Evaluation of Algorithms Using Automated Health Plan Data to Identify Breast Cancer Recurrences
AbstractBackground:. 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 recurrence rates using different recurrence definitions, weighted by inverse sampling probabilities.Results:. Among 600 people, we identified 117 recurrences using the High Specificity and PPV Combination, 50