Validation of the American Heart Association Predicting Risk of Cardiovascular Disease Events Equations in Diverse Socioeconomic Groups: The All of Us Cohort.

BACKGROUND

In 2023, the American Heart Association PREVENT (Predicting Risk of Cardiovascular Disease Events) equations were introduced as a tool to improve cardiovascular disease (CVD) risk prediction. This study tests their performance in a diverse socioeconomic cohort.

METHODS

We analyzed All of Us participants aged 30 to 79 years without baseline CVD who had required PREVENT input data over a 5.4-year follow-up. Discrimination was assessed using Harrell's C-statistic, with calibration by comparing predicted and observed 5-year CVD rates across 10-year risk deciles. Mean data are ±SD.

RESULTS

We examined 9010 individuals (mean age, 63.0±11.0 years; 45.5% male). Racial and ethnic composition was 61.7% non-Hispanic White, 17.2% non-Hispanic Black, 4.5% multiracial/other, 1.3% non-Hispanic Asian, and 11.2% Hispanic or Latino. The "other" race/ethnic category reflects participants who self-identified as "other" in response to the, "Which category describes you?" item in the Basics survey. Over a mean follow-up of 3.6±1.8 years, 9.0% experienced a cardiovascular event. The mean 10-year predicted risks were 0.23±0.17 for total CVD, 0.13±0.10 for atherosclerotic CVD (ASCVD), and 0.19±0.17 for heart failure. The predicted-to-observed rate ratios were 5.3 for CVD and 3.3 for ASCVD. The C statistic for the overall sample was 0.732 (95% CI, 0.718-0.752) for CVD, 0.716 (95% CI, 0.698-0.741) for ASCVD, and 0.777 (95% CI, 0.757-0.800) for heart failure.

CONCLUSIONS

The PREVENT equations showed strong discrimination across all strata in this national cohort. Overprediction of CVD events likely reflects baseline differences in comorbidity burden between the PREVENT development cohort and this All of Us cohort, particularly due to the exclusion of individuals missing estimated glomerular filtration rate, a variable not routinely collected and likely missing, not at random. Strong discrimination supports potential clinical utility, though further work is needed to improve calibration in this population.