Value of Adding Natriuretic Peptides and Electrocardiographic Findings to Assess the Presence of...

Value of Adding Natriuretic Peptides and ElectrocardiographicFindings to Assess the Presence of Cardiac Dysfunction in Patients

‡ 80 Years of Age

Bert Vaes, PhDa,e,*, Benoit Boland, PhDb, Christophe Scavée, PhDc, Séverine Henrard, MSca,Pierre Wallemacq, PhDd, Gijs Van Pottelbergh, MDa,e, Catharina Matheï, PhDa,e, Agnes Pasquet, PhDc,

Jean-Louis Vanoverschelde, PhDc, Nawel Rezzoug, MDc, Niko Speybroeck, PhDa,and Jan Degryse, PhDa,e

Studies estimating the added value of natriuretic peptide levels and electrocardiographic

aInstitute of Hement of CardiologUniversitaires SaiBelgium; and eDLeuven,Leuven,Bmanuscript receive

The BELFRAditional grant froLouvain is the sucharged with devuniversity by colle

See page 10 fo*CorrespondinE-mail addres

0002-9149/13/$ -http://dx.doi.org/1

findings beyond all relevant clinical information to identify cardiac dysfunction remainscarce. The aim of this study was to assess the presence of clinically relevant cardiacdysfunction in an unselected population of subjects aged ‡80 years. A cross-sectionalanalysis using an “intention-to-diagnose” strategy was performed within the BELFRAILstudy (n [ 567). Baseline B-type natriuretic peptide and N-terminal proeB-type natri-uretic peptide levels were determined and echocardiography was performed at subjects’homes. Logistic regression analysis and classification and regression tree analysis were usedas complementary analytic tools. Cardiac dysfunction was present in 17% of subjectswithout and 31% of subjects with chronic atrial fibrillation (AF) or pacemaker. In subjectswithout chronic AF or pacemaker, the clinical model showed a C-statistic of 0.79 (95%confidence interval 0.74 to 0.85). The combination of natriuretic peptides with normalresults on electrocardiography increased, only marginally, the C-statistic. In subjects withchronic AF or pacemaker, the clinical model showed a very high C-statistic of 0.90 (95%confidence interval 0.82 to 0.98). Classification and regression tree analysis showed that anadditional 58 subjects (13%) were correctly classified using natriuretic peptides and elec-trocardiographic findings among those without chronic AF or pacemaker. Of participantswith chronic AF or pacemaker, >90% were correctly classified. In conclusion, in a largepopulation-based sample of patients aged ‡80 years, the clinical model possessed highaccuracy to identify cardiac dysfunction in daily practice. Among subjects without chronicAF or pacemaker, a larger number were correctly classified by integrating natriureticpeptides and electrocardiographic findings in the strategy. � 2013 Elsevier Inc. All rightsreserved. (Am J Cardiol 2013;-:-e-)

Natriuretic peptide levels and electrocardiographic (ECG)findings have been generally accepted as valuable tools for thediagnosis of heart failure.1 Moreover, their mainly exclu-sionary characteristics have also been demonstrated in unse-lected elderly populations for detecting cardiac dysfunction.2

However, previous studies have calculated the diagnosticcharacteristics of a single natriuretic peptide or ECG test asthough the diagnostic procedurewere a univariate activity and

alth and Society and bDepartment of Geriatrics, cDepart-y, and dLaboratory of Analytical Biochemistry, Cliniquesnt-Luc, Université Catholique de Louvain, Brussels,epartment of General Practice, Katholieke Universiteitelgium.Manuscript received September 15, 2012; revisedd and accepted December 17, 2012.IL study (B40320084685) was supported by an uncon-m Fondation Louvain, Brussels, Belgium. Fondationpport unit of Université Catholique de Louvain and iseloping the educational and research projects of thecting gifts from corporations, foundations, and alumni.r disclosure information.g author: Tel: þ32-2-7643460; fax: þ32-2-7643470.s: [email protected] (B. Vaes).

see front matter � 2013 Elsevier Inc. All rights reserved.0.1016/j.amjcard.2012.12.055

previous clinical knowledge had no clinical impact.3 Studiesestimating the added value of natriuretic peptide levels andECG findings in addition to medical history and clinicalexamination, which is representative of current clinicalpractice, remain scarce.4 Therefore, a cross-sectional “inten-tion-to-diagnose” analysis5 was performed within theBELFRAIL cohort to determine the added value of natriureticpeptide levels and ECG findings beyond medical history, thepresence of symptoms and signs, and routine laboratory teststo identify cardiac dysfunction. Thus, the goal of this studywas to investigate, under real-life conditions, the possible gainof implementing natriuretic peptide testing and electrocardi-ography to assess the presence of clinically relevant cardiacdysfunction as a risk factor for symptomatic heart failure anddeath in an unselected population of subjects aged�80 years.

