title: Validation of claims-based algorithms for gout flares.
abstract: Gout is a common inflammatory arthritis characterized by repeated acute flares. The ability to accurately identify gout flares is critical for comparative effectiveness studies of gout treatments. We developed and examined the accuracy of a claims-based algorithm to identify gout flares.
  
  
  
  Patients receiving care at an academic medical center between 2006 and 2010 with a diagnosis of gout or hyperuricemia were selected using an electronic medical record-Medicare claims linked dataset. Gout flares were identified by several claims-based algorithms using a diagnosis of gout combined with gout-related medication claims and/or procedure codes for arthrocentesis or joint injection. We calculated positive predictive value of these algorithms based on physician documentation of gout flare in medical record as the gold standard. Negative predictive value of the gout flare algorithm was calculated in a randomly selected subgroup of 200 patients with gout.
  
  
  
  Among 3952 subjects with gout or hyperuricemia, 503 flares were identified using the medication-based algorithm, and 290 were identified using the procedure-based algorithm. The positive predictive value for gout flares ranged from 50-54% for the medication-based algorithms and 59-68% for the procedure-based algorithms. The negative predictive value of the algorithm combining both medication and procedure claims was high (85.2%).
  
  
  
  Use of gout diagnosis codes in combination with medication dispensing or procedure codes did not appear to accurately capture gout flares in patients with gout in a claims database. However, the claims-based flare algorithm could be useful in identifying a cohort of gout patients with no flares. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, MA, USA.
authors.forename: Lindsey A
authors.initials: LA
authors.lastname: MacFarlane
authors.affiliation: Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, MA, USA.
authors.forename: Chih-Chin
authors.initials: CC
authors.lastname: Liu
authors.affiliation: Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, MA, USA.|Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA, USA.
authors.forename: Daniel H
authors.initials: DH
authors.lastname: Solomon
authors.affiliation: Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, MA, USA.|Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Boston, MA, USA.
authors.forename: Seoyoung C
authors.initials: SC
authors.lastname: Kim
mesh_terms: D000046:Academic Medical Centers; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016208:Databases, Factual / Q000706:statistics & numerical data*; D057286:Electronic Health Records / Q000706:statistics & numerical data; D005260:Female; D006073:Gout / Q000175:diagnosis / Q000453:epidemiology* / Q000503:physiopathology; D006801:Humans; D033461:Hyperuricemia / Q000175:diagnosis / Q000453:epidemiology*; D008297:Male; D006278:Medicare; D008875:Middle Aged; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D014481:United States
keywords: claims data; comparative effectiveness; gout; gout flare; pharmacoepidemiology