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Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort

Teresa A'mar1, J David Beatty2, Catherine Fedorenko1

  • 1Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States.

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|August 18, 2020
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A new data mining algorithm accurately identifies second breast cancer events (SBCE) using linked registry and medical claims data. This approach streamlines cancer registry operations for tracking recurrence.

Keywords:
breast cancercancer recurrence eventcancer registriesdata miningmedical claimsmedical informaticsstatistical learning

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Area of Science:

  • Oncology
  • Data Science
  • Public Health

Background:

  • Automated methods are needed to integrate cancer recurrence data into population-based cancer registries.
  • Accurate tracking of second breast cancer events (SBCE) is crucial for patient management and registry completeness.

Purpose of the Study:

  • To evaluate the accuracy of a novel data mining algorithm for identifying SBCE from linked registry and medical claims data.
  • To assess the algorithm's ability to determine the occurrence and timing of SBCE.

Main Methods:

  • A supervised learning approach was used with data from 3092 stage I/II breast cancer cases and 394 recurrences.
  • The algorithm classified months post-primary treatment as pre- or post-SBCE using registry and claims data features.
  • A probability threshold (PT) determined SBCE classification; timing was estimated by maximum probability increase.

Main Results:

  • The algorithm achieved high accuracy in predicting SBCE, with an area under the curve of 0.986.
  • Person-level predictions demonstrated excellent performance: 89% sensitivity, 98% specificity, 85% positive predictive value, and 98% negative predictive value.
  • The predicted timing of SBCE closely matched observed recurrence, with a median difference of 0 months.

Conclusions:

  • Data mining of medical claims data is a promising method for improving cancer registry operations.
  • This approach offers a feasible way to collect comprehensive information on second breast cancer events.
  • The developed algorithm can enhance the efficiency and accuracy of cancer surveillance systems.