Evaluation of Algorithms Using Automated Health Plan Data to Identify Breast Cancer Recurrences.

ABSTRACT:

Background

We updated algorithms to identify breast cancer recurrences from administrative data, extending previously developed methods.

Methods

In this validation study, we evaluated pairs of breast cancer recurrence algorithms (vs. individual algorithms) to identify recurrences. We generated algorithm combinations that categorized discordant algorithm results as no recurrence [High Specificity and PPV (positive predictive value) Combination] or recurrence (High Sensitivity Combination). We compared individual and combined algorithm results to manually abstracted recurrence outcomes from a sample of 600 people with incident stage I-IIIA breast cancer diagnosed between 2004 and 2015. We used Cox regression to evaluate risk factors associated with age- and stage-adjusted recu

SUBMITTER: Aiello Bowles EJ 

PROVIDER: S-EPMC10922110 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature