A validated algorithm for register-based identification of patients with recurrence of breast cancer—Based on Danish Breast Cancer Group (DBCG) data. (April 2019) - Aagaard Rasmussen, Linda; Jensen, Henry; Flytkjær Virgilsen, Line; Jellesmark Thorsen, Lise Bech; Vrou Offersen, Birgitte; Vedsted, Peter
Highlights: Data on cancer recurrence are incomplete in Danish health registers. An algorithm was developed and identified 97% of all breast cancer recurrences. The algorithm reached a high positive predictive value of 94%. The algorithm displayed a negative predictive value of 99%. Abstract: Background: Cancer recurrence is not routinely and completely registered in Danish national health registers, which challenges register-based research. The aim of this study was to develop and validate a register-based algorithm to identify patients with recurrence of breast cancer (BC). Methods: We conducted a cohort study based on data from Danish national health registers and used the Danish National Patient Register and the Danish National Pathology Register as sources to identify BC recurrence. We used data from the Danish Breast Cancer Group (DBCG) validated against medical records on recurrence status and recurrence date for 471 women with early stage unilateral BC as the gold standard of BC recurrence to assess the accuracy of the algorithm to identify BC recurrence. Results: The algorithm displayed a sensitivity of 97.3% (95% confidence interval (CI): 93.2–99.3), a specificity of 97.2% (95% CI: 94.8–98.7) and a positive predictive value of 94.4% (95% CI: 89.2–97.3). The concordance correlation coefficient for the agreement between recurrence dates generated by the algorithm and the gold standard was 0.97 (95% CI: 0.96-0.98), and the date was estimated within +/−30 days of the gold standard in 66% of the patients and within +/−60 days in 76% of the patients. Conclusion: The developed algorithm almost perfectly identified BC recurrence and with reasonable timing compared to the gold standard.