title: A systematic review of validated methods for identifying hypersensitivity reactions other than anaphylaxis (fever, rash, and lymphadenopathy), using administrative and claims data.
abstract: OBJECTIVE
  The Food and Drug Administration's Mini-Sentinel pilot program aims to conduct active surveillance to refine safety signals that emerge for marketed medical products. A key facet of this surveillance is to develop and understand the validity of algorithms for identifying health outcomes of interest from administrative and claims data. This article summarizes the process and findings of the algorithm review of hypersensitivity reactions.
  
  
  METHODS
  PubMed and Iowa Drug Information Service searches were conducted to identify citations applicable to the hypersensitivity reactions of health outcomes of interest. Level 1 abstract reviews and Level 2 full-text reviews were conducted to find articles using administrative and claims data to identify hypersensitivity reactions and including validation estimates of the coding algorithms.
  
  
  RESULTS
  We identified five studies that provided validated hypersensitivity-reaction algorithms. Algorithm positive predictive values (PPVs) for various definitions of hypersensitivity reactions ranged from 3% to 95%. PPVs were high (i.e. 90%-95%) when both exposures and diagnoses were very specific. PPV generally decreased when the definition of hypersensitivity was expanded, except in one study that used data mining methodology for algorithm development.
  
  
  CONCLUSIONS
  The ability of coding algorithms to identify hypersensitivity reactions varied, with decreasing performance occurring with expanded outcome definitions. This examination of hypersensitivity-reaction coding algorithms provides an example of surveillance bias resulting from outcome definitions that include mild cases. Data mining may provide tools for algorithm development for hypersensitivity and other health outcomes. Research needs to be conducted on designing validation studies to test hypersensitivity-reaction algorithms and estimating their predictive power, sensitivity, and specificity.
authors.affiliation: United BioSource Corporation, Lexington, MA 02420, USA. gary.schneider@unitedbiosource.com
authors.forename: Gary
authors.initials: G
authors.lastname: Schneider
authors.forename: Sumesh
authors.initials: S
authors.lastname: Kachroo
authors.forename: Natalie
authors.initials: N
authors.lastname: Jones
authors.forename: Sheila
authors.initials: S
authors.lastname: Crean
authors.forename: Philip
authors.initials: P
authors.lastname: Rotella
authors.forename: Ruzan
authors.initials: R
authors.lastname: Avetisyan
authors.forename: Matthew W
authors.initials: MW
authors.lastname: Reynolds
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods; D016208:Databases, Factual / Q000706:statistics & numerical data*; D006801:Humans; D006967:Hypersensitivity / Q000175:diagnosis / Q000453:epidemiology* / Q000276:immunology; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D017063:Outcome Assessment, Health Care / Q000379:methods; D010865:Pilot Projects; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D014481:United States / Q000453:epidemiology; D014486:United States Food and Drug Administration; D054928:Validation Studies as Topic