4.6 Article

DDIWAS: High-throughput electronic health record-based screening of drug-drug interactions

Journal

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/jamia/ocab019

Keywords

drug interactions; drug-related side effects; adverse reactions; pharmacovigilance; electronic health records; data mining

Funding

  1. National Institutes of Health [T32GM007347, R01 LM010685, R01 HL133786, T15 LM007450, P50 GM115305, K12HS026395, R35 GM131770]
  2. American Heart Association [16SDG27490014]
  3. National Center for Advancing Translational Science [2UL1 TR000445-06]

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The study developed the DDIWAS method to detect potential drug-drug interactions using data from EHR allergy lists. It showed a positive predictive value of 85% to 86% for rediscovering known DDIs and discovering novel interactions. A software package is available for detecting and validating DDI signals across institutions.
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.

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