Idiosyncratic DILI: Analysis of 46,266 Cases Assessed for Causality by RUCAM and Published From 2014 to Early 2019 - Teschke, Rolf (Frontiers in Pharmacology, 2019)
RUCAM (Roussel Uclaf Causality Assessment Method) is a well established diagnostic algorithms to assess causality in patients with suspected drug induced liver injury (DILI). Inaugurated in 1993, RUCAM is now the worldwide most commonly used causality assessment method (CAM) for DILI. This view is substantiated by the current analysis with additional details of 45,730 DILI cases, all tested for causality using RUCAM. These cases have been derived from 30 reports published from 2014 up to early 2019. The first ranking authors of these reported cases came from 10 countries, with China on top, followed by the US and Germany. Importantly, all RUCAM based DILI reports have been published in journals highly appreciated among DILI and RUCAM experts, including Frontiers in Pharmacology, Journal of Hepatology, Annals of Hepatology, Hepatology, Journal of Gastroenterology & Hepatology, Gastroenterology, Gut & Liver, Current Hepatology Reports, SpringerPlus, Journal of Pharmacoepidemiology and Drug Safety, and British Journal of Clinical Pharmacology. Many other RUCAM based DILI reports have Earlier been published from 1993 up to 2014 in support of the high appreciation of the user friendly RUCAM. Although most of the studies have carefully been performed, the current case analysis revealed for a few studies shortcomings, evidently not at the level of RUCAM itself but rather associated with the work of the users. To ensure in future cases a better performance of the potential users, a list of essential qualifying elements has been established. As an example, all suspected DILI must receive an evaluation by the updated RUCAM to facilitate result comparisons, a prospective study protocol to ensure complete data sets are mandatory, the inclusion of patients with herb induced cases liver injury in a DILI cohort is not allowed to prevent a mix of data leading to confounding variables, and studies should include only cases with RUCAM based causality gradings of highly probable or probable, eliminating a priori those with a ossible grading that may otherwise confound the overall results of DILI. In conclusion, RUCAM benefits from its high appreciation and performs well provided the users adhere to established rules to prevent confounding variability.