Methods

The BELFRAIL study is a prospective, observational,population-based cohort study of subjects aged �80 years in3 well-circumscribed areas of Belgium. All participants inthe study gave informed consent, and the Biomedical EthicsCommittee of the Medical School of Université Catholique

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Table 1Background variables relevant to the presence of cardiac dysfunction in 548 subjects with or without chronic atrial fibrillation or pacemaker

Variable No AF or Pacemaker AF or Pacemaker

No CardiacDysfunction(n ¼ 379)

CardiacDysfunction(n ¼ 78)

p Value* No CardiacDysfunction(n ¼ 63)


p Value*

Age (yrs) 84.5 � 3.6 84.9 � 3.8 0.27 84.7 � 3.0 86.8 � 3.5 0.003Men 133 (35%) 32 (41%) 0.32 24 (38%) 15 (54%) 0.17Institutionalized 38 (10%) 10 (13%) 0.47 4 (6.3%) 3 (11%) 0.48Noncardiovascular co-morbiditiesThyroid dysfunction 26 (6.9%) 11 (14%) 0.083 7 (11%) 4 (14%) 0.67Asthma 18 (4.8%) 3 (3.9%) 0.74 4 (6.3%) 1 (3.6%) 0.60Chronic obstructive pulmonary disease 37 (9.8%) 10 (13%) 0.40 12 (19%) 2 (7.1%) 0.095Osteoarthritis 220 (59%) 42 (55%) 0.49 35 (57%) 16 (57%) 0.98Osteoporosis 86 (23%) 18 (24%) 0.87 14 (23%) 3 (11%) 0.17Malignancies 77 (20%) 16 (21%) 0.94 12 (19%) 7 (25%) 0.52

Cardiovascular co-morbiditiesHypertension 262 (69%) 56 (73%) 0.55 47 (75%) 19 (68%) 0.51Hyperlipidemia 173 (46%) 31 (41%) 0.37 28 (45%) 8 (30%) 0.16Diabetes 70 (19%) 12 (16%) 0.54 12 (19%) 8 (29%) 0.32Angina pectoris 56 (15%) 13 (17%) 0.65 14 (23%) 6 (21%) 0.90Myocardial infarction 35 (9.3%) 12 (16%) 0.16 9 (14%) 3 (11%) 0.65Cardiomyopathy 24 (6.4%) 10 (13%) 0.11 11 (18%) 8 (30%) 0.28Transient ischemic attack 37 (10%) 4 (5.3%) 0.12 8 (13%) 5 (19%) 0.48Cerebrovascular accident 27 (7.2%) 8 (11%) 0.37 5 (7.9%) 5 (18%) 0.23Peripheral arterial disease 28 (7.4%) 8 (10%) 0.38 5 (7.9%) 8 (30%) 0.030Episode of decompensated heart failure 22 (5.8%) 15 (20%) 0.005 17 (27%) 4 (14%) 0.15Reported valvular heart disease 62 (17%) 32 (42%) <0.001 25 (40%) 17 (61%) 0.064Percutaneous transluminal coronary angioplasty or stent 31 (8.2%) 8 (11%) 0.52 7 (11%) 1 (3.6%) 0.16Coronary surgery 16 (4.2%) 9 (12%) 0.051 8 (13%) 3 (11%) 0.79Valvular surgery 8 (2.1%) 3 (4.0%) 0.34 6 (9.5%) 2 (7.1%) 0.72Arterial surgery 16 (4.2%) 4 (5.3%) 0.68 3 (4.8%) 4 (14%) 0.20

Cardiovascular medicationsCardiac glycosides 4 (1.1%) 1 (1.3%) 0.37 9 (14%) 9 (32%) 0.082Diuretics 170 (45%) 43 (56%) 0.082 34 (54%) 20 (71%) 0.11b blockers 146 (39%) 37 (48%) 0.14 33 (52%) 16 (57%) 0.68Calcium antagonists 86 (23%) 18 (23%) 0.91 20 (32%) 11 (39%) 0.49Angiotensin-converting enzyme inhibitors or angiotensin

II receptor blockers143 (38%) 39 (51%) 0.043 32 (51%) 16 (57%) 0.58

Lipid modifiers 130 (34%) 22 (29%) 0.31 18 (29%) 7 (25%) 0.73

Data are expressed as mean � SD for continuous variables or as number (percentage) for categorical variables.* Student’s t test.

2 The American Journal of Cardiology (www.ajconline.org)

de Louvain in Brussels approved the study. The studydesign, methods, recruitment of participants, and charac-teristics of the cohort were previously described in detail.6

Briefly, 29 general practitioner (GP) centers were asked toinclude patients aged �80 years. No other inclusion criteriawere specified, and only 3 exclusion criteria were used:known severe dementia (Mini-Mental State Examinationscore <15), receiving palliative care, and the occurrence ofmedical emergencies such as acute-onset or decompensatedheart failure.

EachGPwas asked to record known noncardiovascular andcardiovascular co-morbidities of the study subjects andperform a structured and standardized history and clinicalexamination.6GPswere blinded to the results of the other studyvisits. Noncardiovascular co-morbidities were defined as thepresence of thyroid problems, asthma, chronic obstructivepulmonary disease, osteoarthritis, documented osteoporosis,and malignancies. Cardiovascular morbidities were defined as

the presence of hypertension, diabetes mellitus, hyperlipid-emia, history of angina pectoris or myocardial infarction,known cardiomyopathy, history of transient ischemic attack orcerebrovascular accident, peripheral arterial disease, history ofdecompensated heart failure, chronic atrial fibrillation (AF) orknown valvular heart disease. GPs reported important cardio-vascular interventions, such as percutaneous transluminalcoronary angioplasty or stenting; coronary, valvular, or arterialsurgery; and the placement of a pacemaker. The AnatomicTherapeutic Chemistry classification system was applied toregister medication use.7 Data on relevant cardiovascularmedication, including cardiac glycosides, diuretics,b blockers,calcium antagonists, angiotensin-converting enzyme inhibi-tors, angiotensin II receptor blockers, and lipid-loweringagents, were used. The history included questions aboutsymptoms of angina pectoris, dyspnea (Medical ResearchCouncil scale score�3), invalidating fatigue, orthopnea, cough(nocturnal or diurnal), wheezing, edema of the lower


