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Population Risk Prediction Models for Incident Heart Failure

A Systematic Review
Originally published Heart Failure. 2015;8:438–447



The prevalence of heart failure is expected to significantly rise unless high-risk patients are effectively screened and appropriate, cost-effective prevention interventions are implemented.

Methods and Results—

We performed a systematic review to evaluate the prediction characteristics of the published heart failure risk prediction models as of August 2014 using MEDLINE and EMBASE databases. Eligible studies reported the development, validation, or impact assessment of a model. Two investigators performed independent review to extract data on study design and characteristics, risk predictors, discrimination, calibration, and reclassification ability of models, as well as validation and impact analysis. We included 13 publications reporting on 28 heart failure risk prediction models. Models had acceptable-to-good discriminatory ability (c-statistics, >0.70) in the derivation sample. Calibration was less commonly assessed, but was acceptable when it was. Only 2 models were externally validated more than once, displaying modest-to-acceptable discrimination (c-statistics, 0.61–0.79). When assessed, novel blood and imaging markers modestly improved risk prediction. One model assessed the prediction properties in race-based subgroups, whereas 2 models evaluated sex-based subgroups. Impact analysis found none of the models recommended for use in any clinical practice guideline.


Incident heart failure risk prediction remains at an early stage. The discrimination ability of current models is acceptable in derivation data sets but most models have not been externally validated. It remains unclear which models are cost-effective and best suit population screening needs. The effects of models on clinical and preventative care requires further study.


Heart failure (HF) affects >5 million Americans.1 Unless effective preventive interventions are implemented, it is estimated that by 2030, HF prevalence will grow by 25% and annual costs of care from $21 to $53 billion.2 The prognosis of patients with HF remains poor with a projected 50% 5-year mortality, underscoring the importance of prevention.3 Prevention interventions, however, are unlikely to be cost-effective unless targeted toward reliably identified high-risk populations. Because of the epidemiological paradox, more HF cases occur in patients with the absence of any given individual risk factor, which makes it important to develop a more comprehensive risk assessment scheme than to rely solely on individual risk factors. Unfortunately, the best approach for quantifying overall incident HF risk in individuals remains unclear. In this study, we sought to systematically assess the performance characteristics and the use of the available HF risk models for predicting incident HF in the general population.

Clinical Perspective on p 447


Data Sources

We searched PubMed and EMBASE databases from January 1990 to August 2014 for HF risk prediction models published in English language. We used search terms related to HF and disease prediction (Appendix A in the Data Supplement) and reviewed the reference lists of studies and identified their citations through the ISI Web of Science for additional data. Eligible studies had to report a risk assessment model in a cohort study either by an equation or by a score. Two evaluators (J.B.E. and J.B.) independently identified articles and screened them for the inclusion criteria (Figure) by reviewing text and the Data Supplement. Disagreements were solved by consensus. We excluded studies for models developed in specific population (eg, postoperative patients, patients already diagnosed with HF, studies that reported measures of risk without information on model characteristics, and simulation studies). We extracted information on the area under the receiver-operating characteristic curve (AUC) or c-statistic, reclassification percentage, net reclassification improvement (NRI), and integrated discrimination improvement index (IDI).4


Figure. Study selection process. Flowchart showing literature search strategy and selection process for inclusion of studies in the systematic review.

Bias, Clinical Usefulness, and External Validity

Risk for bias in models was assessed per Hayden et al5 recommendations, including 7 categories, such as participation (sampling bias), attrition (attrition bias), prognostic factor selection, prognostic factor measurement, outcome measurement (ascertainment bias), statistical analysis, and reporting model performance. Extent and methods of dealing with missing data, (Table in the Data Supplement), appropriateness of statistical methods, and clinical usefulness as a combination of clinical use and usability were assessed. Clinical use was assessed as whether predictive models proposed clinical scenarios or decision nodes linked to a risk category or threshold where the model may be useful for guiding diagnostic or therapeutic interventions. For usability, we noted whether a calculator or nomogram that enables easier knowledge translation was added. To examine generalizability and external validity, we evaluated whether models had been validated in an independent population in the original or a subsequent publication.

