Development and validation of case-finding algorithms for recurrence of breast cancer using routinely collected administrative data

Background: Recurrence is not explicitly documented in cancer registry data that are widely used for research. Patterns of events after initial treatment such as oncology visits, re-operation, and receipt of subsequent chemotherapy or radiation may indicate recurrence. This study aimed to develop and validate algorithms for identifying breast cancer recurrence using routinely collected administrative data.

Methods: The study cohort included all young (≤ 40 years) breast cancer patients (2007-2010), and all patients receiving neoadjuvant chemotherapy (2012-2014) in Alberta, Canada. Health events (including mastectomy, chemotherapy, radiation, biopsy and specialist visits) were obtained from provincial administrative data. The algorithms were developed using classification and regression tree (CART) models and validated against primary chart review.

Results: Among 598 patients, 121 (20.2%) had recurrence after a median follow-up of 4 years. The high sensitivity algorithm achieved 94.2% (95% CI: 90.1-98.4%) sensitivity, 93.7% (91.5-95.9%) specificity, 79.2% (72.5-85.8%) positive predictive value (PPV), and 98.5% (97.3-99.6%) negative predictive value (NPV). The high PPV algorithm had 75.2% (67.5-82.9%) sensitivity, 98.3% (97.2-99.5%) specificity, 91.9% (86.6-97.3%) PPV, and 94% (91.9-96.1%) NPV. Combining high PPV and high sensitivity algorithms with additional (7.5%) chart review to resolve discordant cases resulted in 94.2% (90.1-98.4%) sensitivity, 98.3% (97.2-99.5%) specificity, 93.4% (89.1-97.8%) PPV, and 98.5% (97.4-99.6%) NPV.

Conclusion: The proposed algorithms based on routinely collected administrative data achieved favorably high validity for identifying breast cancer recurrences in a universal healthcare system in Canada.