Cardiovascular Risk Prediction in Men and Women Aged Under 50 Years Using Routine Care Data

Abstract

BACKGROUND: Prediction models for risk of cardiovascular events generally do not include young adults, and cardiovascular risk factors differ between women and men. Therefore, this study aimed to develop prediction models for first-ever cardiovascular event risk in men and women aged 30 to 49 years. METHODS AND RESULTS: We included patients aged 30 to 49 years without cardiovascular disease from a Dutch routine care database. Outcome was defined as first-ever cardiovascular event. Our reference models were sex-specific Cox proportional hazards models based on traditional cardiovascular predictors, which we compared with models using 2 predictor subsets with the 20 or 50 most important predictors based on the Cox elastic net model regularization coefficients. We assessed the C-index and calibration curve slopes at 10 years of follow-up. We stratified our analyses based on 30-to 39-year and 40-to 49-year age groups at baseline. We included 542 141 patients (mean age 39.7, 51% women). During follow-up, 10 767 cardiovascular events occurred. Discrimination of reference models including traditional cardiovascular predictors was moderate (women: C-index, 0.648 [95% CI, 0.645– 0.652]; men: C-index, 0.661 [95%CI, 0.658– 0.664]). In women and men, the Cox proportional hazard models including 50 most important predictors resulted in an increase in C-index (0.030 and 0.012, respec-tively), and a net correct reclassification of 3.7% of the events in women and 1.2% in men compared with the reference model. CONCLUSIONS: Sex-specific electronic health record-derived prediction models for first-ever cardiovascular events in the general population aged <50 years have moderate discriminatory performance. Data-driven predictor selection leads to identifica-tion of nontraditional cardiovascular predictors, which modestly increase performance of models.