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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]

Purpose: Quetiapine is a drug used to treat schizophrenia, bipolar disorder, and major depressive disorder. However, it can cause mild or severe hepatic adverse events and rarely fatal liver damage. This study was aimed at investigating hepatic toxicity caused by quetiapine use by analyzing the information captured from hospital electronic health records by using the Observational Medical Outcomes Partnership common data model (CDM). Methods: This was a retrospective observational study involving a nested case–control method. A CDM based on an electronic health record database from five hospitals between January 2009 and May 2020 was used. We analyzed the status of quetiapine use, adverse events, and hepatic impairment. Results: The numbers of patients with non‐serious and severe hepatic adverse reactions were 2566 (5.05%) and 835 (1.64%) out of 50 766 patients, respectively. After adjusting for covariates, the odds ratio of hepatic adverse events was 2.35 (95% CI: 2.03–2.72), and the odds ratio of severe hepatic adverse events was 1.76 (95% CI: 1.16–2.66). Conclusion: Our findings suggest that quetiapine should be cautiously used, and hepatic function should be monitored in patients using quetiapine because it can cause mild or severe hepatic adverse events, complications, and in rare cases, fatal liver damage. [ABSTRACT FROM AUTHOR]

Background: HLA‐B*58:01 is a well‐known risk factor for allopurinol‐induced severe cutaneous adverse reactions (SCARs). However, only a minority of HLA‐B*58:01 carriers suffer SCARs after taking allopurinol. The aim of this study was to investigate subsidiary genetic markers that could identify those at further increased risk of developing allopurinol‐induced drug reaction with eosinophilia and systemic symptoms (DRESS) in subjects with HLA‐B*58:01. Methods: Subjects with B*58:01 were enrolled (21 allopurinol‐induced DRESS and 52 allopurinol‐tolerant control). HLA‐A, ‐B, ‐C and ‐DRB1 alleles were compared. Comparison of risk between HLAs and allopurinol‐induced SCAR in separate populations was performed to support the results. Kruskal‐Wallis test, Pearson's chi‐square test, Fisher's exact test and binary logistic regression were used to analyze the risk of SCAR development. Results: Frequencies of A*24:02 (71.4 vs. 17.3%, p < 0.001, odds ratio [OR] = 12.0; 95% confidence interval [CI], 3.6–39.2) were significantly higher in B*58:01 (+) DRESS than B*58:01 (+) tolerant controls. In addition, DRB1*13:02 further increased the risk of DRESS. The phenotype frequency of A*24:02/DRB1*13:02 was significantly higher in the B*58:01 (+) DRESS group than in the B*58:01 (+) tolerant controls (52.4% vs. 5.8%, p < 0.001, OR, 66.0; 95% CI, 6.1–716.2). In 2782 allopurinol user cohort, the overall prevalence of DRESS was 0.22%, which increased to 1.62% and 2.86% in the presence of B*58:01 and B*58:01/A*24:02, respectively. Conclusion: The additional secondary screening with A*24:02 and DRB1*13:02 alleles may identify those at further increased risk of allopurinol‐induced DRESS in B*58:01 carriers. [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]

Purpose: Drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome is a rare but serious condition that systematically damages various internal organs through T‐cell–mediated immunological drug reactions. We aimed to investigate whether clinical manifestations of DRESS syndrome differ according to culprit drugs. Methods: We retrospectively analyzed data from 123 patients with probable/definite DRESS syndrome based on the RegiSCAR criteria (January 2011 to July 2016). The data were obtained from the Korean Severe Cutaneous Adverse Reaction Registry. Causality was assessed using the World Health Organization‐Uppsala Monitoring Centre criteria. The culprit drugs were categorized as allopurinol, carbamazepine, anti‐tuberculosis drug, vancomycin, cephalosporins, dapsone, and nonsteroidal anti‐inflammatory drugs. Results: Differences were observed among culprit drugs regarding the frequencies of hepatitis (P < 0.01), renal dysfunction (P < 0.0001), lymphadenopathy (P < 0.01), and atypical lymphocyte (P < 0.01). Latency period differed among culprit drugs (P < 0.0001), being shorter in vancomycin and cephalosporin. In terms of clinical severity, admission duration (P < 0.01) and treatment duration (P < 0.05) differed among culprit drugs, being longer in vancomycin and anti‐tuberculosis drugs, respectively. Conclusions: Based on the findings, clinical manifestations, including latency period and clinical severity, may differ according to culprit drugs in DRESS syndrome. [ABSTRACT FROM AUTHOR]