Data Extraction and Quality Assessment

Two reviewers (J.B.E. and J.B.) performed quality assessment and extracted information about design, setting, population characteristics, number of patients in the derivation and validation cohorts, number of outcomes of interest, number of candidate predictor variables, and the type of regression model used. For predictors, we specifically assessed for age, sex, hypertension, left ventricular hypertrophy, myocardial infarction, diabetes mellitus, valvular disease, and overweight/obesity, based on previously reported association of these factors with HF. For discriminative ability, information on the c-statistic, and for calibration, the difference between the observed and predicted rates, as well as the P value of the corresponding test statistic, were extracted. Data relevant to reclassification included NRI and IDI values, as well as their respective 95% confidence intervals and P values, when available. Given the heterogeneity in the type and number of metrics, the number and nature of risk factors included, and the study designs used, our analysis consisted mainly of a narrative synthesis of the evidence.

Effect of HF Risk Models

Effect of studies was identified through scanning the references of all publications and applying search strategies for impact studies proposed by Reilly and Evans,6 which combines the model’s acronym or name of the cohort or first author with specific search term combination (Appendix B in the Data Supplement). We searched relevant practice guidelines to assess the implementation of HF prediction models in countries or regions, in which such models were developed. We also targeted English-language guidelines compiled by a selection of professional organizations known to regularly issue recommendations for the management of HF, including the American College of Cardiology,7 the American Heart Association,7 Heart Failure Society of America,8 and the European Society of Cardiology.9


Prediction Models

We screened 413 abstracts, retrieved 48 citations for full-text review, and selected 13 studies that described 28 HF prediction risk models (Figure). The characteristics of the risk prediction models are presented in Table 1.

