Statin-Associated Adverse Events Prediction and Drug-Drug Interactions for Cardiovascular Disease Patients from Retrospective Claims Data

Abstract

Statins are commonly used to lower cholesterol levels for cardiovascular disease (CVD) patients in the primary and secondary prevention of acute events. 26% of American adults over age 40 used statins in 2012 and an estimated 26.4 million U.S. adults could benefit from statin use. Although statins are generally well tolerated and show a relatively good safety profile, concerns have been raised regarding statin associated adverse events (AEs) especially muscle related events, leading to medication non-adherence and discontinuation. Besides, AEs are often caused by potential drug-drug interactions (DDIs) which are responsible for up to 2.8% of hospital admissions[58]. Among CVD patients, combination therapy of statins and other medications is highly likely, which results in altered absorption, distribution, metabolism, or excretion of statins and thus causes adverse events. Traditional AE management approaches may include a statin therapy holiday, lower statin dosage, an alternative statin agent, or non-statin cholesterol-lowering therapy. Currently, there are no tools to effectively predict and reduce the risk of AEs prior to statin therapy initiation. In addition, no population-based studies have focused on a specific statin and a specific interacting drug and differentiated their risks among different study time periods. In this study, we investigated the effect of combination therapy of simvastatin and several pre-defined high risk interacting drugs, which belong to cytochrome P450 (CYP) 3A4 and/or organic anion transporting polypeptide (OATP) inhibitors, in CVD patients who used simvastatin for secondary prevention. This could provide some evidence and recommendations for selected interacting drugs used in CVD patients. In addition, we aimed to build a model to predict statin-associated AEs that may reduce the risk of statin associated adverse events and the rate of statin therapy cessation. Several machine learning methods were applied, such as generalized linear model (GLM), support vector machine (SVM), decision tree, random forest, and artificial neural network (ANN). Models were developed and compared for their performance. The best model was selected based on the best performance.