Table 2Clinical characteristics, electrocardiographic findings, and laboratory test results according to the presence of cardiac dysfunction in 548 subjects with orwithout chronic atrial fibrillation or pacemaker






p Value*

Symptoms of angina pectoris 43 (11%) 12 (16%) 0.35 10 (16%) 4 (14%) 0.85Dyspnea 98 (26%) 27 (35%) 0.13 24 (38%) 14 (50%) 0.29Invalidating fatigue 66 (18%) 21 (27%) 0.082 15 (24%) 10 (36%) 0.27Orthopnea 19 (5.0%) 8 (10%) 0.15 3 (4.8%) 3 (11%) 0.37Cough at night 21 (5.6%) 2 (2.6%) 0.18 6 (9.5%) 2 (7.1%) 0.72Cough during the day 38 (10%) 10 (13%) 0.45 10 (16%) 7 (25%) 0.31Wheezing 40 (11%) 10 (13%) 0.55 6 (9.5%) 2 (7.4%) 0.75Peripheral edema (anamnesis) 118 (31%) 21 (27%) 0.50 30 (48%) 16 (57%) 0.41Palpitations 39 (10%) 12 (16%) 0.18 15 (24%) 7 (25%) 0.90Loss of appetite 44 (12%) 6 (7.8%) 0.27 6 (9.5%) 7 (25%) 0.098Current or former smoker 121 (32%) 25 (33%) 0.94 22 (35%) 6 (21%) 0.18Alcohol (units/day) 1 (0e7) 1 (0e7) 0.80† 1 (0e7) 2 (0e7) 0.99†

Recent change in symptoms 49 (13%) 13 (17%) 0.37 5 (7.9%) 3 (11%) 0.63Recent weight change 69 (18%) 17 (22%) 0.45 10 (16%) 5 (18%) 0.82Alteration of mental and

psychological status88 (23%) 26 (34%) 0.078 17 (27%) 14 (50%) 0.045

Heart rate on clinical examination(beats/min)

68 � 10 68 � 10 0.72 71 � 11 68 � 12 0.36

Systolic blood pressure (mm Hg) 138 � 19 141 � 21 0.24 137 � 21 137 � 15 0.95Diastolic blood pressure (mm Hg) 73 � 9 74 � 9 0.62 74 � 9 73 � 10 0.60Respiratory rate (breathes/min) 17 � 5 17 � 4 0.91 18 � 6 19 � 6 0.69Normal heart beats 346 (92%) 69 (90%) 0.59 54 (86%) 22 (79%) 0.40S3 7 (1.9%) 7 (9.1%) 0.035 2 (3.2%) 2 (7.1%) 0.40S4 4 (1.1%) 1 (1.3%) 0.85 3 (4.8%) 0 (0%) 0.083

Cardiac murmur 93 (25%) 40 (52%) <0.001 23 (37%) 19 (68%) 0.005Abnormal apex beat 12 (3.2%) 6 (7.8%) 0.15 9 (14%) 5 (18%) 0.67Normal breathing sounds 288 (76%) 58 (75%) 0.87 45 (71%) 22 (79%) 0.48Crepitation on auscultation 57 (15%) 7 (9.1%) 0.12 11 (18%) 5 (18%) 0.96Wheezing on auscultation 23 (6.1%) 6 (7.8%) 0.58 5 (7.9%) 1 (3.6%) 0.44Rhonchi on auscultation 13 (3.4%) 6 (7.8%) 0.18 3 (4.8%) 1 (3.6%) 0.80

Carotid murmur 36 (9.6%) 15 (20%) 0.039 2 (3.2%) 6 (22%) 0.032Raised jugular venous pressure 22 (5.8%) 10 (13%) 0.079 11 (18%) 8 (29%) 0.27Hepatojugular reflux 22 (5.8%) 16 (21%) 0.003 8 (13%) 8 (29%) 0.11Hepatomegaly 4 (1.1%) 0 (0%) 0.37 1 (1.6%) 1 (3.6%) 0.56Peripheral edema on clinical

examination112 (30%) 23 (30%) 0.97 26 (41%) 17 (61%) 0.088

Suspicion of ascites 1 (0.3%) 0 (0%) 0.54 0 (0%) 0 (0%) —

Body mass index (kg/m2) 27.6 � 4.8 26.6 � 5.7 0.16 27.4 � 4.4 27.3 � 3.7 0.96>25 264 (70%) 45 (58%) 0.046 42 (67%) 22 (82%) 0.13

ECG findingsHeart rate (beats/min) 65 � 11 65 � 13 0.72 67 � 13 65 � 12 0.41Previous myocardial infarction 46 (13%) 14 (18%) 0.25 10 (21%) 6 (24%) 0.80AF — — — 28 (46%) 20 (77%) 0.005Presence of artificial pacemaker — — — 22 (36%) 7 (27%) 0.41LV hypertrophy 22 (6.1%) 11 (15%) 0.053 4 (8.7%) 7 (28%) 0.064Left bundle branch block 14 (3.9%) 4 (5.3%) 0.58 3 (6.3%) 2 (8.0%) 0.78Right bundle branch block 23 (6.4%) 8 (11%) 0.27 7 (14%) 2 (8.0%) 0.46Sinus tachycardia 0 (0%) 0 (0%) — 0 (0%) 0 (0%) —

Sinus bradycardia 22 (6.1%) 7 (9.2%) 0.32 2 (3.3%) 2 (7.7%) 0.38Negative findings 160 (44%) 20 (26%) 0.002 — — —

