LAUSR

AIM The aim of this study is to evaluate and compare the relative performance of the tree-based scan statistic (TreeScan) with the crude cohort study, Bayesian confidence propagation neural network (BCPNN), and Gamma Poisson Shrinker (GPS) in detecting statin-related adverse events (AEs) in an electronic healthcare database. METHODS Data from a Chinese healthcare database from 2010 to 2016 were evaluated. We identified statin users based on prescription information in their out-/in-patient records, and AEs were defined according to the ICD-10 codes in patients' diagnosis records. TreeScan was applied to detect AE signals related to statin use and was compared with three other methods based on sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, the Youden index, area under the precision-recall curve (AUC-PR), and the area under the receiver operating characteristic curve (AUC). RESULTS A total of 224 187 patients were enrolled and divided into 85 758 statin users and 138 429 non-users. TreeScan generated 29 positive signals, of which 9 were known AEs. The sensitivities of TreeScan, BCPNN, and GPS were all 69.2%, which was higher than that of the crude cohort study (46%). The specificity (82.3%), PPV (31.0%), NPV (95.9%), accuracy (81.0%), Youden index (51.5%), and AUC (75.8%) of TreeScan were the highest among the four methods. CONCLUSION TreeScan outperformed the crude cohort, BCPNN, and GPS in detecting statin-related AEs in an electronic healthcare database. Therefore, it can be used as a complementary tool for other signal detection methods in drug safety surveillance.