ArticleDrug safety2024
Application of an Innovative Data Mining Approach Towards Safe Polypharmacy Practice in Older Adults.
Article in Drug safety, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Exploring Risks of Adverse Drug Events From Single Drug to Three-Drug Combination.Clinical and translational science · 2026Article
- Patterns of deprescribing after an emergency department visit due to adverse drug events among concomitant users of antithrombotics and other medications.medRxiv : the preprint server for health sciences · 2026Article
- Potential Drug Dose-Specific Adverse Three-Drug Combinations: A US Insurance Claims Data-Based Study.Pharmacoepidemiology and drug safety · 2025Article
- Data mining results for reduction of adverse drug-drug interaction events in older adults are clinically applicable.Therapeutic advances in drug safety · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
introductionPolypharmacy is common and is associated with higher risk of adverse drug event (ADE) among older adults. Knowledge on the ADE risk level of exposure to different drug combinations is critical for safe polypharmacy practice, while approaches for this type of knowledge discovery are limited. The objective of this study was to apply an innovative data mining approach to discover high-risk and alternative low-risk high-order drug combinations (e.g., three- and four-drug combinations).
methodsA cohort of older adults (≥ 65 years) who visited an emergency department (ED) were identified from Medicare fee-for-service and MarketScan Medicare supplemental data. We used International Classification of Diseases (ICD) codes to identify ADE cases potentially induced by anticoagulants, antidiabetic drugs, and opioids from ED visit records. We assessed drug exposure data during a 30-day window prior to the ED visit dates. We investigated relationships between exposure of drug combinations and ADEs under the case-control setting. We applied the mixture drug-count response model to identify high-order drug combinations associated with an increased risk of ADE. We conducted therapeutic class-based mining to reveal low-risk alternative drug combinations for high-order drug combinations associated with an increased risk of ADE.
resultsWe investigated frequent high-order drug combinations from 8.4 million ED visit records (5.1 million from Medicare data and 3.3 million from MarketScan data). We identified 5213 high-order drug combinations associated with an increased risk of ADE by controlling the false discovery rate at 0.01. We identified 1904 high-order, high-risk drug combinations had potential low-risk alternative drug combinations, where each high-order, high-risk drug combination and its corresponding low-risk alternative drug combination(s) have similar therapeutic classes.
conclusionsWe demonstrated the application of a data mining technique to discover high-order drug combinations associated with an increased risk of ADE. We identified high-risk, high-order drug combinations often have low-risk alternative drug combinations in similar therapeutic classes.
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