A Validated Algorithm for Register-Based Identification of Patients with Relapse of Clinical Stage I Testicular Cancer

PURPOSE: The Danish Testicular Cancer (DaTeCa) database aims to monitor and improve quality of care for testicular cancer patients. Relapse data registered in the DaTeCa database rely on manual registration. Currently, some safeguarding against missing registrations is attempted by a non-validated register-based algorithm. However, this algorithm is inaccurate and entails time-consuming medical record reviews. We aimed (1) to validate relapse data as registered in the DaTeCa database, and (2) to develop and validate an improved register-based algorithm identifying patients diagnosed with relapse of clinical stage I testicular cancer. PATIENTS AND METHODS: Patients registered in the DaTeCa database with clinical stage I testicular cancer from 2013 to 2018 were included. Medical record information on relapse data served as a gold standard. A pre-specified algorithm to identify relapse was tested and optimized on a random sample of 250 patients. Indicators of relapse were obtained from pathology codes in the Danish National Pathology Register and from diagnosis and procedure codes in the Danish National Patient Register. We applied the final algorithm to the remaining study population to validate its performance. RESULTS: Of the 1377 included patients, 284 patients relapsed according to the gold standard during a median follow-up time of 5.9 years. The completeness of relapse data registered in the DaTeCa database was 97.2% (95% confidence interval (CI): 95.2–99.1). The algorithm achieved a sensitivity of 99.6% (95% CI: 98.7–100), a specificity of 98.9% (95% CI: 98.2–99.6), and a positive predictive value of 95.9% (95% CI: 93.4–98.4) in the validation cohort (n = 1127, 233 relapses). CONCLUSION: The registration of relapse data in the DaTeCa database is accurate, confirming the database as a reliable source for ongoing clinical quality assessments. Applying the provided algorithm to the DaTeCa database will optimize the accuracy of relapse data further, decrease time-consuming medical record review and contribute to important future clinical research.