Application of an Innovative Data Mining Approach Towards Safe Polypharmacy Practice in Older Adults

Yi Shi, Chien-Wei Chiang, Kathleen T. Unroe, Ximena Oyarzun-Gonzalez, Anna Sun, Yuedi Yang, Katherine M. Hunold, Jeffrey Caterino, Lang Li, Macarius Donneyong () and Pengyue Zhang ()
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Yi Shi: Indiana University
Chien-Wei Chiang: The Ohio State University
Kathleen T. Unroe: Indiana University
Ximena Oyarzun-Gonzalez: The Ohio State University
Anna Sun: Indiana University
Yuedi Yang: Indiana University
Katherine M. Hunold: The Ohio State University
Jeffrey Caterino: The Ohio State University
Lang Li: The Ohio State University
Macarius Donneyong: The Ohio State University
Pengyue Zhang: Indiana University

Drug Safety, 2024, vol. 47, issue 1, No 8, 93-102

Abstract: Abstract Introduction Polypharmacy 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). Methods A 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. Results We 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. Conclusions We 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.

Date: 2024
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DOI: 10.1007/s40264-023-01370-9

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