Affiliation:
1. Department of Computer Engineering Gachon University Seongnam Republic of Korea
2. Healthcare Data Science Center Konyang University Hospital Daejeon Republic of Korea
3. Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics Seoul National University College of Medicine Seoul Republic of Korea
4. Department of Internal Medicine, Subdivision of Allergy Chungbuk National University Hospital and Chungbuk National College of Medicine Cheongju Republic of Korea
5. Department of Internal Medicine Seoul National University Bundang Hospital Seongnam Republic of Korea
6. Department of Preventive Medicine Ulsan University Hospital Ulsan Republic of Korea
Abstract
AbstractPurposeThis study aimed to advance the MetaLAB algorithm and verify its performance with multicenter data to effectively detect major adverse drug reactions (ADRs), including drug‐induced liver injury.MethodsBased on MetaLAB, we created an optimal scenario for detecting ADRs by considering demographic and clinical records. MetaLAB‐HOI was developed to identify ADR signals using common model‐based multicenter electronic health record (EHR) data from the clinical health outcomes of interest (HOI) template and design for drug‐exposed and nonexposed groups. In this study, we calculated the odds ratio of 101 drugs for HOI in Konyang University Hospital, Seoul National University Hospital, Chungbuk National University Hospital, and Seoul National University Bundang Hospital.ResultsThe overlapping drugs in four medical centers are amlodipine, aspirin, bisoprolol, carvedilol, clopidogrel, clozapine, digoxin, diltiazem, methotrexate, and rosuvastatin. We developed MetaLAB‐HOI, an algorithm that can detect ADRs more efficiently using EHR. We compared the detection results of four medical centers, with drug‐induced liver injuries as representative ADRs.ConclusionsMetaLAB‐HOI's strength lies in fully utilizing the patient's clinical information, such as prescription, procedure, and laboratory results, to detect ADR signals. Considering changes in the patient's condition over time, we created an algorithm based on a scenario that accounted for each drug exposure and onset period supervised by specialists for HOI. We determined that when a template capable of detecting ADR based on clinical evidence is developed and manualized, it can be applied in medical centers for new drugs with insufficient data.
Subject
Pharmacology (medical),Epidemiology