Table 1. Overview of HF Risk Prediction Model

Reference and CohortPopulationRisk FactorsnOutcome Follow-Up, yDefinitionDiscriminationCalibrationValidationReclassification
NRI, %IDI, %
Kannel et al10 FHS risk scoreUS: whitesAge, LVH, HR, SBP, vital capacity, CHD, valve disease, DM, cardiomegaly15 2674864Framingham criteriaNRNRNRNRNR
Butler et al11Health ABC ScoreUS: whites and blacksAge, CHD, smoking, SBP, HR, glucose, creatinine, albumin, and LVH29352584–9Physician diagnosis and treatment for HF0.73 (0.72 after bootstrapping)HL χ2=6.24 (P=0.621)ApparentBootstrappingNRNR
Goyal et al12 Kaiser PermanenteUS: mixed but mainly whitesAge, CHD, HTN, DM, AF, and valvular disease359 94740019.5ICD-9 code defined HFWomen: 0.886 and men: 0.877NRApparentNRNR
Velagaleti et al13 FHSRisk score 1US: mainly whitesAge, HTN, DM, CAD, AF, and valvular disease2754959.4Framingham criteria0.84 (0.838 after jacknife)HL χ2=2.98 (P=0.94)ApparentJacknifeNRNR
Velagaleti et al13 FHSRisk score 2US: mainly whitesAge, HTN, DM, CAD, AF, valvular disease, BNP2754959.4Framingham criteria0.85 (0.862 after jacknife)HL χ2=3.48 (P=0.90)ApparentJacknifeNRNR
Velagaleti et al13 FHSRisk score 3US: mainly whitesAge, HTN, DM, CAD, AF, BNP, albumin/creatinine ratio, valve disease2754959.4Framingham criteria0.86 (0.862 after jacknife)HL χ2=9.45 (P=0.40)ApparentJacknifeNR0.13 (P<0.002)
deFilippi, et al14 CHS score 1US: mainly whitesAge, sex, race, SBP, ECG, glucose, CHD, smoking, creatinine, albumin, HR42211279Diagnosis+symptoms/signs+HF therapy0.752NRNRNRNR
deFilippi, et al14 CHS risk score 2US: mainly whitesAge, sex, race, SBP, ECG, glucose, CHD, status, creatinine, albumin, HR, cTnT42211279Diagnosis+symptoms/signs+HF therapy0.767NRNR0.0430.026
Smith et al15MDCS score 1Sweden: mainly whitesAge, sex, SBP, DBP, LDL, MI antihypertensives518711214ICD-8, 9, and 10 codes0.815NRNRNRNR
Smith et al15MDCS score 2Sweden: mainly whitesAge, sex, SBP, DBP, LDL, MI, antihypertensives, MR-proANP518711214ICD-8, 9, and 10 codes0.824NRNR14% (P=0.005)0.03 (P<0.001)
Smith et al15MDCS score 3Sweden: mainly whitesAge, sex, SBP, DBP, LDL, MI, antihypertensives, NT-proBNP518711214ICD-8, 9, and 10 codes0.837NRNR16% (P=0.003)0.03 (P<0.001)
Smith et al15MDCS score 4Sweden: mainly whitesAge, sex, SBP, DBP, LDL, MI, antihypertensives, CRP518711214ICD-8, 9, and 10 codes0.823NRNR7% (P=0.10)0.01 (P=0.02)
Smith et al15MDCS score 5Sweden: mainly whitesAge, sex, SBP, DBP, LDL, MI, antihypertensives, MR-proANP, NT-proBNP518711214ICD-8, 9, and 10 codes0.838NRNR17% (P=0.002)0.03 (P<0.001)
Smith et al15MDCS score 6Sweden: mainly whitesAge, sex, SBP, DBP, LDL, MI, antihypertensives NT-proBNP, CRP518711214ICD-8, 9, and 10 codes0.842NRNR19% (P=0.003)0.04
Smith et al15MDCS score 7Sweden: mainly whitesAge, sex, SBP, DBP, LDL, MI, antihypertensives, MR-proANP, NT-proBNP, CRP518711214ICD-8, 9, and 10 codes0.842NRNR22% (P<0.001)0.05 (P<0.001))
Kalogeropoulos et al16 Health ABC score 2US: whites and blacksAge, CHD, smoking, SBP, HR, glucose, LVH, creatinine, albumin, IL-626103119.4Diagnosis+clinical or echo findings+medical therapy0.734 (0.706–0.763)NRNRNRNR
Kalogeropoulos et al16 Health ABC score 2US: whites and blacksAge, CHD, smoking, SBP, HR, glucose, LVH, creatinine, albumin, IL-6, TNF-α, CRP26103119.4Diagnosis+clinical or echo findings+medical therapy0.737 (0.709–0.765)NRNRNRNR
Pfister et al17 PRoactive trial score19 European countries: whitesAge, CKD, diuretic, MI use, HbA1c, DM, LDL, HR, R/LBBB, pioglitazone, microalbuminuria49512332.9Medical record diagnosis0.75(0.751 after bootstrapping correction)NRApparentNRNR
Kalogeropoulos et al18 CHSUS: mixed but 86.5% whitesAge, CHD, smoking, SBP, HR, glucose, creatinine, albumin LVH, echo score (EF, LA, and E/A) and NT-proBNP3752 2865Diagnosis+clinical or echo findings+medical therapy0.777NRNR10.6%0.044
Kalogeropoulos et al18 CHSUS: mixed but 86.5% whitesAge, CHD, smoking, SBP, HR, glucose, creatinine, albumin and NT-proBNP37522865Diagnosis+clinical or echo findings+medical therapy0.773NRNR11.3%0.045
Kalogeropoulos et al18 CHSUS: mixed but 86.5% whitesAge, CHD, smoking, SBP, HR, glucose, creatinine, albumin LVH, echo score (EF, LA, and E/A ratio) and NT-proBNP37522865Diagnosis+clinical or echo findings+medical therapy0.789NRNR16.3%0.064
Wang et al19FHSUS: mainly whitesAge, sex, BMI, SBP, HTN, DM, smoking, total and HDL-cholesterol, AF, CVD, EKG LVH, murmur342816211.3Framingham criteria0.846NRNRNRNR
Wang et al19FHSUS: mainly whitesAge, sex, BMI, SBP, HTN, DM, smoking, total and HDL-cholesterol, AF, CVD, EKG LVH, murmur, and sST2, GDF-15, hsTnI, hsCRP, and BNP342816211.3Framingham criteria0.870NRNR0.39 (0.21–0.57)0.04 (0.02–0.06)
Agarwal et al20ARICUS: biethnic—whites and blacksAge, race, sex, prevalent CHD, SBP, antihypertensives, DM, smoking, HR, BMI13 555148710HF hospitalization or ICD-9 code on the death certificateNRNRNRNRNR
0.7937 after bootstrapBootstrapping
Agarwal et al20ARICUS: whites and blacksAge, race, sex, NT-proBNP13 555148710HF admission or ICD-9 code on death certificateNRNRNR
Schnabel et al21FHSUS: whitesAge, BMI, EKG LVH, DM, MI significant murmur72516110Framingham criteria0.71 (0.67–0.75)HL χ2=7.29, (P=0.61)NRNRNR
Wong et al22 NAVIGATOR study38 Countries mixedAge, CHD, EKG, waist SBP, AF, COPD, urine albumin/creatinine ratio, hemoglobin89751246.5HF hospitalization0.864 (0.79after bootstrapping)NRApparentNRNR
Wannamethee et al23 British Regional Heart StudyMainly whitesAge obesity, HTN, DM, MI, and angina387025411Physician diagnosis0.708 (0.676–0.741)NRApparentNR

