title: An automated causality assessment algorithm to detect drug-induced liver injury in electronic medical record data.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Kaiser Permanente Southern California, Pharmacy Analytical Services, CA, USA.
authors.forename: T Craig
authors.initials: TC
authors.lastname: Cheetham
authors.forename: Janet
authors.initials: J
authors.lastname: Lee
authors.forename: Christine M
authors.initials: CM
authors.lastname: Hunt
authors.forename: Fang
authors.initials: F
authors.lastname: Niu
authors.forename: Steph
authors.initials: S
authors.lastname: Reisinger
authors.forename: Rich
authors.initials: R
authors.lastname: Murray
authors.forename: Greg
authors.initials: G
authors.lastname: Powell
authors.forename: Julie
authors.initials: J
authors.lastname: Papay
mesh_terms: D000465:Algorithms; D056486:Chemical and Drug Induced Liver Injury / Q000175:diagnosis* / Q000453:epidemiology; D016208:Databases, Factual / Q000592:standards*; D057286:Electronic Health Records / Q000592:standards*; D006801:Humans; D010865:Pilot Projects; D055553:Prescription Drugs / Q000009:adverse effects*
chemical_list: D055553:Prescription Drugs
keywords: algorithm; automated; causality assessment; drug safety; drug-induced; liver injury; pharmacoepidemiology; risk assessment