C-C5-01: Drug Safety Data Mining with a Tree-Based Scan Statistic

摘要

Background/AimsActive post-marketing drug safety surveillance has traditionally focused on predefined drug-adverse event (AE) pairs. However, evaluating predefined pairs does not illuminate unsuspected potential AEs. Drug safety data mining is a surveillance approach that formally evaluates the relationship between medications and a very large number of AEs. We used varying specifications of the tree-based scan statistic data mining method (TreeScan) to search for adverse events among clozapine drug users. MethodsElectronic health records from three HMO Research Network health plans were assessed. We used TreeScan to evaluate a hierarchical clinical classification to identify signals of excess risk during prevalent drug exposure as compared to unexposed time. The test statistic - a Poisson based log likelihood ratio - is adjusted for multiple testing inherent in the many potential AEs evaluated. Four alternate specifications were incorporated: ramp-up periods of 180 and 400 days and outcome definitions using inpatient plus outpatient diagnoses and inpatient diagnoses only. For each drug and specification, we calculated expected and observed counts for each level of the hierarchical tree, adjusting for age, sex, and health plan. ResultsWe identified 242,000 to 580,000 exposed clozapine days and 150 to 345 exposed outcomes across the different specifications. Both ramp-up periods found 17 unique statistical signals using inpatient plus outpatient diagnoses. Of those, several represent confounding by indication (three signals in mental health, two for injury and poisoning) and others are known AEs (e.g., convulsions, hypotension, GI system). Limiting outcomes to the inpatient setting reduced the number of signals to 14 (180-day ramp-up) and 10 (400-day ramp-up). Overall, the inpatient-only AE signals were in the same clinical systems, with some exceptions. The inpatient specification signaled for circulatory events and diseases of the heart, but hypotension was no longer found. Genitourinary AEs signaled using inpatient plus outpatient diagnoses but were not identified using inpatient diagnoses only. ConclusionsData mining using electronic health records is an important complement to other post-marketing drug safety research. Once specifications are finalized, TreeScan will be applied to assess the safety of over 100 oral outpatient medications.