Hemoglobin (g/dl) 13.4 � 1.4 13.1 � 1.5 0.075 13.5 � 1.6 13.3 � 1.6 0.52Anemiaz 61 (16%) 26 (34%) 0.003 11 (18%) 9 (32%) 0.16Total cholesterol (mg/dl) 204 � 46 195 � 45 0.11 205 � 30 179 � 32 <0.001Low-density lipoprotein (mg/dl) 124 � 39 118 � 36 0.20 128 � 30 109 � 28 0.005High-density lipoprotein (mg/dl) 56 � 15 57 � 16 0.70 55 � 15 46 � 11 0.003

(continued)

Heart Failure/Natriuretic Peptides in Patients Aged �80 Years 3

Table 2(continued)






p Value*

Total cholesterol/high-densitylipoprotein

3.8 � 1.1 3.6 � 0.84 0.019 3.9 � 0.97 4.1 � 1.3 0.36

Triglycerides (mg/dl) 123 � 67 99 � 39 <0.001 107 � 41 120 � 76 0.30Ultrasensitive C-reactive protein

(mg/dl)0.17 (0.078e0.40) 0.20 (0.069e0.41) 0.67† 0.23 (0.11e0.40) 0.36 (0.14e0.77) 0.055†

Cystatin C (mg/L) 1.2 � 0.48 1.4 � 0.65 0.037 1.3 � 0.41 1.5 � 0.59 0.15Estimated glomerular filtration

ratex (ml/min)66 � 26 58 � 22 0.013 60 � 23 53 � 20 0.16

Estimated glomerular filtration rate�45 ml/min

73 (20%) 22 (29%) 0.14 17 (27%) 10 (37%) 0.35

BNP (pg/ml) 75 (47e130) 200 (86e374) <0.001† 170 (98e242) 250 (171e328) 0.020†

NT-pro-BNP (pg/ml) 137 (79e255) 445 (169e1,088) <0.001† 607 (188e920) 1,047 (551e2,056) 0.002†

Data are expressed as mean � SD or median (interquartile range) for continuous variables or as number (percentage) for categorical variables.* Student’s t test.† Mann-Whitney U test.z Hemoglobin <12 g/dl for women and <13 g/dl for men.x Glomerular filtration rate was estimated using the Chronic Kidney Disease Epidemiology Collaboration 2 cystatin C equation.


extremities, palpitations, loss of appetite, smoking status(former or current), alcohol intake (units per week), recentchanges in symptoms, recent weight change, and alteration ofmental and psychological status. The clinical examinationconsisted of rest heart rate and breathing rate measurements.Blood pressure was measured in the sitting position in botharms, and measurements were repeated after 2 minutes. Thehighest systolic and diastolic blood pressure (left or right) after2 minutes was used in the analyses. Heart auscultation wasperformed to detect third or fourth heart sounds or cardiacmurmurs. The apex beat was palpated and recorded whenabnormal. Lung auscultation was performed to detect crepita-tion, wheezing, and rhonchi. The carotid arteries wereauscultated to detect murmurs. Jugular venous pressure wasmeasured and noted if increased, and the presence of hep-atojugular reflux was also checked. GPs determined whetherhepatomegaly or edema of the lower extremities was presentand noted whether patients had ascites.

Body mass index was calculated on the basis of stan-dardized measurements of weight and height. Twelve-leadelectrocardiograms were recorded using a QRS UniversalECG device (QRS Diagnostic, Maple Grove, Minnesota).Each electrocardiogramwas digitally stored and analyzed off-line by a single cardiologist (blinded to the results of all otherexaminations) according to the Minnesota Code Classifica-tion System.8 Patients with previous myocardial infarctions,AF, artificial pacemaker, left ventricular (LV) hypertrophy,left bundle branch block, right bundle branch block, sinustachycardia, or sinus bradycardia were included. Moreover,cardiologists were asked to report whether ECG findingswerecompletely normal.

A blood sample was collected in the morning after fasting,and plasma (ethylenediaminetetraacetic acid) and serumsamples were stored at �80�C. Hemoglobin concentrationswere measured on whole blood using the Sysmex XE-2100automated hematology analyzer (Sysmex, Milton Keynes,

United Kingdom). Anemia was defined as hemoglobin<12 g/dl for women and<13 g/dl formen.9 Total cholesterol,low-density lipoprotein and high-density lipoprotein choles-terol, triglycerides, ultrasensitive C-reactive protein, andcystatin C were measured using the UniCel DxC 800Synchron (Beckman-Coulter, Brea, California). Glomerularfiltration ratewas estimated using theChronicKidneyDiseaseEpidemiology Collaboration 2 cystatin C formula.10 Plasmalevels of B-type natriuretic peptide (BNP) were measuredusing the Biosite kit (Biosite, San Diego, California) ona UniCel DxI 800 Immunoassay System (Beckman-Coulter),and serum N-terminal pro-BNP (NT-proBNP) levels weremeasured using the Dade-Dimension Xpand (SiemensHealthcare Diagnostics, Deerfield, Illinois). Coefficients ofvariation ranged from 5.4% to 6.7% and from 3.9 to 4.3%,respectively.

