Signal Detection of Adverse Events Associated with Trastuzumab in a Cohort of Elderly Patients with Breast Cancer

Aim: Utilization of signal detection methods in longitudinal claims data can improve post-marketing drug surveillance, but to date there has been limited application. The aim of this study is to use 3 approaches, the proportional reporting ratio, Gamma Poisson Shrinker, and tree-based scan statistic in detecting adverse drug events (ADEs) attributed to trastuzumab using an administrative claims dataset. Methods: Using data from the Texas Cancer Registry and SEER linked to Medicare from 2010 to 2013, we conducted 1:2 propensity score matching. Breast cancer HER2+ patients treated with trastuzumab in addition to standard chemotherapy were matched to HER2– patients treated with standard chemotherapy. Inpatient and outpatient encounters up to 6 months from start of therapy were used to identify adverse events. Results: A total of 4191 patients were included in the study. Across all methods, use of trastuzumab generated signals on 9 distinct body systems. Cardiomyopathy and heart valve disease were the most consistently detected signals. Clinical review determined that most signals represented known ADEs. Conclusions: We showed that claims data can be used to complement current ADE monitoring using common data mining methods with propensity score matching. Our analysis identified all expected ADEs associated with trastuzumab, and additional signals of valvular heart disorders.