Paul H Hayashi1, M Isabel Lucena2,3, Robert J Fontana4, Einar S Bjornsson5,6, Guruprasad P Aithal7, Huiman Barnhart8, Andres Gonzalez-Jimenez2,3, Qinghong Yang8, Jiezhun Gu8, Raul J Andrade2,3, Jay H Hoofnagle9. 1. Food and Drug Administration, Silver Spring, Maryland, USA. 2. Servicio de Aparato Digestivo y de Farmacología Clínica, Hospital Universitario Virgen de la Victoria, Instituto de Investigación Biomédica de Málaga-IBIMA, Facultad de Medicina, Universidad de Málaga, Málaga, Spain. 3. Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, UICEC-IBIMA, Plataforma Investigación Clínica, Madrid, Spain. 4. University of Michigan, Ann Arbor, Michigan, USA. 5. Landspitali University Hospital, Reykjavik, Iceland. 6. Faculty of Medicine, University of Iceland, Reykjavik, Iceland. 7. NIHR Nottingham BRC, Nottingham University Hospitals NHS Trust and the University of Nottingham, Nottingham, UK. 8. Duke University, Durham, North Carolina, USA. 9. National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland, USA.
Abstract
BACKGROUND AND AIMS: Roussel Uclaf Causality Assessment Method (RUCAM) for DILI has been hindered by subjectivity and poor reliability. We sought to improve the RUCAM using data from the Drug-Induced Liver Injury Network (DILIN) and the Spanish DILI Registry, published literature, and iterative computer modeling. APPROACH AND RESULTS: RUCAM criteria were updated, clarified, and computerized. We removed criteria 3 (risk factors) for lack of added value and criteria 4 because we felt it more useful to assess each drug separately. Criteria 6 (drug-specific risk) was anchored to LiverTox likelihood scores. Iterative testing in subsets of 50-100 single-agent, nonherbal cases from both registries was done to optimize performance. We used classification tree analysis to establish diagnostic cutoffs for this revised electronic causality assessment method (RECAM) and compared RECAM with RUCAM for correlation with expert opinion diagnostic categories in 194 DILI cases (98 DILIN, 96 Spanish DILI). Area under receiver operator curves for identifying at least probable DILI were the same at 0.89 for RECAM and RUCAM. However, RECAM diagnostic categories have better observed overall agreement with expert opinion (0.62 vs. 0.56 weighted kappa, p = 0.14), and had better sensitivity to detect extreme diagnostic categories (73 vs. 54 for highly likely or high probable, p = 0.02; 65 vs. 48 for unlikely/excluded, p = 0.08) than RUCAM diagnostic categories. CONCLUSIONS: RECAM is an evidence-based update that is at least as capable as RUCAM in diagnosing DILI compared with expert opinion but is better than RUCAM at the diagnostic extremes. RECAM's increased objectivity and clarity will improve precision, reliability, and standardization of DILI diagnosis, but further refinement and validation in other cohorts are needed.
BACKGROUND AND AIMS: Roussel Uclaf Causality Assessment Method (RUCAM) for DILI has been hindered by subjectivity and poor reliability. We sought to improve the RUCAM using data from the Drug-Induced Liver Injury Network (DILIN) and the Spanish DILI Registry, published literature, and iterative computer modeling. APPROACH AND RESULTS: RUCAM criteria were updated, clarified, and computerized. We removed criteria 3 (risk factors) for lack of added value and criteria 4 because we felt it more useful to assess each drug separately. Criteria 6 (drug-specific risk) was anchored to LiverTox likelihood scores. Iterative testing in subsets of 50-100 single-agent, nonherbal cases from both registries was done to optimize performance. We used classification tree analysis to establish diagnostic cutoffs for this revised electronic causality assessment method (RECAM) and compared RECAM with RUCAM for correlation with expert opinion diagnostic categories in 194 DILI cases (98 DILIN, 96 Spanish DILI). Area under receiver operator curves for identifying at least probable DILI were the same at 0.89 for RECAM and RUCAM. However, RECAM diagnostic categories have better observed overall agreement with expert opinion (0.62 vs. 0.56 weighted kappa, p = 0.14), and had better sensitivity to detect extreme diagnostic categories (73 vs. 54 for highly likely or high probable, p = 0.02; 65 vs. 48 for unlikely/excluded, p = 0.08) than RUCAM diagnostic categories. CONCLUSIONS: RECAM is an evidence-based update that is at least as capable as RUCAM in diagnosing DILI compared with expert opinion but is better than RUCAM at the diagnostic extremes. RECAM's increased objectivity and clarity will improve precision, reliability, and standardization of DILI diagnosis, but further refinement and validation in other cohorts are needed.
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