Risk factors and prediction models for incident heart failure with reduced and preserved ejection fraction

• Type of Material: Article
• Authors: Liam Gaziano, VA Boston Healthcare System; Kelly Cho, VA Boston Healthcare System; Luc Djousse, VA Boston Healthcare System; Petra Schubert, VA Boston Healthcare System; Ashley Galloway, VA Boston Healthcare System; Yuk-Lam Ho, VA Boston Healthcare System; Katherine Kurgansky, VA Boston Healthcare System; David R. Gagnon, VA Boston Healthcare System; John P. Russo, VA Boston Healthcare System; Emanuele Di Angelantonio, University of Cambridge; Angela M. Wood, University of Cambridge; John Danesh, University of Cambridge; John Michael Gaziano, VA Boston Healthcare System; Adam S. Butterworth, University of Cambridge; Peter Wilson, Emory University; Jacob Joseph, VA Boston Healthcare System
• Language: English
• Date: 2021-09-16
• Publisher: WILEY PERIODICALS, INC
• Publication Version: Final Published Version
• Copyright Statement: © 2021 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology. This article has been contributed to by US Government employees and their work is in the public domain in the USA.
• License: Creative Commons Attribution-NonCommercial 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1002/ehf2.13429
• Title of Journal or Parent Work: ESC HEART FAILURE
• Volume: 8
• Issue: 6
• Start Page: 4893
• End Page: 4903
• Grant/Funding Information: This research was supported by VA Merit Awards I01 CX001025 and I01 CX001922‐01. This publication does not represent the views of the Department of Veterans Affairs or the U.S. Government. Support for VA/CMS data provided by the Department of Veterans Affairs, VA Health Services Research and Development Service, VA Information Resource Center (Project Numbers SDR 02‐237 and 98‐004).
• Keywords: LEFT-VENTRICULAR HYPERTROPHY; Heart failure with reduced ejection fraction; Science & Technology; COHORT; PHENOTYPES; Heart failure with preserved ejection fraction; Life Sciences & Biomedicine; Cardiac & Cardiovascular Systems; Heart failure; INHIBITION; POPULATION; HYPERTENSION; Cardiovascular System & Cardiology; DYSFUNCTION; Electronic health records; PREVENTION; ASSOCIATION; Risk prediction; SURVIVAL
• Research Categories: Health Sciences, Medicine and Surgery

Abstract

Aims: This study aims to develop the first race-specific and sex-specific risk prediction models for heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF). Methods and results: We created a cohort of 1.8 million individuals who had an outpatient clinic visit between 2002 and 2007 within the Veterans Affairs (VA) Healthcare System and obtained information on HFpEF, HFrEF, and several risk factors from electronic health records (EHR). Variables were selected for the risk prediction models in a ‘derivation cohort’ that consisted of individuals with baseline date in 2002, 2003, or 2004 using a forward stepwise selection based on a change in C-index threshold. Discrimination and calibration were assessed in the remaining participants (internal ‘validation cohort’). A total of 66 831 individuals developed HFpEF, and 92 233 developed HFrEF (52 679 and 71 463 in the derivation cohort) over a median of 11.1 years of follow-up. The HFpEF risk prediction model included age, diabetes, BMI, COPD, previous MI, antihypertensive treatment, SBP, smoking status, atrial fibrillation, and estimated glomerular filtration rate (eGFR), while the HFrEF model additionally included previous CAD. For the HFpEF model, C-indices were 0.74 (SE = 0.002) for white men, 0.76 (0.005) for black men, 0.79 (0.015) for white women, and 0.77 (0.026) for black women, compared with 0.72 (0.002), 0.72 (0.004), 0.77 (0.017), and 0.75 (0.028), respectively, for the HFrEF model. These risk prediction models were generally well calibrated in each race-specific and sex-specific stratum of the validation cohort. Conclusions: Our race-specific and sex-specific risk prediction models, which used easily obtainable clinical variables, can be a useful tool to implement preventive strategies or subtype-specific prevention trials in the nine million users of the VA healthcare system and the general population after external validation.