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

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 the codes. 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 (χ<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.