AF indicates atrial fibrillation; ARIC, atherosclerosis risk in communities; BMI, body mass index; BNP, brain natriuretic peptide; BP, blood pressure; BSA, body surface area; CAD, coronary artery disease; CHD, coronary heart disease; CHS, Cardiovascular Health Study; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; cTnT, cardiac troponin T; COPD, chronic obstructive pulmonary disease; CVD, cardiovascular disease, CRP, C-reactive protein; DBP, diastolic blood pressure; DM, diabetes mellitus; EF, ejection fraction; eGFR, estimated glomerular filtration rate; EKG, electrocardiogram; ENRICHD, Enhancing Recovery in Coronary Heart Disease Patients Trial; FFM, fat-free mass; FHS, Framingham Heart Study; GDF-15, growth differentiation factor-15; HbA1c, hemoglobin A1c; Health ABC, health aging, and body composition; HF, heart failure; HL, Hosmer–Lemeshow; HDL-cholesterol, high-density lipoprotein-cholesterol; HR, heart rate; hsTnI, high-sensitivity troponin I; HTN, hypertension; ICD-9, International Classification of Diseases Ninth Revision; IDI, Integrative Discriminative Index, IL, interleukin; LA, left atrium; LDL, low-density cholesterol; LBBB, left bundle branch block; LV, left ventricle; LVH, LV hypertrophy; MDCS, Malmö Diet and Cancer Study; MI, myocardial infarction; MR-proANP, mid-regional proatrial natriuretic peptide; NAVIGATOR, Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research Study; NT-proBNP, N-terminal pro-B-type natriuretic peptide; NRI, Net reclassification Index; NR, not reported; PVD, peripheral vascular disease; SBP, systolic blood pressure; sST2, soluble ST2; TNF, tumor necrosis factor; US, United States; and VISP, Vitamin Intervention for Stroke Prevention Trial.

Populations, Risk Factors, and Outcomes

Eleven models were developed from US cohorts, 1 in Sweden, 1 in Britain, and 2 multicenter cohorts (19 European countries in 1 study17 and 38 countries from America, Europe, Asia, and Africa in another study22). Models were mostly developed in whites; the models based on US populations included non-white participants, mainly blacks. The number of participants ranged from 725 to 359 947 and 18 to 99 years of age. The length of follow-up ranged from 2.9 to 14 years. Three models were developed in specific populations, including diabetes mellitus,17 impaired glucose tolerance,22 and atrial fibrillation.21 The method of HF identification varied, but was mainly based on clinical presentation, hospital discharge records, and cause of death. One study defined HF using outpatient records in addition to hospital and death record12 and another study included an echocardiographic-based definition of HF.18 None of the studies assessed HF with preserved ejection fraction and HF with reduced ejection fraction separately.

