimentary analyses ( p =0.58 and p =0.68, respectively). Unfortunately, only 132 of the total 181 patients (74%) had a documented creatinine level, as it wasnot a mandatory test according to the CHARM protocol.Without accounting for the missing creatinine values, we cansurmise that the multivariate interactions of creatinine andthe other model predictors does not change the important association of natriuretic peptides and diastolic dysfunctionas discussed below. All of these analyses were also repeated

after the exclusion of patients with atrial fibrillation, and theresults remained the same.

Various models were evaluated to determine those most strongly associated with moderate/severe DD as outlined inTable 3 . Model 7, which included the most clinically relevant variables, BNP and LAVI was most predictive for moderate/ severe DD on echocardiography, AUC=0.81 (95% CI, 0.73to 0.88) (Table 4 ). Models 6 and 5 had comparable AUCs of 0.80 and 0.78 respectively ( Table 4 ). Likelihood ratio testingshowed that model 7 was only marginally more likely to predict the presence of significant DD than model 6 (LR 4.2df 1, p =0.04). Among the variables included in model 7, the

Table 2Univariate predictors of moderate/severe diastolic dysfunction

Variable a OR (95% CI) Area under ROC-curve (CI)

p

NT-proBNP N 600 pg/ml 7.4 (3.4 16) .74 (.66 .81) b 0.0001 NT-proBNP N 300 pg/ml 2.2 (1.1 4.4) .67 (.58 .75) 0.01

BNPN

100 pg/ml 4.9 (2.3

10.4) .72 (.64

.80) b

0.0001LAVI N 28 ml/m 2 4.4 (1.2 15.7) .68 (.59 .77) 0.001History of atrial fibrillation 2.5 (1.2 5.1) .67 (.59 .75) 0.001History of diabetes mellitus 2.2 (1.2 4.8) .66 (.57 .74) 0.001History of coronary

artery disease1.1 (0.5 2.4) .62 (.53 .70) 0.35

History of hypertension 1.0 (0.5 1.9) .62 (.54 .70) 0.21BMI N 30 kg/m 2 0.8 (0.4 1.4) .62 (.54 .70) 0.29Heart rate (beats/min) 0.99 (0.96 1.01) .55 (.46 .63) 0.30ACE-Inhibitor therapy 1.1 (0.53 2.3) .51 (.42 .59) 0.79Beta blocker therapy 0.74 (0.40 1.4) .53 (.45 .62) 0.32Diuretic therapy 1.6 (0.8 3.1) .55 (.46 .63) 0.17Creatinine ( mol/L) b 3.8 (1.4 10.1) .63 (.53 .73) 0.004

NT-proBNP = N-terminal pro brain natriuretic peptide, BNP = brainnatriuretic peptide, LAVI = left atrial volume index, BMI = bodymass index.

a Each variable adjusted for age, sex. b For each 100 umol/L increase.

Table 3 Nested models for predicting moderate/severe diastolic dysfunction

Model Xs

1 Age, Sex, BNP/NT-proBNP2 Age, Sex, BNP/NT-proBNP, LAVI3 Age, Sex, BNP/NT-proBNP, LAVI, AF4 Age, Sex, BNP/NT-proBNP, LAVI, AF, Diabetes5 Age, Sex, BNP/NT-proBNP, LAVI, AF, Diabetes, CAD6 Age, Sex, BNP/NT-proBNP, LAVI, AF, Diabetes, CAD, BMI7 Age, Sex, BNP/NT-proBNP, LAVI, AF, Diabetes, CAD, BMI, HTN

BNP = brain natriuretic peptide N 100 pg/ml, NT-proBNP = N terminal pro brain natriuretic peptide N 600 pg/ml, LAVI = left atrial volume indexN 28 ml/m 2 , AF = atrial fibrillation, CAD = coronary artery disease, BMI = body mass index N 30 kg/ m 2 , HTN = hypertension.

Table 4Models predicting moderate/severe diastolic dysfunction with BNPN 100 pg/ml

Variable OR (95% CI) p Area under ROC curve (CI)

Model 1 0.72 (0.64 0.80)Model 2 0.76 (0.67 0.83)

Model 3 0.76 (0.68

0.80)Model 4 0.77 (0.69 0.85)Model 5 0.78 (0.71 0.86)Model 6 0.80 (0.72 0.87)Model 7 0.81 (0.73 0.88)

Age 1.02 (0.97 1.06) 0.51Sex 1.25 (0.55 2.83) 0.60BNP 6.24 (2.42 16.09) 0.0002LAVI 2.94 (0.74 11.72) 0.12BMI 0.53 (0.21 1.29) 0.16AF 2.20 (0.79 6.14) 0.13CAD 2.44 (0.80 7.46) 0.18Diabetes 3.52 (1.43 8.70) 0.006HTN 0.42 (0.18 0.98) 0.05

BNP = brain natriuretic peptide N 100 pg/ml, LAVI = left atrial volume indexN 28 ml/m 2 , AF = atrial fibrillation, CAD = coronary artery disease, BMI = body mass index N 30 kg/m 2 , HTN = hypertension.

