A Practical Statin Recommendation System Based on Real-World Data to Improve LDL-C Management in Secondary Prevention

Abstract

:Statins are considered the cornerstone of secondary prevention in patients with atherosclerotic cardiovascular disease (ASCVD). However, many patients fail to achieve the guide-recommended goal of low-density lipoprotein cholesterol (LDL-C) after statin monotherapy, leading to a high residual risk of cardiovascular events. Owing to individual differences in statin therapy, it is possible first to consider changing the type of statin before adding nonstatin medications in certain patients to improve LDL-C management. We developed and evaluated a statin recommendation system using real-world data. Ensemble learning was performed to develop the recommendation system that integrated the output results of support vector machines (SVM) and the similarity of patients. Model performance was assessed to investigate whether treatment according to the recommended model would increase the proportion of patients with the primary end point. Finally, a total of 3510 patients were enrolled in the development and validation of the recommender system. Of them, 1240 patients received atorvastatin (35.3%), 1714 patients received rosuvastatin (48.8%), and 556 patients received pitavastatin (15.8%). The statin recommendation system could significantly improve LDL-C target rate achievement in the recommended treatment group compared with the nonrecommended treatment group in the validation set (50.8% vs. 31.5%, P < 0.001). This study demonstrated that the statin recommendation system could significantly improve the achievement of LDL-C goals in ASCVD patients, providing a new approach to improve LDL-C management.