One study published the numbers of variables tested for inclusion.22 Thus for most studies, the number of outcomes of interest per candidate variable could not be assessed. The predictors most commonly included in the final models were age, sex, body mass index, diabetes mellitus, systolic blood pressure, creatinine, serum albumin or total protein, LV hypertrophy, and coronary artery disease. Biomarkers were evaluated in several studies, including B-type natriuretic peptide (BNP) or N-terminal pro-BNP (NT-proBNP), midregional proatrial natriuretic peptide, interleukin (IL)-6), tumor necrosis factor (TNF)-α, C-reactive protein (CRP), soluble IL-2 receptor, growth differentiation factor-15, high-sensitivity troponin I (hsTnI), and urinary albumin/creatinine ratio.

All the models were developed using a Cox regression model with reporting of the model with β coefficients, with 2 studies providing additional point-based scoring systems.10,11

Performance of the Models

Table 1 displays the model performance. The AUC ranged from 0.71 to 0.87, indicating a modest-to-good discrimination. Risk scores were internally validated through bootstrapping in 411,17,20,22 and jackknife in 1 study.13 Five models had calibration estimated with the Hosmer–Lemeshow test. Most studies did not test for sex-based1013,16–18,21,22 or race-based1013,15–19,21,22 specific performance, except the Health Aging, and Body Composition (ABC) study HF score that predicted HF risk similarly in both sex and white/black race in development cohort,11 but the risk was underestimated in white men, especially in the 5% to 10% risk category, in the validation cohort.24 The AUCs were 0.71, 0.75, 0.74, and 0.72 in white men, white women, black men, and black women, respectively.24

Model Improvement

In the Framingham Study, BNP and the urinary albumin/creatinine ratio increased the AUC from 0.84 to 0.8613 and 13% of subjects were appropriately reclassified. In the Health ABC study, the addition of IL-6, TNF-α, and CRP increased AUC from 0.717 to 0.737, mainly because of IL-6 that improved AUC to 0.734 (P=0.001).16 In the Cardiovascular Health Study, NT-proBNP increased AUC from 0.667 to 0.719 (P<0.001), but discrimination remained moderate.25 In the Malmö Diet and Cancer study, midregional proatrial natriuretic peptide, NT-proBNP, and CRP together improved AUC from 0.815 to 0.842 with an IDI of 0.05 (P<0.001) and an NRI of 22% (P<0.001).15 In the Atherosclerosis Risk in Communities study, adding NT-proBNP increased the AUC by 0.032, NRI by 13%, and IDI by 0.057; NRI was higher in women (17.9%) than in men (14.4%).20

Echocardiographic score (left ventricular ejection fraction and mass, E/A ratio, and left atrium), and NT-proBNP improved AUC (echocardiography, 0.031; NT-proBNP, 0.027; combined, 0.043; all P<0.01), and yielded robust IDI (echocardiography, 43.3%; NT-proBNP, 42.2%; combined, 61.7%; all P<0.001), and NRI (echocardiography, 11.3%; NT-proBNP, 10.6%; combined, 16.3%; all P<0.01), when added to the Health ABC model.18 Participants at intermediate risk (5%–20% 5-year risk; 35.7% of the cohort) derived the most reclassification benefit. Echocardiography yielded modest reclassification when used sequentially after NT-proBNP.

A multimarker score with soluble ST2, growth differentiation factor-15, hsTnI, hsCRP, and BNP increased AUC (0.846–0.870), NRI (0.39), and IDI (0.04) in the Framingham study.19 In the Atherosclerosis Risk in Communities study, cardiac troponin T and NT-proBNP improved AUC by 0.040 and 0.057; and NRIs by 50.7% and 54.7% in women and in men, respectively.26 In the Multi-Ethnic Study of Atherosclerosis cohort, NT-proBNP and LV mass index change AUC from 0.81 to 0.89; IDI by 0.046; NRI 6-year risk probability categorized by <3%, 3% to 10%, >10% of 0.175, P=0.019 and category-less NRI by 561, P<0.001.27 In the Framingham cohort, a composite of LV hypertrophy and LV dysfunction increased AUC from 0.765 to 0.770. With growth differentiation factor-15 and hsTnI, the NRI was 0.212 (P<0.0001).28 In the British Heart Regional Study, the addition of NT-proBNP to the Health ABC model improved AUC from 0.706 to 0.764 (P<0.0001).23 In the Prevention of Vascular and Renal End stage Disease (PREVEND) study, NT-proBNP, midregional proatrial natriuretic peptide, hsTnT, hsCRP, cystatin-C and urinary albumin excretion individually modestly improved the AUC of 0.826 by 4.7, 2.2, 2.5, 1.9, 1.0, and 1.6%, respectively.29

External Validity

Five models were externally validated (Table 2). Of these, the Framingham10 and Health ABC models11 were externally validated more than once. The AUC in validation studies were generally lower and with a poorer calibration, although the latter was not assessed in most of validation studies.

