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

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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.

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Author information

Author notes

  1. Yi Shi and Chien-Wei Chiang contributed equally.

Authors and Affiliations

  1. Department of Biostatistics and Health Data Science, Indiana University, 410 West 10th Street, Suite 3000, Indianapolis, IN, USA

    Yi Shi, Anna Sun, Yuedi Yang & Pengyue Zhang

  2. Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA

    Chien-Wei Chiang & Lang Li

  3. School of Medicine, Indiana University, Indianapolis, IN, USA

    Kathleen T. Unroe

  4. Center for Aging Research, Regenstrief Institute, Indianapolis, IN, USA

    Kathleen T. Unroe

  5. College of Pharmacy, The Ohio State University, 500 West 12th Ave., Columbus, OH, USA

    Ximena Oyarzun-Gonzalez & Macarius Donneyong

  6. Department of Emergency Medicine, The Ohio State University, Columbus, OH, USA

    Katherine M. Hunold & Jeffrey Caterino

  7. Department of Internal Medicine, The Ohio State University, Columbus, OH, USA

    Jeffrey Caterino

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  1. Yi Shi
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Corresponding authors

Correspondence to Macarius Donneyong or Pengyue Zhang.

Ethics declarations

Funding

This work is supported by NIH under R01AG071018.

Conflicts of interest

All authors declare no conflict of interest.

Ethics approval

This retrospective observational study was approved by the Institutional Review Board (IRB) at The Ohio State University (ID: 2020H0546).

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Data availability

MarketScan data is available from IBM, and Medicare data is available from Center of Medicare and Medicaid Services (CMS).

Code availability

Sample codes can be found in Appendix.

Author contributions

PZ conceived the study; YS and CW conducted data analysis; YS, CW, MD, and PZ drafted the manuscript; KH, KU, JC, AS, YY, LL, MD, and PZ interpreted the results; All authors critically revised and gave final approval of the manuscript.

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Shi, Y., Chiang, CW., Unroe, K.T. et al. Application of an Innovative Data Mining Approach Towards Safe Polypharmacy Practice in Older Adults. Drug Saf 47, 93–102 (2024). https://doi.org/10.1007/s40264-023-01370-9

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