DDIWAS: High-throughput electronic health record-based screening of drug-drug interactions
Year: 2021
Type: article
Abstract: OBJECTIVE: We developed and evaluated Drug-Drug Interaction Wide Association Study (DDIWAS). This novel method detects potential drug-drug interactions (DDIs) by leveraging data from the electronic health record (EHR) allergy list. MATERIALS AND METHODS: To identify potential DDIs, DDIWAS scans for drug pairs that are frequently documented together on the allergy list. Using deidentified medical records, we tested 616 drugs for potential DDIs with simvastatin (a common lipid-lowering drug) and amlodipine (a common blood-pressure lowering drug). We evaluated the performance to rediscover known DDIs using existing knowledge bases and domain expert review. To validate potential novel DDIs, we manually reviewed patient charts and searched the literature. RESULTS: DDIWAS replicated 34 known DDIs. The positive predictive value to detect known DDIs was 0.85 and 0.86 for simvastatin and amlodipine, respectively. DDIWAS also discovered potential novel interactions between simvastatin-hydrochlorothiazide, amlodipine-omeprazole, and amlodipine-valacyclovir. A software package to conduct DDIWAS is publicly available. CONCLUSIONS: In this proof-of-concept study, we demonstrate the value of incorporating information mined from existing allergy lists to detect DDIs in a real-world clinical setting. Since allergy lists are routinely collected in EHRs, DDIWAS has the potential to detect and validate DDI signals across institutions.
Authors: Patrick Wu, Scott D Nelson, Juan Zhao, Cosby A Stone Jr, QiPing Feng, Qingxia Chen, Eric A Larson, Bingshan Li, Nancy J Cox, C Michael Stein, Elizabeth J Phillips, Dan M Roden, Joshua C Denny, Wei-Qi Wei
Language: English
Subfield: Toxicology
Domain: Life Sciences
Open Access status: green
Funders: American Heart Association, National Institutes of Health, Agency for Healthcare Research and Quality, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Institute of General Medical Sciences, U.S. National Library of Medicine, National Center for Advancing Translational Sciences
Awards: G129402337, Vanderbilt Genome-Electronic Records (VGER) Project, Improving Prediction of Drug Action, Medical Scientist Training Program, Pleiotropy of PCSK9 Inhibition, G4333865332, Initiative for Maximizing Student Development, Beyond PheWAS: Recognition of Phenotype Patterns for Discovery and Translation, Exploring Statin Pleiotropic Effects within a Very Large EHR Cohort, PheMAP: Measured, Automated Profile to Facilitate High Throughput Phenotyping, The Vanderbilt Institute for Clinical and Translational Research (VICTR), Pharmacogenetics to improve drug therapy, Exploring Statin Pleiotropic Effects within a Very Large EHR Cohort, Vanderbilt Biomedical Informatics Training Program, Medical Scientist Training Program
OpenAlex ID: W3135761017