title: Developing indicators of inpatient adverse drug events through nonlinear analysis using administrative data.
abstract: BACKGROUND
  Because of uniform availability, hospital administrative data are appealing for surveillance of adverse drug events (ADEs). Expert-generated surveillance rules that rely on the presence of International Classification of Diseases, 9th Revision Clinical Modification (ICD-9-CM) codes have limited accuracy. Rules based on nonlinear associations among all types of available administrative data may be more accurate.
  
  
  OBJECTIVE
  By applying hierarchically optimal classification tree analysis (HOCTA) to administrative data, derive and validate surveillance rules for bleeding/anticoagulation problems and delirium/psychosis.
  
  
  METHODS
  Retrospective cohort design.
  
  
  METHODS
  A random sample of 3987 admissions drawn from all 41 Utah acute-care hospitals in 2001 and 2003.
  
  
  METHODS
  Professional nurse reviewers identified ADEs using implicit chart review. Pharmacists assigned Medical Dictionary for Regulatory Activities codes to ADE descriptions for identification of clinical groups of events. Hospitals provided patient demographic, admission, and ICD9-CM data.
  
  
  RESULTS
  Incidence proportions were 0.8% for drug-induced bleeding/anticoagulation problems and 1.0% for drug-induced delirium/psychosis. The model for bleeding had very good discrimination and sensitivity at 0.87 and 86% and fair positive predictive value (PPV) at 12%. The model for delirium had excellent sensitivity at 94%, good discrimination at 0.83, but low PPV at 3%. Poisoning and adverse event codes designed for the targeted ADEs had low sensitivities and, when forced in, degraded model accuracy.
  
  
  CONCLUSIONS
  Hierarchically optimal classification tree analysis is a promising method for rapidly developing clinically meaningful surveillance rules for administrative data. The resultant model for drug-induced bleeding and anticoagulation problems may be useful for retrospective ADE screening and rate estimation.
authors.affiliation: VA Salt Lake City Geriatrics, Research, Education, and Clinical Center (GRECC, Salt Lake City, Utah, USA. Jonathan.Nebeker@hsc.utah.edu
authors.forename: Jonathan R
authors.initials: JR
authors.lastname: Nebeker
authors.forename: Paul R
authors.initials: PR
authors.lastname: Yarnold
authors.forename: Robert C
authors.initials: RC
authors.lastname: Soltysik
authors.forename: Brian C
authors.initials: BC
authors.lastname: Sauer
authors.forename: Shannon A
authors.initials: SA
authors.lastname: Sims
authors.forename: Matthew H
authors.initials: MH
authors.lastname: Samore
authors.forename: Randall W
authors.initials: RW
authors.lastname: Rupper
authors.forename: Kathleen M
authors.initials: KM
authors.lastname: Swanson
authors.forename: Lucy A
authors.initials: LA
authors.lastname: Savitz
authors.forename: Judith
authors.initials: J
authors.lastname: Shinogle
authors.forename: Wu
authors.initials: W
authors.lastname: Xu
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D000368:Aged; D001778:Blood Coagulation Disorders / Q000139:chemically induced / Q000453:epidemiology / Q000517:prevention & control; D003625:Data Collection / Q000379:methods*; D003693:Delirium / Q000139:chemically induced / Q000453:epidemiology / Q000517:prevention & control; D005260:Female; D006470:Hemorrhage / Q000139:chemically induced / Q000453:epidemiology / Q000517:prevention & control; D006753:Hospital Records / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D038801:International Classification of Diseases; D008297:Male; D008875:Middle Aged; D017711:Nonlinear Dynamics; D011237:Predictive Value of Tests; D011605:Psychoses, Substance-Induced / Q000453:epidemiology / Q000517:prevention & control; D012372:ROC Curve; D012189:Retrospective Studies; D012308:Risk Management / Q000379:methods / Q000706:statistics & numerical data*; D014589:Utah / Q000453:epidemiology