ArticleJCO clinical cancer informatics2022
Algorithm to Identify Incident Epithelial Ovarian Cancer Cases Using Claims Data.
Article in JCO clinical cancer informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- BRCA testing utilization among commercially insured women with breast or ovarian cancer.Breast cancer research and treatment · 2025Article
- Validation of an Administrative Claims-based Line of Therapy Algorithm for Women with Ovarian Cancer Using Medical Chart Review.Advances in therapy · 2025Article
- Cost of ovarian cancer by the phase of care in the United States.American journal of obstetrics and gynecology · 2025Article
Corrections and comments
- Erratum issuedErratum.2022
Authors and funding
7 authors.
Funding
Abstract
purposeTo 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.
methodsWe 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.
resultsUsing 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%
conclusionOur 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.
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