Identification of endometrial cancer recurrence – a validated algorithm based on nationwide Danish registries. (3rd April 2021)

• Record Type: Journal Article
• Authors: Rasmussen, Linda A.; Jensen, Henry; Virgilsen, Line F.; Jeppesen, Mette M.; Blaakaer, Jan; Hansen, Dorte G.; Jensen, Pernille T.; Mogensen, Ole; Vedsted, Peter
• Is Part Of: Acta oncologica. Volume 60:Number 4(2021)
• Page Start: 452
• Page End: 458
• Publication Date: 2021-04-03
• Subjects: Oncology -- Periodicals; Cancer -- Treatment -- Periodicals; 616.992
• DOI: 10.1080/0284186X.2020.1859133
• Languages: English
• ISSNs: 0284-186X

Abstract

Abstract: Introduction: Recurrence of endometrial cancer is not routinely registered in the Danish national health registers. The aim of this study was to develop and validate a register-based algorithm to identify women diagnosed with endometrial cancer recurrence in Denmark to facilitate register-based research in this field. Material and methods: We conducted a cohort study based on data from Danish health registers. The algorithm was designed to identify women with recurrence and estimate the accompanying diagnosis date, which was based on information from the Danish National Patient Registry and the Danish National Pathology Registry. Indicators of recurrence were pathology registrations and procedure or diagnosis codes suggesting recurrence and related treatment. The gold standard for endometrial cancer recurrence originated from a Danish nationwide study of 2612 women diagnosed with endometrial cancer, FIGO stage I–II during 2005–2009. Recurrence was suspected in 308 women based on pathology reports, and recurrence suspicion was confirmed or rejected in the 308 women based on reviews of the medical records. The algorithm was validated by comparing the recurrence status identified by the algorithm and the recurrence status in the gold standard. Results: After relevant exclusions, the final study population consisted of 268 women, hereof 160 (60%) with recurrence according to the gold standard. The algorithm displayed a sensitivity of 91.3% (95% confidence interval (CI): 85.8–95.1), a specificity of 91.7% (95% CI: 84.8–96.1) and a positive predictive value of 94.2% (95% CI: 89.3–97.3). The algorithm estimated the recurrence date within 30 days of the gold standard in 86% and within 60 days of the gold standard in 94% of the identified patients. Discussion: The algorithm demonstrated good performance; it could be a valuable tool for future research in endometrial cancer recurrence and may facilitate studies with potential impact on clinical practice.