Table 5Models predicting moderate/severe diastolic dysfunction with NT-proBNP

Variable OR (95% CI) p Area under ROC curve (CI)

Model 1 0.74 (0.66 0.81)Model 2 0.76 (0.66 0.84)Model 3 0.76 (0.68 0.84)Model 4 0.76 (0.68 0.84)

Model 5 0.77 (0.69 0.85)Model 6 0.78 (0.70 0.86)Model 7 0.79 (0.72 0.87)

Age 1.01 (0.97 1.06) 0.53Sex 1.03 (0.46 2.32) 0.94 NT-proBNP 5.93 (2.21 15.92) 0.0004LAVI 3.32 (0.85 12.95) 0.08BMI 0.54 (0.22 1.32) 0.18AF 1.43 (0.49 4.16) 0.51CAD 1.60 (0.54 4.72) 0.39Diabetes 2.75 (1.12 6.76) 0.03HTN 0.50 (0.22 1.32) 0.11

NT-proBNP = N-terminal pro brain natriuretic peptide N 600 pg/ml, LAVI =left atrial volume index N 28 ml/m 2 , AF = atrial fibrillation, CAD = coronaryartery disease, BMI = body mass index N 30 kg/m 2 , HTN = hypertension.



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only independent predictors of moderate/severe DD wereBNP N 100 pg/ml (OR 6.24 CI 2.42 16.09, p =0.0002) and ahistory of diabetes (OR 3.52 CI 1.43 8.70, p =0.0006).

Models with NT-proBNP N 600 pg/ml were also exam-ined (Table 5 ). Models with NT-proBNP N 300 pg/ml werealso examined but the results are not shown since theywere found to be less predictive. In Model 7, which includedthe most clinically relevant variables, NT-proBNP and LAVIwas most predictive of moderate/severe DD, AUC of 0.79(Table 5 ). The strongest independent predictors of moderate/ severe DD in this model were NT-proBNP N 600 pg/ml(OR 5.93 CI 2.21 15.92, p =0.0004) and a history of diabetes (OR 2.75 CI 1.12 6.76, p =0.03). The other modelshad comparable AUCs ( Table 5 ) and likelihood ratio testingimplied that model 7 was no more predictive of DD than theother simpler models (results not shown).

4. Discussion

Our study is the first to identify the best combination of easily measured clinical parameters associated with clini-cally important echocardiographic DD in the setting of HF-PLEF. Our results demonstrate that high levels of natriuretic peptides are the most powerful in diagnosing significant DDin the setting of HF-PLEF as compared to other clinicalvariables and LAVI. Natriuretic peptides can therefore beused to provide objective evidence of prognostically impor-tant DD in HF-PLEF. This would be especially useful inclinical settings where detailed and complex echocardio-graphic assessments are not possible and in clinical trials,where objective, standardized and reproducible identifica-tion of prognostically important DD is needed.

Our findings are certainly complimentary to a recent con-sensus statement published by Paulus et al [22]. This groupnicely outlined an algorithm for the diagnosis of HF withnormal ejection fraction. The emphasis is on using a combination of elevated filling pressures as determined by tis-sue Doppler echocardiography and natriuretic peptides (BNPN 200 pg/ml and NT-proBNP N 220 pg/ml). If one or the other is not available, then the physician is advised to look at mitralinflow velocity parameters, pulmonary vein flow parameters,left ventricular mass index, left atrial volume or the presenceof atrial fibrillation. Our approach was a little different as we

set out to determine the best clinical predictors of HF-PLEF,in an effort to provide an approach for the many medicalcentres where detailed echocardiographic assessment of di-astolic dysfunction is not possible. Like Paulus et al, we usedfairly conservative cut-off points for BNP (values designatedas being most sensitive rather than specific , as per the diagnosis of systolic heart failure) in determining theassociation with HF-PLEF, and still found that natriuretic peptides were most valuable in predicting moderate/severeDD in HF-PLEF. Similarly, other studies have also usedmore sensitive values for the diagnosis of HF-PLEF. For ex-ample, in a study of patients with normal systolic function onechocardiogram, plasma BNP 57 pg/mL detected the 28

patients with isolated abnormal diastolic function with 100% positive predictive value [23]. Another study which eval-uated 294 patients with normal left ventricular function,reported that plasma BNP 62 pg/mL had a sensitivity of 85% and a specificity of 83% for the diagnosis of diastolicdysfunction [24]. We have confirmed the importance of na-triuretic peptides i0n the diagnosis of HF-PLEF and haveshown that the specificity can be increased with evaluationof other parameters. The recommendations by Paulus et al.suggest that increased left atrial volumes, atrial fibrillationor increased left ventricular mass in addition to natriuretic peptides would increase the likelihood of HF-PLEF. Our results suggest that the presence of diabetes would also in-crease the likelihood of HF-PLEF. These findings should prove useful for clinicians in the diagnosis of prognosticallyimportant HF-PLEF.

