title: Accuracy of identifying neutropenia diagnoses in outpatient claims data.
abstract: OBJECTIVE
  Diagnosis codes have been valid tools to identify severe neutropenia leading to hospitalization in claims data, but no data exist on the accuracy of outpatient diagnosis of neutropenia. We examined the validity and accuracy of claims-based algorithms to identify neutropenia from outpatient visits.
  
  
  METHODS
  Adults with outpatient diagnosis of neutropenia in the HealthCore Integrated Research Database™ were identified by several algorithms using a combination of International Classification of Diseases, 9th Revision (ICD-9) codes and drug use data. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value of these algorithms using outpatient laboratory data within 3 months of the diagnosis as the gold standard to ascertain cases of mild (absolute neutrophil count (ANC) <1,500/μL) and severe (ANC <500/μL) neutropenia.
  
  
  RESULTS
  Among 95,742 eligible subjects, 867 patients were identified with any ICD-9 codes for neutropenia. This algorithm had high specificity (99%), but low sensitivity (9%) and PPV (18%) for mild neutropenia. Among the subjects identified with the ICD-9 288.0 (N = 203), sensitivity was 4% and PPV was 33%. Specificity and PPV of the algorithm that combined any ICD-9 codes for neutropenia with dispensing of pegfilgrastim or filgrastim were 100 and 56% for mild neutropenia, respectively. Sensitivity was 1%. All algorithms had slightly higher sensitivity, but lower PPV for severe neutropenia.
  
  
  CONCLUSIONS
  Use of ICD-9 codes for neutropenia in combination with drug use data did not appear to accurately identify outpatient diagnosis of neutropenia without using laboratory results, but it may be useful in determining the absence of neutropenia in claims data.
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA, USA. skim62@partners.org
authors.forename: Seo Young
authors.initials: SY
authors.lastname: Kim
authors.forename: Daniel H
authors.initials: DH
authors.lastname: Solomon
authors.forename: Jun
authors.initials: J
authors.lastname: Liu
authors.forename: Chun-Lan
authors.initials: CL
authors.lastname: Chang
authors.forename: Gregory W
authors.initials: GW
authors.lastname: Daniel
authors.forename: Sebastian
authors.initials: S
authors.lastname: Schneeweiss
mesh_terms: D000328:Adult; D000465:Algorithms; D016208:Databases, Factual / Q000706:statistics & numerical data*; D005260:Female; D000069585:Filgrastim; D016179:Granulocyte Colony-Stimulating Factor / Q000627:therapeutic use; D006801:Humans; D038801:International Classification of Diseases; D008297:Male; D008875:Middle Aged; D009503:Neutropenia / Q000175:diagnosis* / Q000188:drug therapy / Q000453:epidemiology; D010045:Outpatients; D011092:Polyethylene Glycols; D011237:Predictive Value of Tests; D011994:Recombinant Proteins; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index
chemical_list: D011994:Recombinant Proteins; D016179:Granulocyte Colony-Stimulating Factor; C455861:pegfilgrastim; D011092:Polyethylene Glycols; D000069585:Filgrastim