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Review
. 2017 May 2;6(5):e005231.
doi: 10.1161/JAHA.116.005231.

Risk for Incident Heart Failure: A Subject-Level Meta-Analysis From the Heart "OMics" in AGEing (HOMAGE) Study

Lotte Jacobs  1 , Ljupcho Efremov  1 , João Pedro Ferreira  2 , Lutgarde Thijs  1 , Wen-Yi Yang  1 , Zhen-Yu Zhang  1 , Roberto Latini  3 , Serge Masson  3 , Nera Agabiti  4 , Peter Sever  5 , Christian Delles  6 , Naveed Sattar  6 , Javed Butler  7 , John G F Cleland  8   9 , Tatiana Kuznetsova  1 , Jan A Staessen  10 , Faiez Zannad  2 ; Heart “OMics” in AGEing (HOMAGE) investigators
Collaborators, Affiliations
Review

Risk for Incident Heart Failure: A Subject-Level Meta-Analysis From the Heart "OMics" in AGEing (HOMAGE) Study

Lotte Jacobs et al. J Am Heart Assoc. .

Abstract

Background: To address the need for personalized prevention, we conducted a subject-level meta-analysis within the framework of the Heart "OMics" in AGEing (HOMAGE) study to develop a risk prediction model for heart failure (HF) based on routinely available clinical measurements.

Methods and results: Three studies with elderly persons (Health Aging and Body Composition [Health ABC], Valutazione della PREvalenza di DIsfunzione Cardiaca asinTOmatica e di scompenso cardiaco [PREDICTOR], and Prospective Study of Pravastatin in the Elderly at Risk [PROSPER]) were included to develop the HF risk function, while a fourth study (Anglo-Scandinavian Cardiac Outcomes Trial [ASCOT]) was used as a validation cohort. Time-to-event analysis was conducted using the Cox proportional hazard model. Incident HF was defined as HF hospitalization. The Cox regression model was evaluated for its discriminatory performance (area under the receiver operating characteristic curve) and calibration (Grønnesby-Borgan χ2 statistic). During a follow-up of 3.5 years, 470 of 10 236 elderly persons (mean age, 74.5 years; 51.3% women) developed HF. Higher age, BMI, systolic blood pressure, heart rate, serum creatinine, smoking, diabetes mellitus, history of coronary artery disease, and use of antihypertensive medication were associated with increased HF risk. The area under the receiver operating characteristic curve of the model was 0.71, with a good calibration (χ2 7.9, P=0.54). A web-based calculator was developed to allow easy calculations of the HF risk.

Conclusions: Simple measurements allow reliable estimation of the short-term HF risk in populations and patients. The risk model may aid in risk stratification and future HF prevention strategies.

Keywords: heart failure; meta‐analysis; risk factor; risk prediction.

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Figures

Figure 1
Figure 1
Flow chart of studies and participants included in the analysis. HF indicates heart failure.
Figure 2
Figure 2
Sex‐adjusted cumulative heart failure (HF) incidence per age group.
Figure 3
Figure 3
Calibration is shown. Observed (open bar) vs expected (solid bar) number of heart failure events across deciles of 5‐year heart failure risk.
Figure 4
Figure 4
Calibration per individual study. Observed (open bar) vs expected (solid bar) number of heart failure events across deciles of 5‐year risk for the Health ABC (Health Aging and Body Composition), PREDICTOR (Valutazione della PREvalenza di DIsfunzione Cardiaca asinTOmatica e di scompenso cardiac), and PROSPER (Prospective Study of Pravastatin in the Elderly at Risk) studies, which were used to develop the heart failure risk function. ASCOT (Anglo‐Scandinavian Cardiac Outcomes Trial) was used as an external validation cohort.
See this image and copyright information in PMC

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