Algorithm to Identify Incident Epithelial Ovarian Cancer Cases Using Claims Data

Abstract

PURPOSE To create an algorithm to identify incident epithelial ovarian cancer cases in claims-based data sets and evaluate performance of the algorithm using SEER-Medicare claims data. METHODS We created a five-step algorithm on the basis of clinical expertise to identify incident epithelial ovarian cancer cases using claims data for (1) ovarian cancer diagnosis, (2) receipt of platinum-based chemotherapy, (3) no claim for platinum-based chemotherapy but claim for tumor debulking surgery, (4) removed cases with nonplatinum chemotherapy, and (5) removed patients with prior claims with personal history of ovarian cancer code to exclude prevalent cases. We evaluated algorithm performance using SEER-Medicare claims data by creating four cohorts: incident epithelial ovarian cancer, a 5% random sample of cancer-free Medicare beneficiaries, a 5% random sample of incident nonovarian cancer, and prevalent ovarian cancer cases. RESULTS Using SEER tumor registry data as the gold standard, our algorithm correctly classified 89.9% of incident epithelial ovarian cancer cases (cohort n = 572) and almost 100% of cancer-free controls (n = 97,127), nonovarian cancer (n = 714), and prevalent ovarian cancer cases (n = 3,712). The overall algorithm sensitivity was 89.9%, the positive predictive value was 93.8%, and the specificity and negative predictive value were > 99.9%. Patients were more likely to be correctly classified as incident ovarian cancer if they had stage III or IV disease compared with early stage I or II disease (93.5% v 83.7%, P < .01), and grade 1-4 compared with unknown grade tumors (93.8% v 81.4%, P < .01). CONCLUSION Our algorithm correctly identified most incident epithelial ovarian cancer cases, especially those with advanced disease. This algorithm will facilitate research in other claims-based data sets where cancer registry data are unavailable.