Table 2. External Validation of HF Risk Prediction Models

Author, ReferenceValidated Risk ScoreValidation Population/CountryEthnicityDesignSample SizeAge, yTime-Horizon, yDiscriminationAUCCalibrationReclassification
NRI, % (95% CI/P Value)IDI, % (95% CI/P Value)
Butler et al11Framingham HF risk scoreUSA/Health ABC studyMainly whiteProspective cohortMales (n=1315) and female (n=1364)70–795 y0.735 in men and 0.684 in womenNRNRNR
Kalogeropoulos et al16Health ABC scoreCardiovascular Health StudyMainly whiteProspective cohort5335 (400 events)Mean 72.7 (SD: 5.5)50.74 (95% CI, 0.72–0.76)HL χ2=14.72 (P=0.14)NANA
Gupta et al30Health ABC Composition HF risk scoreDallas Heart StudyCaucasians and BlacksProspective cohort254030–65Stage B HF-BSA, 0.73stage B HF-FFM, 0.75stage B HF-height, 0.64NRNANA
Agarwal et al20Framingham-published scoreARIC cohort/USMixedProspective cohort12 0380.610NRNANA
Agarwal et al20Framingham-recalibrated scoreARIC cohort/USAMixedProspective cohort4298 for NHANES and 21, 221 for ARIC/CHS5 (ARIC/CHS cohort)0.762NRNANA
Agarwal et al20Health ABC HF recalibrated scoreARIC cohort/USAMixed/Mainly whiteProspective cohort2145 for ENRICHD and 3640 for VISPNA0.783NRNANA
Agarwal et al20ARIC risk score validation 1ARIC cohort/USAMixed/Mainly whiteProspective cohort516840.785NRNANA
Agarwal et al20ARIC risk score validation 2ARIC cohort/USAMixed/Mainly whiteProspective cohort5168Mean: 51.2 (SD, 10.5)40.797NRNANA
Wannamethe et al23Health ABC risk scoreBritish Heart Regional StudyMainly whiteProspective cohort317040–59110.706NR

ARIC indicates Atherosclerosis Risk in Communities Study; AUC, area under the curve; BSA, body surface area; CHS, Cardiovascular Health Study; CI, confidence interval; Health ABC, Health Aging, and Body Composition study; ENRICHD, Enhancing Recovery in Coronary Heart Disease Patients Trial; FFM, fat-free mass; HF, heart failure; HL, Hosmer–Lemeshow; IDI, Integrative Discriminative Index; NA, not applicable; NHANES, National Health and Nutrition Examination Survey; NR, not reported; NRI, Net Reclassification Index; TIHN, The Health Improvement Network; US, United States; and VISP, Vitamin Intervention for Stroke Prevention Trial.

Single Biomarker or Imaging Data

In the Cardiovascular Health Study, a comparison of the Health ABC score, NT-proBNP, and echocardiographic data for detecting LV dysfunction indicated >5% risk of HF at 5 years in 44% of the cohort and identified 76% of those who developed HF by 5 years.24,25 An abnormal NT-proBNP was present in 29.5% of the cohort and identified 45% of those who developed HF by 10 years; and baseline echocardiography identified reduced ejection fraction in 7.2% of the population, but identified only 12% of those who developed HF by 10 years.