Echocardiography was performed at the home of eachsubject by a single cardiologist (blinded to all other test results)using a commercially available portable system (CX50; PhilipsMedical Systems, Andover, Massachusetts). A completeexamination, in accordance with the recommendations of theAmerican Society of Echocardiography and the EuropeanAssociation of Echocardiography, was performed.11 Themethod, prevalence of echocardiographic abnormalities,quality of echocardiographic images, and classification ofsubjects according the presence of cardiac dysfunction werepreviously described in detail.12 Briefly, LV function wascalculated using Simpson’s biplane method.11 Systolicdysfunctionwas defined as an LV ejection fraction�50%. Thefunction of the mitral and aortic valves was evaluated usingcolor Doppler echocardiography after optimizing the gain andNyquist limit. Stenotic and regurgitant valve diseases wereevaluated according to semiquantitative and quantitativemethods recommended by the American Society of Echocar-diography.13,14 Subjects with prosthetic valves were evaluatedseparately.13 Clinically relevant valvular heart disease was


Figure 1. Study flow diagram.


defined as any mitral stenosis severity, severe aortic stenosis,moderate or severemitral regurgitation, andmoderate or severeaortic regurgitation. Diastolic function was assessed usingmitral flow velocities obtained using pulsed Doppler andpulsed tissue Doppler at the level of the mitral annulus.15

Additional apical and parasternal views for the assessment oftissue velocity (color tissue Doppler) were recorded. TheAmerican Society of Echocardiography and the EuropeanAssociation of Echocardiography guidelines were used toassess the presence of diastolic dysfunction.15 Because ofdifficulties in interpreting diastolic parameters in subjects withAF or pacemaker, the population under study was split into2 groups for analysis. In subjects without chronic AF orpacemaker (on electrocardiographic or by GP assessment), thetarget condition was clinically relevant cardiac dysfunction,including an LV ejection fraction �50%, valvular heartdisease, or severe diastolic dysfunction. In subjects withchronic AF or pacemaker, the target condition was an LV

ejection fraction�50% or valvular heart disease. A compositetarget condition was chosen because the main goal was toidentify those patients with increased risk who could bereferred for further testing.

Continuous variables are expressed as mean � SD oras median (interquartile range). Categorical variables areexpressed as numbers and frequencies. Comparisons betweendifferent categories of subjectswere performed using Student’st test or the Mann-Whitney U test (for nonparametric data) forunpaired data. The added value of natriuretic peptide levels andECG findings was measured using logistic regression analysisand classification and regression tree (CART) analysis, ascomplementary tools. Two strategies were used: one used allclinical variables collected by the GP and blood analyseswithout natriuretic peptide levels (the clinical model), and theother included natriuretic peptide levels and/or ECG findings(the clinicalþ model). For the logistic regression analysis,BNP, NT-proBNP, and ultrasensitive C-reactive protein were

Table 3Determinants of the presence of cardiac dysfunction in subjects with or without chronic atrial fibrillation or pacemaker from background variables, history,clinical examination, electrocardiographic findings, and natriuretic peptide levels with logistic regression analysis


Unadjusted OR(95% CI)

Adjusted OR(95% CI)

C-Statistic(95% CI)

Unadjusted OR(95% CI)

Adjusted OR(95% CI)

C-Statistic(95% CI)

Clinical model 0.79 (0.74e0.85) 0.90 (0.82e0.98)Age NS — 1.2 (1.1e1.4) 1.3 (1.0e1.6)Thyroid dysfunction 2.3 (1.1e4.8) 2.3 (0.92e5.7) NS —

Cardiomyopathy 2.2 (1.0e4.8) NS NS —

Peripheral arterial disease NS — 4.9 (1.4e17) 5.7 (1.0e32)Episode of decompensated

heart failure3.9 (1.9e7.9) NS NS —

Reported valvular heart disease 3.6 (2.1e6.1) 2.7 (1.4e5.1) 2.3 (0.95e5.8) NSCoronary surgery 3.1 (1.3e7.3) 2.8 (1.0e7.5) NS —

Cardiac glycosides NS — 2.8 (0.98e8.2) NSDiuretics 1.5 (0.95e2.5) NS NS —

Angiotensin-convertingenzyme inhibitors orangiotensin II receptorblockers

1.7 (1.0e2.8) NS NS —

Invalidating fatigue 1.8 (0.99e3.1) NS NS —

Orthopnea 2.2 (0.92e5.2) NS NS —

Loss of appetite NS — 3.2 (0.95e11) NSAlteration of mental and

psychological status1.7 (0.99e2.8) 1.8 (0.97e3.4) 2.7 (1.1e6.8) 4.2 (1.0e17)

S3 5.3 (1.8e15) 4.5 (1.2e17) NS —

Cardiac murmur 3.3 (2.0e5.9) 2.3 (1.2e4.2) 3.7 (1.4e9.4) 4.5 (1.1e18)Abnormal apex beat 2.6 (0.94e7.1) NS NS —

Rhonchi 2.4 (0.87e6.5) NS NS —

Carotid murmur 2.3 (1.2e4.5) NS 8.7 (1.6e47) NSIncreased jugular venous

pressure2.4 (1.1e5.3) NS NS —

Hepatojugular reflux 4.2 (2.1e8.5) NS 2.8 (0.91e8.3) 5.4 (1.2e25)Peripheral edema on clinical

examinationNS — 2.2 (0.89e5.5) NS

Body mass index >25 kg/m2 0.58 (0.35e0.96) NS NS —

Anemia 2.6 (1.5e4.5) 1.8 (0.90e3.4) NS —

Total cholesterol NS NS 0.97 (0.96e0.99) 0.96 (0.94e0.98)High-density lipoprotein NS — 0.94 (0.90e0.98) NSTotal cholesterol/high-density

lipoprotein0.77 (0.59e0.99) NS NS —

Triglycerides 0.99 (0.99e1.0) 0.99 (0.98e1.0) NS —

Log ultrasensitive C-reactiveprotein

NS — 2.2 (0.97e5.2) NS

Cystatin C 1.7 (1.1e2.6) 1.7 (1.0e2.8) 2.2 (0.87e5.6) NSClinicalþ modelElectrocardiography