Some other important observations have also been madein this group of patients with HF-PLEF. Progressive NYHA

Class symptoms were present in a large portion of patientswith mild diastolic dysfunction. This was independent of ejection fraction which remained preserved across the spec-trum of diastolic function. Also, ejection fraction was sim-ilar in those with NYHA Class II, III, and IV symptoms (559%, 54 8%, 57 4% respectively). This observation may beanalogous to the presence of advanced heart failure symp-toms that seem to be out of proportion to the degree of LVdepression in low EF HF. Also, in contrast to the low EF HF population, this group of patients with HF-PLEF had higher BMI measurements. A lower BMI in systolic HF studiesmay be attributed to cardiac cachexia occurring as a result of advanced (NYHA IV) symptoms and more importantlyreduced cardiac output. The prevalence of advanced symp-toms, and then presumably cardiac cachexia, was approxi-mately 2% in the overall CHARM cohort and 1% in our analysis, less than that seen in other HF studies. We alsoconsidered that patients with an increased BMI and symp-toms of dyspnoea were incorrectly diagnosed as having HF,resulting in an increase in BMI in the study group. Thisis a concern, however, we could not find an associa-tion between high BMI and normal and mild diastolic dys-function ( Table 2 ).

We demonstrated that both BNP and NT-proBNP areelevated in individuals with moderate and severe DD as com-

pared to normal and mild DD. BNP N 100 pg/ml and NT-proBNP N 600 pg/ml alone are both strongly associated withclinically important DD. These results are consistent withthe known pathophysiology of HF-PLEF where a variety of clinical conditions can ultimately lead to an increased left ventricular end diastolic wall stress. Iwanaga et al. have dem-onstrated that BNP levels correlate very closely with left ventricular end diastolic wall stress in the setting of HF-PLEF ( r 2 =0.887) making it a good surrogate marker of worsening DD [25]. Moreover, BNP was shown to strong-ly correlate with pulsed wave Doppler and tissue Doppler parameters in patients with HF-PLEF [26]. Similarly, LAVIvalues were larger in the moderate/severe DD group and



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strongly predicted DD in univariate analysis. LAVI isincreasingly recognized as a relatively load-independent marker of LV filling pressures and has been shown to in-crease with worsening DD [27] and correlates closely with NT-proBNP [28]. Natriuretic peptides and LAVI both reflect increases in wall stress. However, our study demonstratesthat BNP N 100 pg/ml and NT-proBNP levels N 600 pg/mlwere more strongly associated with DD in HF-PLEF thanLAVI.

Several previous studies have demonstrated the utility of both BNP and NT-proBNP used in isolation in predictingDD in HF-PLEF, but have not evaluated the use of modelsincluding easily measured clinical variables and natriuretic peptides [7 10]. Our study shows that a history of diabetes isassociated with HF-PLEF. This may be related to impairment of myocardial microcirculation, reduction of coronary flowreserve, hyperglycaemia, poor metabolic control, endothelialdysfunction and myocardial fibrosis. Several previous stud-

ies have shown LV diastolic filling abnormalities in peoplewith diabetes, thought to be related to LV remodelling andhypertrophy independent of other concomitant risk factors,such as hypertension and epicardial coronary artery disease[29,30] . Among these, the Framingham study found in-creased rates of HF in people with diabetes and suggestedthat this association was independent of other CHD risk factors [30].

5. Study limitations

The echocardiography protocol used in our study did not include tissue Doppler imaging for determining the degree of DD due to the multicenter design of the trial and inherent variabilities in tissue Doppler assessment. However, in a previous study using a similar combination of mitral inflowand pulmonary vein Doppler measurements, 93% of patientswith HF-PLEF were found to show evidence of DD [31],making us confident of the classification of DD for the purposes of our paper. We included patients in atrial fibril-lation at the time of the echocardiogram. We used previously published criteria for assessment of DD in atrial fibrilla-tion; moreover there were few patients in atrial fibrillationat the time of echocardiography. Furthermore, our resultswere similar when participants with atrial fibrillation were

excluded.

6. Conclusions

HF-PLEF is a common entity and is most commonlyattributed to DD. Advanced degrees of DD are associatedwith worse outcomes. Currently, the presence and severityof DD is generally ascertained with the use of complexechocardiographic methods using multiple often challengingmeasurements. These are difficult to standardize and per-form reproducibly in routine clinical practice. In such clin-ical settings, measuring BNP or NT-proBNP, using availablestandardized assays, provides an alternate simple method of

identifying DD and grading its severity. Combining themeasure of BNP or NT-proBNP with a simple clinical pa-rameter such as a history of diabetes mellitus can further helpin predicting moderate/severe DD in HF-PLEF.

Acknowledgments

We gratefully acknowledge the contributing 48 sites inCanada, Iceland, Malaysia, Russia, Sweden and USA, thecore laboratory teams, and the Executive Committee of theCHARM program for important contributions to the study.

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