Depending on the method of indexing LV mass (body surface area, fat-free mass, or height), the AUC for the Health ABC HF score (0.73, 0.75, and 0.64, respectively) was greater than that for BNP (0.62, 0.70, and 0.57, respectively) and NT-pro-BNP (0.62, 0.69, and 0.56, respectively; P<0.01 for all). Similar results were seen in the Framingham score except when LV mass was indexed to fat-free mass (0.65, 0.69 and 0.66, and 0.64, respectively). Addition of BNP resulted in an improvement in discrimination of stage B HF (difference in AUC, 0.01–0.03; P<0.05 for all).30 In an elderly at high risk, NT-proBNP (AUC, 0.74) was superior to the Framingham HF score (AUC, 0.63) in predicting asymptomatic LV systolic dysfunction.31

Model Effect

There were no recommendations for using any risk prediction model to estimate the risk of incident HF in either the clinical or the community settings in any of the existing guidelines. There were no studies assessing the effect of adopting HF risk models in clinical practice on the process of care and outcomes of patients.


This systematic review demonstrates the feasibility of global HF risk prediction in the general population. Compared with coronary artery disease and stroke, prediction of incident HF risk is at an early stage and is not incorporated into contemporary clinical or public health practice. Defining risk can be reliably done using a combination of common variables with an acceptable-to-good discrimination, although correcting for overfitting (internal validation) or testing in a new population (external validation) may attenuate discrimination. Implementation of prediction models may identify a wider segment of at-risk population compared with single risk factors. In addition, biomarkers may be used to further categorize patients into different risk strata in concert with the clinical variables.

HF guidelines advocate prevention of progression from at-risk HF stages (A and B) to symptomatic stages (C and D), thus requiring identification of susceptible patients likely to benefit from interventions,7 including lifestyle changes or more intense risk factor modification. Risk communication to patients may motivate adherence; indeed healthy lifestyle is associated with a lower lifetime risk of HF.32 Furthermore, risk scores can be incorporated into electronic medical records, allowing continual passive screening of high-risk subjects with alerts for patients entering higher-risk strata. These risk models may also circumvent uncertainties surrounding the stage B HF definition, which is currently dictated by what can be measured with imaging rather than a complete understanding of risk. In stepwise screening with models as the first step followed by imaging or biomarkers, those with stage B HF may be more reliably referred for interventions known to halt and possibly reverse structural changes that precede clinical HF. With increasing HF risk, LV mass and concentric remodeling increases, supporting the traditional belief that HF risk correlates with structural heart disease.30 HF models may be useful in assessing the use of novel biomarkers for risk prediction or for selecting patients for inclusion in clinical trials. Indeed, investigating novel markers is warranted, especially given the relatively modest additional discriminative ability of natriuretic peptides (BNP or NT-proBNP) beyond that of clinical variables. This finding is not surprising because factors included in models (eg, plasma creatinine, female sex, and age) may increase these biomarkers independently of left ventricular ejection fraction and filling pressures.33 Moreover, several screening studies have found use of natriuretic peptide level as stand-alone test to lack discriminative ability for detecting structural abnormalities among healthy young adults.34,35

Some of the existing models have been statistically underpowered, especially as consideration was not given to the ratio of outcomes to predictors. Models with an inappropriate ratio (<10 events per variable) are less robust, with a performance that shrinks when applied to other populations. Although the overall study size and the prognostic ability of individual predictors is important, a key driver of statistical power is the number of events. These performance metrics mostly originated from the derivation sample, which are optimistic. Some models were internally validated (bootstrap or jackknife validation), providing an idea about their potential performance when applied to a new population. Calibration was not always assessed. A poorly calibrated model may have the same discrimination as a well-calibrated model, necessitating both of these characteristics to be assessed in concert. Attempts to update models by extra variables have focused on discrimination,13,15,16,1820,25 and none of the studies reported change in the calibration. Finally, validation is an important step before a model can be recommended for widespread use. This was limited to only 3 models in US populations. Hence, further validations of existing models are needed to guarantee their generalizability.

HF models have mainly been published as mathematical equations. Complex formulas may not be suitable for clinical application. Indeed, previous research suggests that many clinicians find risk calculation too time consuming and are not convinced of the value of information derived from these models.36 Additional translation efforts are needed to convert predictions equations into user-friendly tools to improve uptake.15 Efforts are needed to derive the appropriate context for defining high-risk status to facilitate their use. It is also important to study whether implementing HF risk prediction affects the behavior of healthcare providers and improves clinical outcomes. Few studies examined the incremental predictive value of novel biomarkers (eg, collagen matrix37,38 and omic markers) that may be useful for risk stratification. Clinical risk models provide good basis to assess the incremental value of new discoveries.

