title: A systematic review of validated methods for identifying lymphoma using administrative data.
abstract: OBJECTIVE
  To systematically review published studies for algorithms that identified lymphoma as a health outcome of interest in administrative or claims data and examined the validity of the algorithm to identify lymphoma cases.
  
  
  METHODS
  A systematic literature search was executed using PubMed and the Iowa Drug Information Service database. Two investigators reviewed search results to identify studies using administrative or claims databases from the USA or Canada that both reported and validated an algorithm to identify lymphoma.
  
  
  RESULTS
  The search identified 713 unique citations with 402 eliminated by an initial screen of the article abstract. The remaining 311 resulted in one study that identified and validated an algorithm. Ten other studies reported algorithms but were not validated. The validated study reported four possible algorithms that had a specificity (> 99%), but the algorithm using two diagnostic codes recorded within 2 months had the best positive predictive value (PPV = 62.83%) and a sensitivity (79.81%). The most comprehensive algorithm required multiple diagnostic codes 2 months apart or diagnostic, and procedure codes on the same day had the greatest sensitivity (88.31%) and a PPV = 56.69%. The algorithm that required only a single diagnostic or procedure code had the worst PPV (34.72%).
  
  
  CONCLUSIONS
  The International Classification of Disease, Ninth Revision diagnostic, clinical procedure, and complication codes for lymphoma can identify incident hematologic malignancies and solid tumors with high specificity but with relatively low to moderate sensitivity and PPVs. When diagnostic and procedure codes were required on the same visit or multiple codes between visits, then PPV was increased. Relying on a single registry to confirm true positive cases is also not sufficient.
authors.affiliation: Division of Drug Information Services, The University of Iowa College of Pharmacy, Iowa City, IA 52242-4710, USA. Ronald-A-Herman@uiowa.edu.
authors.forename: Ronald A
authors.initials: RA
authors.lastname: Herman
authors.forename: Bradley
authors.initials: B
authors.lastname: Gilchrist
authors.forename: Brian K
authors.initials: BK
authors.lastname: Link
authors.forename: Ryan
authors.initials: R
authors.lastname: Carnahan
mesh_terms: D000465:Algorithms; D002170:Canada / Q000453:epidemiology; D016208:Databases, Factual / Q000706:statistics & numerical data*; D006801:Humans; D008223:Lymphoma / Q000175:diagnosis / Q000453:epidemiology*; D017063:Outcome Assessment, Health Care; D011237:Predictive Value of Tests; D012042:Registries; D012680:Sensitivity and Specificity; D014481:United States / Q000453:epidemiology; D054928:Validation Studies as Topic