Search

Purpose: This 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. Methods: Based 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. Results: The 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. Conclusions: MetaLAB‐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. [ABSTRACT FROM AUTHOR]

Background: Several type B adverse drug reactions (ADRs), especially severe cutaneous adverse reactions (SCARs), are associated with particular human leukocyte antigen (HLA) genotypes. However, pre‐stored HLA information obtained from other clinical workups has not been used to prevent ADRs. We aimed to simulate the preemptive use of pre‐stored HLA information in electronic medical records to evaluate whether this information can prevent ADRs. Methods: We analyzed the incidence and the risk of ADRs for selected HLA alleles (HLA‐B*57:01, HLA‐B*58:01, HLA‐A*31:01, HLA‐B*15:02, HLA‐B*15:11, HLA‐B*13:01, HLA‐B*59:01, and HLA‐A*32:01) and seven drugs (abacavir, allopurinol, carbamazepine, oxcarbazepine, dapsone, methazolamide, and vancomycin) using pre‐stored HLA information of transplant patients based on the Pharmacogenomics Knowledge Base guidelines and experts' consensus. Results: Among 11,988 HLA‐tested transplant patients, 4092 (34.1%) had high‐risk HLA alleles, 4583 (38.2%) were prescribed risk drugs, and 580 (4.8%) experienced type B ADRs. Patients with HLA‐B*58:01 had a significantly higher incidence of type B ADR and SCARs associated with allopurinol use than that of patients without HLA‐B*58:01 (17.2% vs. 11.9%, odds ratio [OR] 1.53 [95% confidence interval {CI} 1.09–2.13], p = 0.001, 2.3% versus 0.3%, OR 7.13 [95% CI 2.19–22.69], p < 0.001). Higher risks of type B ADR and SCARs were observed in patients taking carbamazepine or oxcarbazepine if they had one of HLA‐A*31:01, HLA‐B*15:02, or HLA‐B*15:11 alleles. Vancomycin and dapsone use in HLA‐A*32:01 and HLA‐B*13:01 carriers, respectively, showed trends toward increased risk of type B ADRs. Conclusion: Utilization of pre‐stored HLA data can prevent type B ADRs including SCARs by screening high‐risk patients. [ABSTRACT FROM AUTHOR]