title: An administrative data validation study of the accuracy of algorithms for identifying rheumatoid arthritis: the influence of the reference standard on algorithm performance.
abstract: BACKGROUND
  We have previously validated administrative data algorithms to identify patients with rheumatoid arthritis (RA) using rheumatology clinic records as the reference standard. Here we reassessed the accuracy of the algorithms using primary care records as the reference standard.
  
  
  METHODS
  We performed a retrospective chart abstraction study using a random sample of 7500 adult patients under the care of 83 family physicians contributing to the Electronic Medical Record Administrative data Linked Database (EMRALD) in Ontario, Canada. Using physician-reported diagnoses as the reference standard, we computed and compared the sensitivity, specificity, and predictive values for over 100 administrative data algorithms for RA case ascertainment.
  
  
  RESULTS
  We identified 69 patients with RA for a lifetime RA prevalence of 0.9%. All algorithms had excellent specificity (>97%). However, sensitivity varied (75-90%) among physician billing algorithms. Despite the low prevalence of RA, most algorithms had adequate positive predictive value (PPV; 51-83%). The algorithm of "[1 hospitalization RA diagnosis code] or [3 physician RA diagnosis codes with ≥1 by a specialist over 2 years]" had a sensitivity of 78% (95% CI 69-88), specificity of 100% (95% CI 100-100), PPV of 78% (95% CI 69-88) and NPV of 100% (95% CI 100-100).
  
  
  CONCLUSIONS
  Administrative data algorithms for detecting RA patients achieved a high degree of accuracy amongst the general population. However, results varied slightly from our previous report, which can be attributed to differences in the reference standards with respect to disease prevalence, spectrum of disease, and type of comparator group.
authors: University of Toronto, Toronto, 200 Elizabeth St 13EN-224, Toronto, ON M5G 2C4, Canada. Jessica.widdifield@utoronto.ca.
authors: Jessica
authors: J
authors: Widdifield
authors: Claire
authors: C
authors: Bombardier
authors: Sasha
authors: S
authors: Bernatsky
authors: J Michael
authors: JM
authors: Paterson
authors: Diane
authors: D
authors: Green
authors: Jacqueline
authors: J
authors: Young
authors: Noah
authors: N
authors: Ivers
authors: Debra A
authors: DA
authors: Butt
authors: R Liisa
authors: RL
authors: Jaakkimainen
authors: J Carter
authors: JC
authors: Thorne
authors: Karen
authors: K
authors: Tu
mesh_terms: D000305:Adrenal Cortex Hormones / Q000627:therapeutic use; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000894:Anti-Inflammatory Agents, Non-Steroidal / Q000627:therapeutic use; D018501:Antirheumatic Agents / Q000627:therapeutic use; D001172:Arthritis, Rheumatoid / Q000175:diagnosis* / Q000188:drug therapy / Q000453:epidemiology; D016208:Databases, Factual; D003953:Diagnosis-Related Groups; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008297:Male; D008403:Mass Screening; D008498:Medical Record Linkage; D008875:Middle Aged; D009864:Ontario / Q000453:epidemiology; D011237:Predictive Value of Tests; D015995:Prevalence; D011320:Primary Health Care / Q000706:statistics & numerical data*; D012015:Reference Standards; D012189:Retrospective Studies; D012494:Sampling Studies; D012680:Sensitivity and Specificity; D018855:Single-Payer System / Q000706:statistics & numerical data; D019472:Universal Health Insurance
chemical_list: D000305:Adrenal Cortex Hormones; D000894:Anti-Inflammatory Agents, Non-Steroidal; D018501:Antirheumatic Agents