Data mining results for reduction of adverse drug-drug interaction events in older adults are clinically applicable

Abstract

Background: Polypharmacy increases the risks of drug-drug interactions (DDIs), which increase the risk of adverse drug events (ADEs). Data mining techniques have described high-order (three or four) drug combinations as high risk for ADE and suggested alternative low-risk combinations, but were not able to establish clinical feasibility. Objective: The objective of this study is to evaluate the clinical feasibility of potentially low-risk drug combinations identified in previous work through a data mining technique as substitutes for high-risk high-order drug combinations. Methods: This expert-review study utilizes potentially high-risk high-order drug combinations and complementary low-risk combinations identified in previously published work from Medicare fee-for-service (2018) and MarketScan Medicare Supplemental claims emergency department (ED) data (2012-2020). We convened a panel of clinical experts to adjudicate the list for clinical feasibility. A standard rubric was developed, and the reviewers indicated whether the data-driven substitutions were always, sometimes, or never clinically acceptable and provided comments. Two experts, not involved in the initial panel review, reviewed the results produced by the panel to determine agreement or disagreement among reviewers. The results of this clinical review are presented. Results: Of the 1904 high-/low-risk combinations reviewed, 606 were deemed always acceptable; 588 were anticoagulant ADEs, 18 were opioid, and none were antidiabetic. The 20 most frequently observed high-risk combinations were all anticoagulant ADEs; 11 of the top 20 involved proton pump inhibitor substitution and 9 involved statin substitutions. Conclusion: High-risk high-order DDIs with low-risk alternatives as identified in Medicare fee-for-service and MarketScan databases are identifiable, are clinically acceptable, and could decrease ADE-related ED visits.