LV hypertrophy 2.6 (1.2e5.6) 1.5 (0.60e4.0)* 0.79 (0.74e0.85) 4.1 (1.1e16) 3.5 (0.24e52)* 0.91 (0.82e1.0)AF — — 3.9 (1.4e11) 3.5 (0.71e18)* 0.91 (0.83e0.99)Negative findings 0.45 (0.26e0.79) 0.46 (0.25e0.87) 0.80 (0.74e0.85) — —

Log BNP 16 (7.5e35) 8.3 (3.3e21) 0.81 (0.75e0.87) 4.2 (0.83e21) 0.74 (0.089e6.2)* 0.90 (0.82e0.98)Log NT-proBNP 7.1 (4.1e12) 6.5 (3.1e13) 0.81 (0.75e0.87) 5.3 (1.7e16) 1.0 (0.23e4.7)* 0.90 (0.82e0.98)

Clinical model þ log BNP þnegative ECG findings

0.82 (0.76e0.87) —

Clinical model þ log NT-proBNP þ negative ECGfindings

0.82 (0.76e0.88) —

CI ¼ confidence interval; OR ¼ odds ratio.* p >0.10.


log-transformed, anemia was used instead of hemoglobinlevels, cystatin C was used instead of estimated glomerularfiltration rate, and bodymass index was dichotomized (>25 or

�25 kg/m2). Low-density lipoprotein was not used, because ofa strong correlation with levels of total cholesterol (Pearson’scorrelation ¼ 0.94, p <0.001). First, a univariate logistic


Table 4Accuracy of different diagnostic algorithms for the presence of cardiac dysfunction as determined using classification and regression tree analysis

Model* Target Condition Prevalence Area Underthe Curve

Sensitivity(%) (95% CI)

Specificity(%) (95% CI)

Post-Test Probabilityof a Negative TestResult (%) (95% CI)

Post-Test Probabilityof a Positive Test

Result (%) (95% CI)

No AF or pacemakerClinical model

(tree 1)Cardiac dysfunction 78/457 (17%) 0.63 51 (40e63) 75 (71e80) 12 (8.4e16) 30 (22e39)

Clinical model,algorithm toexclude cardiacdysfunction†

(tree 2)

Cardiac dysfunction 78/457 (17%) 0.73 100 (95e100) 35 (30e40) 0.0 (0.0e2.8) 24 (19e29)

Clinical model afteralgorithm toexclude cardiacdysfunction(tree 3)

Ejection fraction�50%

24/328 (7.3%) 0.87 88 (68e97) 76 (70e80) 1.3 (0.3e3.7) 22 (14e32)

Valvular heart disease 38/328 (12%) 0.79 79 (63e90) 71 (65e76) 3.7 (1.6e7.2) 26 (19e35)Severe diastolic

dysfunction16/328 (4.9%) 0.92 88 (62e98) 83 (78e87) 0.8 (0.1e2.7) 21 (12e33)

Clinicalþ model(tree 4)

Cardiac dysfunction 78/457 (17%) 0.71 53 (41e64) 89 (85e92) 10 (7.1e13) 49 (38e61)

Clinicalþ model,algorithm toexclude cardiacdysfunction†

(tree 5)

Cardiac dysfunction 78/457 (17%) 0.76 97 (91e100) 38 (33e43) 1.4 (0.2e4.9) 24 (20e30)

Clinicalþ modelafter algorithm toexclude cardiacdysfunction(tree 6)

Ejection fraction�50%

23/316 (7.6%) 0.91 96 (79e100) 77 (72e82) 0.4 (0.0e2.4) 26 (17e36)

Valvular heart disease 36/316 (11%) 0.85 83 (67e94) 76 (70e81) 2.8 (1.0e5.9) 31 (22e41)Severe diastolic

dysfunction16/316 (5.1%) 0.86 63 (35e85) 95 (92e97) 2.0 (0.8e4.4) 42 (22e63)

AF or pacemakerClinical model

(tree 7)Cardiac dysfunction 28/91 (31%) 0.95 93 (77e99) 94 (85e98) 3.3 (0.4e11) 87 (69e96)

Clinicalþ model(tree 8)

Cardiac dysfunction 28/91 (31%) 0.95 93 (77e99) 92 (82e97) 3.3 (0.41e12) 84 (66e95)

CI ¼ confidence interval.* The tree with the lowest cost was selected.† The cost for FNs was set to 3 times the cost for FPs. Only for trees 3 and 6, trees with higher relative costs were selected because of the clinical

interpretation (for tree 3, relative cost 0.785 instead of 0.770; for tree 6, relative cost 0.825 instead of 0.773).


regression analysis was performed with all clinical variables.Subsequently, all variables with p values�0.10 were includedin the multivariate analyses (stepwise, forward conditional) todetermine which items were independently related to thepresence of cardiac dysfunction. Afterward, natriuretic peptidelevels and ECGvariables were added to the clinicalmodel. TheC-statistic and 95% confidence interval (CI) was calculatedusing the probabilities calculated from the regression analysisand compared between different models.16 Tree-based models(such as CART) are nonlinear and nonparametric alternativesto linear models for regression and classification problems.17

There are multiple advantages to using CART analysis: themethod deals with multicollinearity in an intuitively correctway, interactions are automatically taken into account in anobjective way, weights can be attributed to false-negatives(FNs) and false-positives (FPs), missing data are dealt with ina natural way, and indicators of diagnostic accuracy can easilybe calculated from the output. The minimum-cost tree wasselected as the best tree. The cost represents the proportion ofmisclassified cases.17 A stepwise approach in tree generationwas used. First, a tree with equal costs for FNs and FPs wascalculated. Second, the cost of FNs was set to 3 times that of

FPs18 to generate a tree that excluded cardiac dysfunction.Third, a tree to identify the different subtypes of cardiacdysfunction (equal costs for FNs and FPs) was calculated in theremaining population. For each tree, the area under the curvewas determined, and the sensitivity, specificity, and post-testprobability of negative and positive test results were calculatedusing 2 � 2 tables. Data analysis was performed using SPSSversion 19.0 for Windows (SPSS, Inc., Chicago, Illinois)and Salford Predictive Model Builder version 6.6 (SalfordSystems, San Diego, California).

