Predictive Accuracy of Heart Failure-Specific Risk Equations in an Electronic Health Record-Based Cohort

Circulation: Heart Failure, Ahead of Print.
Background:Guidelines recommend identification of individuals at risk for heart failure (HF). However, implementation of risk-based prevention strategies requires validation of HF-specific risk scores in diverse, real-world cohorts. Therefore, our objective was to assess the predictive accuracy of the Pooled Cohort Equations to Prevent HF within a primary prevention cohort derived from the electronic health record.Methods:We retrospectively identified patients between the ages of 30 to 79 years in a multi-center integrated healthcare system, free of cardiovascular disease, with available data on HF risk factors, and at least 5 years of follow-up. We applied the Pooled Cohort Equations to Prevent HF tool to calculate sex and race-specific 5-year HF risk estimates. Incident HF was defined by theInternational Classification of Diseasescodes. We assessed model discrimination and calibration, comparing predicted and observed rates for incident HF.Results:Among 31 256 eligible adults, mean age was 51.4 years, 57% were women and 11% Black. Incident HF occurred in 568 patients (1.8%) over 5-year follow-up. The modified Pooled Cohort Equations to Prevent HF model for 5-year risk prediction of HF had excellent discrimination in White men (C-statistic 0.82 [95% CI, 0.79–0.86]) and women (0.82 [0.78–0.87]) and adequate discrimination in Black men (0.69 [0.60–0.78]) and women (0.69 [0.52–0.76]). Calibration was fair in all race-sex subgroups (χ2 <20).Conclusions:A novel sex- and race-specific risk score predicts incident HF in a real-world, electronic health record-based cohort. Integration of HF risk into the electronic health record may allow for risk-based discussion, enhanced surveillance, and targeted preventive interventions to reduce the public health burden of HF.



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