Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort.

Journal: PLoS computational biology
Published Date: Jul 6, 2021

Abstract

Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse events. In this study, we investigated hospitalizations' data to study drug interactions with non-steroidal anti-inflammatory drugs (NSAIDS) that result in drug-induced liver injury (DILI). We propose a logistic regression based machine learning algorithm that unearths several known interactions from an EHR dataset of about 400,000 hospitalization. Our proposed modeling framework is successful in detecting 87.5% of the positive controls, which are defined by drugs known to interact with diclofenac causing an increased risk of DILI, and correctly ranks aggregate risk of DILI for eight commonly prescribed NSAIDs. We found that our modeling framework is particularly successful in inferring associations of drug-drug interactions from relatively small EHR datasets. Furthermore, we have identified a novel and potentially hepatotoxic interaction that might occur during concomitant use of meloxicam and esomeprazole, which are commonly prescribed together to allay NSAID-induced gastrointestinal (GI) bleeding. Empirically, we validate our approach against prior methods for signal detection on EHR datasets, in which our proposed approach outperforms all the compared methods across most metrics, such as area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC).

Authors

  • Arghya Datta
    Department of Computer Science and Engineering, Washington University in Saint Louis, Saint Louis, Missouri, United States of America.
  • Noah R Flynn
    Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, Missouri, United States of America.
  • Dustyn A Barnette
    Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
  • Keith F Woeltje
    Department of Internal Medicine, Washington University School of Medicine, Saint Louis, Missouri, United States of America.
  • Grover P Miller
    Department of Biochemistry and Molecular Biology, University of Arkansas for Medical Sciences , Little Rock, Arkansas 72205, United States.
  • S Joshua Swamidass
    Department of Computer Science and Engineering, McKelvey School of Engineering, Washington University in St. Louis, St. Louis, Missouri.

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