A super-combo-drug test to detect adverse drug events and drug interactions from electronic health records in the era of polypharmacy

Anqi Zhu, Donglin Zeng, Li Shen, Xia Ning, Lang Li, Pengyue Zhang

Research output: Contribution to journalArticle

Abstract

Pharmacoinformatics research has experienced a great deal of successes in detecting drug-induced adverse events (AEs) using large-scale health record databases. In the era of polypharmacy, pharmacoinformatics faces many new challenges, and two significant challenges are to detect high-order drug interactions and to handle strongly correlated drugs. In this article, we propose a super-combo-drug test (SupCD-T) to address the aforementioned two challenges. SupCD-T detects drug interactions by identifying optimal drug combinations with increased AE risks. In addition, SupCD-T increases the statistical powers to detect single-drug effects by combining strongly correlated drugs. Although SupCD-T does not distinguish single-drug effects from their combination effects, it is noticeably more powerful in selecting an individual drug effect in the multiple regression analysis, where confounding justification between two correlated drugs reduces the power in testing the individual drug effects on AEs. Our simulation studies demonstrate that SupCD-T has generally better power comparing with the multiple regression analysis. In addition, SupCD-T is able to select meaningful drug combinations (eg, highly coprescribed drugs). Using electronic health record database, we illustrate the utility of SupCD-T and discover a number of drug combinations that have increased risk in myopathy. Some novel drug combinations have not yet been investigated and reported in the pharmacology research.

Original languageEnglish (US)
JournalStatistics in Medicine
DOIs
StateAccepted/In press - Jan 1 2020

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Keywords

  • adverse event
  • drug interaction
  • EHR
  • pharmacoinformatics
  • SupCD-T

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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