Machine learning-based high-benefit approach versus traditional high-risk approach in statin therapy: the Shizuoka Kokuho database study.

ABSTRACT: Statins are widely prescribed for the primary prevention of cardiovascular diseases, yet individual responses vary, necessitating personalized treatment strategies. Conventional approaches prioritize treating high-risk patients, but advancements in machine learning now enable the estimation of conditional average treatment effects (CATE), offering opportunities to enhance treatment effectiveness. This study utilized the Shizuoka Kokuho Database to investigate heterogeneity in statin treatment effects. A 1:1 propensity score-matched cohort design was employed to evaluate the effect of statins in preventing a composite outcome, cardiovasuclar and cerebrovascular events and all-cause mortality. CATE was estimated using the causal forest model, an advanced ensemble machine learning technique.

SUBMITTER: Watanabe R 

PROVIDER: S-EPMC12307890 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature