title: External validation of a claims-based algorithm for classifying kidney-cancer surgeries.
abstract: BACKGROUND
  Unlike other malignancies, there is no literature supporting the accuracy of medical claims data for identifying surgical treatments among patients with kidney cancer. We sought to validate externally a previously published Medicare-claims-based algorithm for classifying surgical treatments among patients with early-stage kidney cancer. To achieve this aim, we compared procedure assignments based on Medicare claims with the type of surgery specified in SEER registry data and clinical operative reports.
  
  
  METHODS
  Using linked SEER-Medicare data, we calculated the agreement between Medicare claims and SEER data for identification of cancer-directed surgery among 6,515 patients diagnosed with early-stage kidney cancer. Next, for a subset of 120 cases, we determined the agreement between the claims algorithm and the medical record. Finally, using the medical record as the reference-standard, we calculated the sensitivity, specificity, and positive and negative predictive values of the claims algorithm.
  
  
  RESULTS
  Among 6,515 cases, Medicare claims and SEER data identified 5,483 (84.1%) and 5,774 (88.6%) patients, respectively, who underwent cancer-directed surgery (observed agreement = 93%, kappa = 0.69, 95% CI 0.66 - 0.71). The two data sources demonstrated 97% agreement for classification of partial versus radical nephrectomy (kappa = 0.83, 95% CI 0.81 - 0.86). We observed 97% agreement between the claims algorithm and clinical operative reports; the positive predictive value of the claims algorithm exceeded 90% for identification of both partial nephrectomy and laparoscopic surgery.
  
  
  CONCLUSIONS
  Medicare claims represent an accurate data source for ascertainment of population-based patterns of surgical care among patients with early-stage kidney cancer.
authors.affiliation: Department of Urology, University of Michigan, Ann Arbor, MI 48109, USA. dcmiller@umich.edu
authors.forename: David C
authors.initials: DC
authors.lastname: Miller
authors.forename: Christopher S
authors.initials: CS
authors.lastname: Saigal
authors.forename: Joan L
authors.initials: JL
authors.lastname: Warren
authors.forename: Meryl
authors.initials: M
authors.lastname: Leventhal
authors.forename: Dennis
authors.initials: D
authors.lastname: Deapen
authors.forename: Mousumi
authors.initials: M
authors.lastname: Banerjee
authors.forename: Julie
authors.initials: J
authors.lastname: Lai
authors.forename: Jan
authors.initials: J
authors.lastname: Hanley
authors.forename: Mark S
authors.initials: MS
authors.lastname: Litwin
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D007345:Insurance Claim Review; D007680:Kidney Neoplasms / Q000453:epidemiology / Q000601:surgery*; D008297:Male; D006278:Medicare; D009392:Nephrectomy / Q000145:classification*; D011237:Predictive Value of Tests; D018426:SEER Program; D012680:Sensitivity and Specificity; D014481:United States / Q000453:epidemiology