10-Year Risk Equations for Incident Heart Failure in the General Population
Key result
Race- and sex-specific equations accurately predict 10-year incident heart failure risk across diverse cohorts.
- n=33,010
Why the study?
No validated risk prediction tools for 10-year incident heart failure are currently in use to guide primary prevention strategies.
Study Design
Type
Cohort (n=33,010)
Multicenter
Yes
Structured PICO
P
Population
33,010 men and women aged 30 to 79 years, free of cardiovascular disease at baseline, followed for at least 12 years to derive and validate 10-year risk equations for incident heart failure.
O
Outcome
Incident heart failure at 10 yearshard clinical
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Key expert perspectives
Captured external expert commentary on this paper, strongest first. Original sources are linked where available.
“Once someone develops symptoms of heart failure, the window for prevention has closed, which is a missed opportunity, given that the risk of dying in the five years after diagnosis is 50 percent, similar to a cancer diagnosis.”
“A new cardiovascular disease risk calculator was needed, particularly one that includes measures of CKM syndrome, which is highly prevalent in the U.S. The new PREVENT risk calculator enables clinicians to quantify this risk and may help people receive preventive care or treatment earlier to reduce CVD risk.”
“There have been prior risk estimation approaches for heart failure, but they've always required more expensive or intensive testing. We can estimate your risk, but we have to do an ultrasound of your heart first or EKGs.”
Implication
A newly derived and validated race- and sex-specific 10-year risk equation can accurately predict incident heart failure in the general population using readily available clinical parameters.
Abstract
BACKGROUND: Primary prevention strategies to mitigate the burden of heart failure (HF) are urgently needed. However, no validated risk prediction tools are currently in use. OBJECTIVES: This study sought to derive 10-year risk equations of developing incident HF. METHODS: Race- and sex-specific 10-year risk equations for HF were derived and validated from individual-level data from 7 community-based cohorts with at least 12 years of follow-up. Participants who were recruited between 1985 and 2000, between 30 to 79 years, and were free of cardiovascular disease at baseline were included to create a pooled cohort (PC) and were randomly split for derivation and internal validation. Model performance was also assessed in 2 additional cohorts. RESULTS: In the derivation sample of the PC (n = 11,771), 58% were women, 22% were black with a mean age of 52 ± 12 years, and HF occurred in 1,339 participants. Predictors of HF included in the race-sex-specific models were age, blood pressure (treated or untreated), fasting glucose (treated or untreated), body mass index, cholesterol, smoking status, and QRS duration. The PC equations to Prevent HF model had good discrimination and strong calibration in internal and external validation cohorts. A web-based tool was developed to facilitate clinical application of this tool. CONCLUSIONS: The authors present a contemporary analysis from 33,010 men and women demonstrating the utility of the sex- and race-specific 10-year PC equations to Prevent HF risk score, which integrates clinical parameters readily available in primary care settings. This tool can be useful in risk-based decision making to determine who may merit intensive screening and/or targeted prevention strategies.
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Cite This Study
Khan et al. (2019) conducted a cohort in Incident heart failure (n=33,010). 10-year PC equations to Prevent HF risk score was evaluated on Incident heart failure. Race- and sex-specific 10-year risk equations for incident heart failure demonstrated good discrimination and strong calibration in internal and external validation cohorts of 33,010 individuals.