Elevated Lipoprotein(a) Independently Increases Risk for Short-Term Atherosclerotic Cardiovascular Events in Machine Learning Predictive Models

BACKGROUND: Lipoprotein(a) [Lp(a)] is underutilized in short-term atherosclerotic cardiovascular disease (ASCVD) risk prediction. OBJECTIVES: This study investigates Lp(a) contribution to short-term ASCVD event prediction using contemporary real-world data and machine learning (ML). METHODS: A cohort of 731,983 individuals from a claims database was used to investigate the association of Lp(a) with incident ASCVD and all-cause mortality using Cox proportional hazards models. Novel ML models were developed to predict incident ASCVD events at 1, 2, and 3 years after Lp(a) testing. The models were validated in an independent cohort of 53,930 patients. RESULTS: An increase of 50 nmol/L in Lp(a) was independently associated with incident ASCVD events (HR: 1.072; 95% CI: 1.059-1.084) and all-cause mortality (HR: 1.041; 95% CI: 1.015-1.068) after adjustment for age, sex, and race/ethnicity. Novel ML models featuring Lp(a) predicted incident ASCVD events at 1, 2, and 3 years with robust discrimination (C-statistic: 0.83-0.84) in both the derivation and validation cohorts. Modest underestimation of risk was observed in the validation cohort for the 1-year model (calibration slope 1.25). Lp(a) contributed more to 1-year ASCVD prediction than smoking, diabetes, and other lipid parameters. Inclusion of Lp(a) in the 1-year model led to an integrated discrimination improvement of 0.03 and an optimal net reclassification improvement of 10% at a risk threshold of 26%. CONCLUSIONS: Lp(a) is a significant predictor of short-term ASCVD risk. Assessing Lp(a) and imminent ASCVD risk may assist in identifying patients who may benefit from escalation of preventative therapies.