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Algorithm to Identify Incident Epithelial Ovarian Cancer Cases Using Claims Data.

Sarah P Huepenbecker1, Hui Zhao2, Charlotte C Sun1

  • 1Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX.

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Summary

An algorithm accurately identifies incident epithelial ovarian cancer (EOC) cases in claims data, achieving 89.9% sensitivity. This tool aids research when cancer registry data is unavailable, particularly for advanced EOC cases.

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Area of Science:

  • Oncology
  • Health Informatics
  • Epidemiology

Background:

  • Claims-based data sets are valuable for epidemiological research but require robust methods for accurate case identification.
  • Identifying incident epithelial ovarian cancer (EOC) cases in large administrative datasets is crucial for understanding disease patterns and outcomes.
  • Existing methods may not reliably distinguish between new and pre-existing EOC diagnoses in claims data.

Purpose of the Study:

  • To develop and validate an algorithm for identifying incident epithelial ovarian cancer (EOC) cases using administrative claims data.
  • To evaluate the performance of this algorithm against a gold standard, such as SEER-Medicare data.
  • To facilitate EOC research in settings lacking direct access to cancer registry information.

Main Methods:

  • A five-step algorithm was created using clinical expertise, incorporating diagnosis codes, chemotherapy receipt, debulking surgery, and exclusion criteria for non-platinum chemotherapy and prior EOC history.
  • Algorithm performance was assessed using SEER-Medicare claims data, comparing identified cases against four distinct cohorts.
  • Cohorts included: incident EOC, cancer-free controls, incident non-ovarian cancer, and prevalent EOC cases, with SEER tumor registry data serving as the gold standard.

Main Results:

  • The algorithm demonstrated high accuracy, correctly classifying 89.9% of incident EOC cases and nearly 100% of controls and non-EOC cases.
  • Overall performance metrics included 89.9% sensitivity, 93.8% positive predictive value, and >99.9% specificity and negative predictive value.
  • Classification accuracy was significantly higher for advanced stage (III/IV) EOC and tumors with known grades (1-4) compared to early-stage or unknown-grade cases.

Conclusions:

  • The developed algorithm effectively identifies incident epithelial ovarian cancer (EOC) cases within claims-based datasets, particularly those with advanced disease.
  • This validated algorithm provides a reliable tool for EOC research where cancer registry data is not accessible.
  • The findings support the use of claims data for epidemiological studies on EOC, enhancing research capabilities.