External validation of a claims-based algorithm for classifying kidney-cancer surgeries

BMC Health Serv Res. 2009 Jun 6:9:92. doi: 10.1186/1472-6963-9-92.

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.

Conclusion: Medicare claims represent an accurate data source for ascertainment of population-based patterns of surgical care among patients with early-stage kidney cancer.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.
  • Validation Study

MeSH terms

  • Algorithms*
  • Female
  • Humans
  • Insurance Claim Review*
  • Kidney Neoplasms / epidemiology
  • Kidney Neoplasms / surgery*
  • Male
  • Medicare
  • Nephrectomy / classification*
  • Predictive Value of Tests
  • SEER Program
  • Sensitivity and Specificity
  • United States / epidemiology