An automated causality assessment algorithm to detect drug-induced liver injury in electronic medical record data

Abstract

Purpose: The aim of this study was to develop an automated causality assessment algorithm to identify drug-induced liver injury.

Methods: The Roussel Uclaf Causality Assessment Method (RUCAM) is an algorithm for determining the causal association between a drug and liver injury. In collaboration with hepatology experts, definitions were developed for the RUCAM criteria to operationalize an electronic RUCAM (eRUCAM). The eRUCAM was tested in a population of patients taking 14 drugs with a characteristic phenotype for liver injury. Quality assurance for programming specifications involved comparisons between scores generated by the eRUCAM, for probable and highly probable cases, and expert manual RUCAM (n = 20). Concordance between eRUCAM and manual RUCAM subscores and total score was tested using the Wilcoxon signed rank test.

Results: Causality scores were the same for 6 of 20 patients (30%) by manual and eRUCAM algorithms. Analysis of subscores revealed ≥80% concordance between manual and eRUCAM for five of the seven criteria. In general, the total scores tended to be higher for the eRUCAM compared with the manual RUCAM. Programming issues were identified for criterion 5 'non-drug causes of liver injury' where significant differences existed between manual and eRUCAM scoring (p = 0.001). For criterion 5, identical scores occurred in 9 of 20 patients (45%), and manual review identified additional codes, timing criteria, and laboratory results for improving subsequent eRUCAM revisions.

Conclusion: The eRUCAM had generally good concordance with manual RUCAM scoring. These preliminary findings suggest that the eRUCAM algorithm is feasible and could have application in clinical practice and drug safety surveillance.

Keywords: algorithm; automated; causality assessment; drug safety; drug-induced; liver injury; pharmacoepidemiology; risk assessment.