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Introduction To Survival Analysis01:18

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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Identifying Breast Cancer Recurrence in Administrative Data: Algorithm Development and Validation.

Claire M B Holloway1,2, Omid Shabestari3, Maria Eberg4

  • 1Disease Pathway Management, Clinical Institutes and Quality Programs, Ontario Health, 525 University Avenue, Toronto, ON M5G 2L3, Canada.

Current Oncology (Toronto, Ont.)
|August 25, 2022
PubMed
Summary
This summary is machine-generated.

An algorithm was developed to detect second breast cancer events in Ontario, Canada, using administrative data. This method accurately identifies recurrence, aiding healthcare system monitoring and outcome measurement.

Keywords:
algorithmsbreast neoplasmscohort studiesdiagnostic techniques and procedureshealthcarehumanslocalneoplasm recurrenceoutcome assessmentpredictive value of testsprevalencerecurrence

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Area of Science:

  • Oncology
  • Health Informatics
  • Epidemiology

Background:

  • Breast cancer recurrence is a critical outcome but not consistently reported in cancer registries.
  • Accurate tracking of second breast cancer events is essential for patient care and healthcare system evaluation.

Purpose of the Study:

  • To develop and validate an algorithm for identifying second breast cancer events using administrative healthcare data.
  • To assess the feasibility of using administrative data for population-level breast cancer outcome monitoring.

Main Methods:

  • Retrospective cohort study of stage 0-III breast cancer patients in Ontario, Canada (2009-2012).
  • Algorithm applied to administrative healthcare utilization data from six months post-diagnosis.
  • Algorithm validated against manual patient record review (n=2245) for diagnostic accuracy.

Main Results:

  • The algorithm demonstrated high accuracy: 93% overall, 85% sensitivity, 94% specificity, and 98% negative predictive value.
  • Identified a second breast cancer event rate of 16.5% via algorithm versus 13.0% via manual review.
  • Algorithm performance was comparable to existing methods, suitable for healthcare system surveillance.

Conclusions:

  • An administrative data-based algorithm can reliably identify second breast cancer events in a population.
  • This approach enables the development of novel outcome measures for healthcare system monitoring.
  • Utilizing administrative data with advanced methods enhances the understanding of breast cancer outcomes.