Results

From November 2, 2008, to September 15, 2009, 567subjects were included in the BELFRAIL study. Echocardi-ographywas performed in 548 participants, of whom 91 (17%)had AF or pacemaker. The target condition was present in 78subjects (17%) without and 28 (31%) with AF or pacemaker.Table 1 lists the background variables, and Table 2 lists theresults of the index tests according to the presence of the targetcondition. The median time from echocardiography to exam-ination by the GP was 29 days (interquartile range 14 to 43),

Figure 2. (A) Tree 2, representing the clinical model to exclude cardiac dysfunction in subjects without chronic AF or pacemaker (n ¼ 457). (B) Tree 3,representing the clinical model after the algorithm to exclude cardiac dysfunction in subjects without chronic AF or pacemaker (n ¼ 328). (C) Tree 5, rep-resenting the clinicalþ model to exclude cardiac dysfunction in subjects without chronic AF or pacemaker (n ¼ 457). (D) Tree 6, representing the clinicalþmodel after the algorithm to exclude cardiac dysfunction in subjects without chronic AF or pacemaker (n ¼ 316). (C,D) Class 0 ¼ no cardiac dysfunction, class1 ¼ systolic dysfunction, class 2 ¼ valvular heart disease, and class 3 ¼ severe diastolic dysfunction. BMI ¼ body mass index; eGFR ¼ estimated glomerularfiltration rate; HDL ¼ high-density lipoprotein; LDL ¼ low-density lipoprotein; usCRP ¼ ultra-sensitive C-reactive protein.


from echocardiography to electrocardiography was 7 days(interquartile range 3 to 15), and from echocardiography tonatriuretic peptide testing was 3 days (interquartile range 1 to13). In total, 509 participants (90%) underwent all index testsand echocardiography (Figure 1). Some subjects failed toundergo electrocardiography, natriuretic peptide testing, orechocardiography as a result of technical issues or had incon-clusive results.

Logistic regression analysis showed a C-statistic of 0.79(95% CI 0.74 to 0.85) for the clinical model in subjectswithout AF or pacemaker (Table 3). Of the additional tests,LV hypertrophy on electrocardiography, negative ECGfindings, NT-proBNP, and BNP were univariate determi-nants of cardiac dysfunction. The combination of NT-proBNP or BNP with negative ECG findings had the largestadded value, resulting in an increase in the C-statistic to 0.82(95% CI 0.76 to 0.88, p ¼ 0.50). In subjects with AF orpacemaker, the clinical model had a high C-statistic (0.90,95% CI 0.82 to 0.98). No additional test was an independentcorrelate of cardiac dysfunction.

CART analysis in subjects without AF or pacemakershowed the presence of cardiac murmur as the strongestdiscriminating factor for cardiac dysfunction when buildinga clinical model (Table 4, tree 1). The lowest cost tree

showed that cardiac murmur was the only splitter (38 FNs,93 FPs). Increasing the cost for FNs yielded a 5-node tree(Figure 2, tree 2) that was able to exclude 132 participantsfrom further analysis (0 FNs, 247 FPs). Three participantscould not be classified and were kept in the group for furtheranalysis as a result of missing discriminating factors. In theremaining 328 subjects, the lowest cost tree (relative cost0.770) was a single-node tree with cardiac murmur as theonly splitter that was able to correctly classify 52 subjects(16%) in different categories of cardiac dysfunction (tree notshown). A 9-node tree (Figure 2, tree 3) with an acceptablerelative cost (0.785) is shown because of its clinical use.This tree was able to correctly classify 111 subjects (34%).BNP emerged as the strongest discriminating factor whenbuilding the clinicalþ model (tree 4). The lowest cost treewas a single-node tree using BNP with a cut-off value of192 pg/ml (37 FNs, 42 FPs). Increasing the cost for FNs,a 4-node tree emerged (Figure 2, tree 5) that was able toexclude 145 participants (2 FNs, 236 FPs). Four participantscould not be classified because of missing values and werekept in the group for further analysis. In the remaining 316subjects, the lowest cost tree (relative cost 0.773) was alsoa single node tree that used the presence of cardiac murmurand correctly categorized 50 participants (16%) (tree not


Figure 3. (A) Tree 7, representing the clinical model in subjects with chronic AF or pacemaker (n ¼ 91). (B) Tree 8, representing the clinicalþ model in subjectswith chronic AF or pacemaker (n ¼ 91). HDL ¼ high-density lipoprotein; LDL ¼ low-density lipoprotein.


shown). A 9-node tree (Figure 2, tree 6) with an acceptablerelative cost (0.825) is shown because of its clinical use.This tree was able to correctly classify 160 subjects (51%).

In subjects with AF or pacemaker, a 7-node tree (Figure 3,tree 7) emerged from the CART analysis for the clinicalmodel,

and a 6-node tree emerged for the clinicalþ model (Figure 3,tree 8). These 2 trees had a large area under the curve of 0.95and low and high post-test probabilities of, respectively,negative and positive test results (Table 4). Low-density lipo-protein and total cholesterol appeared to be the strongest


discriminating factors in the 2 models, followed by BNP in theclinicalþmodel. The trees for the clinical and clinicalþmodelswere able to correctly classify 85 (93%) and 84 (92%) partic-ipants, respectively.

