Faculty Collaboration Database

Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort. PLoS Comput Biol 2021 Jul;17(7):e1009053

Date

07/07/2021

Pubmed ID

34228716

Pubmed Central ID

PMC8284671

DOI

10.1371/journal.pcbi.1009053

Scopus ID

2-s2.0-85109425000 (requires institutional sign-in at Scopus site)   31 Citations

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).

Author List

Datta A, Flynn NR, Barnette DA, Woeltje KF, Miller GP, Swamidass SJ

Author

Keith F. Woeltje MD, PhD Associate Dean, Professor in the Medicine department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Adolescent
Adult
Aged
Aged, 80 and over
Algorithms
Anti-Inflammatory Agents, Non-Steroidal
Chemical and Drug Induced Liver Injury
Computational Biology
Drug Interactions
Electronic Health Records
Female
Humans
Liver
Machine Learning
Male
Middle Aged
Models, Statistical
Retrospective Studies
Young Adult