Rasmussen Linda A, Jensen Henry, Virgilsen Line F, Jeppesen Mette M, Blaakaer Jan, Hansen Dorte G, Jensen Pernille T, Mogensen Ole, Vedsted Peter
Research Centre for Cancer Diagnosis in Primary Care (CaP), Research Unit for General Practice, Aarhus, Denmark.
Department of Gynaecology and Obstetrics, Odense University Hospital, Odense, Denmark.
Acta Oncol. 2021 Apr;60(4):452-458. doi: 10.1080/0284186X.2020.1859133. Epub 2020 Dec 11.
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.
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.
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.
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.
丹麦国家健康登记系统并未常规登记子宫内膜癌复发情况。本研究旨在开发并验证一种基于登记系统的算法，以识别丹麦被诊断为子宫内膜癌复发的女性，从而促进该领域基于登记系统的研究。
我们基于丹麦健康登记系统的数据进行了一项队列研究。该算法旨在识别复发女性并估算伴随的诊断日期，其依据丹麦国家患者登记系统和丹麦国家病理登记系统的信息。复发指标包括病理登记以及提示复发和相关治疗的程序或诊断代码。子宫内膜癌复发的金标准源自丹麦一项针对2005年至2009年期间诊断为FIGO I-II期子宫内膜癌的2612名女性的全国性研究。基于病理报告，308名女性被怀疑复发，通过病历审查对这308名女性的复发怀疑进行了确认或排除。通过比较算法识别的复发状态与金标准中的复发状态对该算法进行了验证。
经过相关排除后，最终研究人群包括268名女性，其中根据金标准有160名（60%）复发。该算法的敏感性为91.3%（95%置信区间（CI）：85.8 - 95.1），特异性为91.7%（95% CI：84.8 - 96.1），阳性预测值为94.2%（95% CI：89.3 - 97.3）。在86%的已识别患者中，该算法估计的复发日期在金标准的30天内，在94%的已识别患者中在金标准的60天内。
该算法表现良好；它可能是未来子宫内膜癌复发研究的宝贵工具，并可能促进对临床实践有潜在影响的研究。
