title: A collaborative approach to developing an electronic health record phenotyping algorithm for drug-induced liver injury.
abstract: OBJECTIVE
  To describe a collaborative approach for developing an electronic health record (EHR) phenotyping algorithm for drug-induced liver injury (DILI).
  
  
  METHODS
  We analyzed types and causes of differences in DILI case definitions provided by two institutions-Columbia University and Mayo Clinic; harmonized two EHR phenotyping algorithms; and assessed the performance, measured by sensitivity, specificity, positive predictive value, and negative predictive value, of the resulting algorithm at three institutions except that sensitivity was measured only at Columbia University.
  
  
  RESULTS
  Although these sites had the same case definition, their phenotyping methods differed by selection of liver injury diagnoses, inclusion of drugs cited in DILI cases, laboratory tests assessed, laboratory thresholds for liver injury, exclusion criteria, and approaches to validating phenotypes. We reached consensus on a DILI phenotyping algorithm and implemented it at three institutions. The algorithm was adapted locally to account for differences in populations and data access. Implementations collectively yielded 117 algorithm-selected cases and 23 confirmed true positive cases.
  
  
  CONCLUSIONS
  Phenotyping for rare conditions benefits significantly from pooling data across institutions. Despite the heterogeneity of EHRs and varied algorithm implementations, we demonstrated the portability of this algorithm across three institutions. The performance of this algorithm for identifying DILI was comparable with other computerized approaches to identify adverse drug events.
  
  
  CONCLUSIONS
  Phenotyping algorithms developed for rare and complex conditions are likely to require adaptive implementation at multiple institutions. Better approaches are also needed to share algorithms. Early agreement on goals, data sources, and validation methods may improve the portability of the algorithms.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, New York, USA.
authors.forename: Casey Lynnette
authors.initials: CL
authors.lastname: Overby
authors.forename: Jyotishman
authors.initials: J
authors.lastname: Pathak
authors.forename: Omri
authors.initials: O
authors.lastname: Gottesman
authors.forename: Krystl
authors.initials: K
authors.lastname: Haerian
authors.forename: Adler
authors.initials: A
authors.lastname: Perotte
authors.forename: Sean
authors.initials: S
authors.lastname: Murphy
authors.forename: Kevin
authors.initials: K
authors.lastname: Bruce
authors.forename: Stephanie
authors.initials: S
authors.lastname: Johnson
authors.forename: Jayant
authors.initials: J
authors.lastname: Talwalkar
authors.forename: Yufeng
authors.initials: Y
authors.lastname: Shen
authors.forename: Steve
authors.initials: S
authors.lastname: Ellis
authors.forename: Iftikhar
authors.initials: I
authors.lastname: Kullo
authors.forename: Christopher
authors.initials: C
authors.lastname: Chute
authors.forename: Carol
authors.initials: C
authors.lastname: Friedman
authors.forename: Erwin
authors.initials: E
authors.lastname: Bottinger
authors.forename: George
authors.initials: G
authors.lastname: Hripcsak
authors.forename: Chunhua
authors.initials: C
authors.lastname: Weng
mesh_terms: D000465:Algorithms; D056486:Chemical and Drug Induced Liver Injury / Q000235:genetics*; D057286:Electronic Health Records; D006801:Humans; D010641:Phenotype
keywords: Drug-induced liver injury; Electronic health records; Pharmacovigilance; Phenotyping; Rare diseases