Further limitations to existing models exist. Misclassification of HF events may have occurred as there are no universally agreed specific criteria for diagnosing HF, thus affecting the performance of the models. HF is a heterogeneous syndrome, representing a spectrum of pathogenesis, as well as normal to depressed ejection fraction. Existing studies use multiple criteria to define HF39 with little agreement.40 Inconsistencies were indeed observed in the presently reviewed studies, which may have affected model performance. In most studies, diagnosis did not rely on imaging. Consequently, specific forms of HF, such as HF with reduced ejection fraction and HF with preserved ejection fraction, were not considered. Although there is an overlap between predictors of HF with reduced ejection fraction and HF with preserved ejection fraction, there are differences that merit consideration.41,42 Moreover, HF with reduced ejection fraction and HF with preserved ejection fraction are regarded as distinct pathophysiologic entities, as exemplified by the lack of evidence-based disease modifying therapies for the later. Accordingly, it stands to reason that differing preventative strategies and interventions may exist. Furthermore, the majority of participants included were whites. However, HF risk is known to be heightened in specific groups (eg, blacks), thus supporting the need to incorporate more patients of different ethnic backgrounds.

Although existing models may lead to implementation of prevention interventions, such efforts may be premature, particularly in the absence of external validation studies and impact analysis. The lack of studies evaluating the effect of model use on outcomes should be addressed in future studies. Such an undertaking could potentially strengthen the case for their use in routine clinical or public health practice. The absence of existing studies may simply be an indication of the embryonic nature of the HF prediction field, and of HF prevention in general. This limitation is not specific to HF and does not in itself invalidate the future use of the models. Indeed, even with coronary heart disease, for which prediction models have been in existence because the early 1990s, only a limited number of studies have assessed the effect of their implementation, although their use has been consistently recommended in guidelines. We did not explicitly rank or categorize the quality of existing HF models, as there is no agreed-on scientific system for rating risk prediction model quality. Some argue minimizing risk for potential bias is of critical importance, whereas others contend that a risk score should be judged on ability to perform accurately across diverse settings. Finally, our ability to assess publication bias was limited.

In conclusion, risk models for predicting incident HF have acceptable-to-good discriminative ability. These tools need to be better calibrated and externally validated, and the effect of their use on outcomes assessed before incorporation into clinical practice guidelines. With advancement of our understanding of systemic conditions and cardiac and noncardiac structural and functional abnormalities that predispose to HF risk, better approaches for HF prevention are needed. HF prevention extends beyond simply addressing risk factors or precursors, such as diabetes mellitus, hypertension, and coronary artery disease, especially as the latter is not a sine qua non for the development of HF. HF scores may be viewed as a means for selecting a targeted population that would undergo imaging for detection of asymptomatic structural disease (eg, left ventricular systolic dysfunction), a potential therapeutic target in itself. Successful prevention approaches mandate the correct identification of the target population, underscoring the importance of further research in the performance and use of incident HF risk models.


The Data Supplement is available at

Correspondence to Javed Butler, MD, MPH, Division of Cardiology, Stony Brook University, Health Sciences Center, T-16, Room 080, SUNY at Stony Brook, NY 11794. E-mail


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Risk prediction models for incident heart failure (HF) in asymptomatic populations have the potential to improve risk stratification and augment the practitioner’s ability to prevent HF or to influence its natural history in several ways. These might include communicating risk to patients, offering appropriate patient education, proactive risk factor management, and timely use of imaging to identify asymptomatic structural heart disease for therapeutic intervention, with consequent referral to a cardiologist. Incident HF risk models may also be useful for selecting high-risk patients for inclusion in HF prevention trials or in trials assessing the benefits of therapies for patients with asymptomatic structural heart disease. However, incident HF risk prediction using combinations of clinical and biological variables remains at an early stage in development. Although the discriminative ability of current incident HF prediction models is acceptable, in general, these tools need to be better calibrated and externally validated before incorporation into clinical practice guidelines. It remains unclear which models are cost-effective and best suit population screening needs. The effects of models on clinical and preventative care require further study.