Discussion

In this epidemiologic study, an intention-to-diagnoseanalysis, analogous to “intention-to-treat” analysis, was used.This approach means that all patients who are at risk fora specific target condition should be involved in the analyses,regardless of all previously known diagnoses, using all rele-vant and known clinical data. This method starts froma diagnostic intent, at any given moment, in a population atrisk. With >90% having cardiovascular morbidity, 55%presenting with �1 cardinal symptom of heart failure, and85% taking potentially symptom-reducing medications, thisrepresentative population-based sample of very old patientscan clearly be considered an at-risk population. Thus, thisstudy was not about diagnosing new, unidentified cases butabout assessing the presence of cardiac dysfunction as a riskfactor for heart failure and death in real-life circumstances.

In this large sample of unselected patients aged �80years, logistic regression analysis showed little added valueof implementing natriuretic peptide testing or electrocardi-ography to identify cardiac dysfunction in daily practice.CART analysis confirmed these results when looking at thereported estimates of the diagnostic accuracy of differentstrategies. However, 58 more subjects (13%) were correctlyclassified according to the presence of cardiac dysfunctionwith the clinicalþ model in subjects without AF or pace-maker. In participants with AF or pacemaker, CART anal-ysis yielded a correct classification for >90% for the2 models.

Previous studies investigating the added value of natri-uretic peptide levels and ECG findings were conducted inselected populations of patients suspected of heart failure19,20

or patients with stable chronic obstructive pulmonarydisease4 and thus are difficult to compare with the presentstudy. These studies showed a benefit in including natriureticpeptides in a diagnostic model for heart failure. Aside fromrisk scores, which were generated on the basis of thesestudies,4,20 algorithms for the diagnosis of heart failure thatuse clinical information have also been suggested.21 In thepresent study, we also tried to determine the place of natri-uretic peptide levels and ECG findings in a possible diag-nostic pathway. CART analysis found BNP to be a goodfirst-line triage test for excluding cardiac dysfunction andnormal ECG findings to further rule out the target condition.Furthermore, a high plasma level of BNP was able to iden-tify a substantial number of subjects with systolic dysfunc-tion. Our findings confirmed the “rule-out” and “rule-in”cut-off values of natriuretic peptides previously reported.1

Studies investigating the test performance of natriureticpeptides in selected patients with AF are scarce.22,23 BNPand NT-proBNP levels were higher in those with AF, sug-gesting that a higher diagnostic threshold should be used insubjects with AF.22,23 The present findings are in line withthese results but are the first to show that there is no addedvalue of using natriuretic peptides to identify cardiacdysfunction in patients with AF or pacemaker.

A possible weakness in the present study is that echocar-diography has beenused as the reference standard.24,25 Todate,no consensus exists about cut-off values for echocardiographicparameters, especially in the old. However, choosing clinicallyrelevant cardiac dysfunction as a reference standard completelyprecluded any possible incorporation bias, in contrast toprevious studies using the diagnosis of heart failure by a panelof experts as the reference standard.4,19,20 Furthermore, theclinical relevance of identifying old subjects with cardiacdysfunction is evidenced by numerous epidemiologic studiesshowing increased mortality in these patients.26e28 Becauseold patients are often excluded from clinical trials, one couldargue that the management of cardiac dysfunction in oldpatients is hampered by the lack of evidence-based guidelines.The therapeutic management of old patients with systolicdysfunction is based mainly on extrapolating the results ofstudies with younger populations, although some have shownthe benefit of b blockers and angiotensin-converting enzymeinhibitors in the elderly.29,30 No therapeutic interventions havebeen proved to improve outcomes in patients with heart failurewith preserved ejection fractions; therefore, interventionsare directed mainly toward monitoring the risk factors of dia-stolic dysfunction, such as diabetes, hypertension, and AF.1

However, above all, assessing the presence of cardiacdysfunction in very old patients remains essential, not only tooptimize the monitoring of identified cases but also to preventpotential side effects of medication in those being excludedwith cardiac dysfunction.

This study is the first to examine the added value of natri-uretic peptide levels and ECG findings for cardiac dysfunctionin a large, unselected, population-based sampleof patients aged�80 years. As all relevant and known clinical data were used,this study accurately reflects the impact of implementing thesetests in routine clinical practice. However, a few limitationsshould be considered. First, co-morbidities may have beenunderdiagnosed, because they were not assessed but insteadreported by GPs. Second, because of the large number ofmissing values (n¼ 198 [37%]) for systolic pulmonary arterypressure,12 right-sided dysfunction was not used in theconception of the target condition. Third, the most importantdisadvantage of CART analysis remains its variability.Different data sets from the same setting can lead to quitedifferent final trees. A reproducibility test of the present anal-yses was not performed because no external data set wasavailable, and alternative methods that average different treeson the basis of bootstrap samples lose their interpretability.17

However, the CART analyses include an internal cross-vali-dation process to reduce overoptimism. Fourth, imputation ofmissing values was not used for the multivariate regressionanalysis, because <10% of data were missing. Furthermore,CART analysis uses a technique that deals withmissing valuesfor covariates in a natural way, which is automatically inte-grated within an analysis.

Acknowledgment: This study was made possible by theparticipating GPs. Dr. Van Pottelbergh is a fellow of theResearch FoundationeFlanders.

Disclosures

The authors have no conflicts of interest to disclose.



Supplementary data

Supplementary data related with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.amjcard.2012.12.055.

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