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Thomas Wagner,1,2 Jakob Lauritsen,1 Mikkel Bandak,1 Linda Aagaard Rasmussen,3 Johannes Bakker,4 Hanna Birkbak Hovaldt,4 Heidi Larsson,4 Ib Jarle Christensen,1 Birgitte Grønkær Toft,2 Mads Agerbæk,5 Lars Dysager,6 Michael Kreiberg,1 Josephine Julie Rosenvilde,1 Birte Engvad,7 Daniel M Berney,8 Gedske Daugaard1 1Department of Oncology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark; 2Department of Pathology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark; 3Research Unit for General Practice, Aarhus, Denmark; 4The Danish Clinical Quality Program – National Clinical Registries (RKKP), Aarhus, Odense and Copenhagen, Denmark; 5Department of Oncology, Aarhus University Hospital, Aarhus, Denmark; 6Department of Oncology, Odense University Hospital, Odense, Denmark; 7Department of Pathology, Odense University Hospital, Odense, Denmark; 8Centre for Cancer Biomarkers and Biotherapeutics, Barts Cancer Institute, Queen Mary University of London, London, UKCorrespondence: Thomas Wagner, Department of Oncology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, Copenhagen, 2100, Denmark, Tel +45 35459682, Email thomas.wagner.nielsen@regionh.dkPurpose: 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.Keywords: testicular cancer, relapse, algorithm, validation, registries

Linda Aagaard Rasmussen,1 Niels Lyhne Christensen,2 Anne Winther-Larsen,3 Susanne Oksbjerg Dalton,4,5 Line Flytkjær Virgilsen,1 Henry Jensen,1 Peter Vedsted1 1Research Unit for General Practice, Aarhus, Denmark; 2Department of Pulmonary Medicine and Allergy, Aarhus University Hospital, Aarhus, Denmark; 3Department of Clinical Biochemistry, Aarhus University Hospital, Aarhus, Denmark; 4Survivorship and Inequality in Cancer, Danish Cancer Society Research Center, Copenhagen, Denmark; 5Department of Clinical Oncology & Palliative Care, Zealand University Hospital, Næstved, DenmarkCorrespondence: Linda Aagaard Rasmussen, Research Unit for General Practice, Bartholins Allé 2, Aarhus C, 8000, Denmark, Tel +45 8716 8365, Email linda.rasmussen@ph.au.dkIntroduction: Recurrence of cancer is not routinely registered in Danish national health registers. This study aimed to develop and validate a register-based algorithm to identify patients diagnosed with recurrent lung cancer and to estimate the accuracy of the identified diagnosis date.Material and Methods: Patients with early-stage lung cancer treated with surgery were included in the study. Recurrence indicators were diagnosis and procedure codes recorded in the Danish National Patient Register and pathology results recorded in the Danish National Pathology Register. Information from CT scans and medical records served as the gold standard to assess the accuracy of the algorithm.Results: The final population consisted of 217 patients; 72 (33%) had recurrence according to the gold standard. The median follow-up time since primary lung cancer diagnosis was 29 months (interquartile interval: 18– 46). The algorithm for identifying a recurrence reached a sensitivity of 83.3% (95% CI: 72.7– 91.1), a specificity of 93.8% (95% CI: 88.5– 97.1), and a positive predictive value of 87.0% (95% CI: 76.7– 93.9). The algorithm identified 70% of the recurrences within 60 days of the recurrence date registered by the gold standard method. The positive predictive value of the algorithm decreased to 70% when the algorithm was simulated in a population with a recurrence rate of 15%.Conclusion: The proposed algorithm demonstrated good performance in a population with 33% recurrences over a median of 29 months. It can be used to identify patients diagnosed with recurrent lung cancer, and it may be a valuable tool for future research in this field. However, a lower positive predictive value is seen when applying the algorithm in populations with low recurrence rates.Keywords: lung neoplasms, recurrence, algorithms, validation study, registries, Denmark

Linda Aagaard Rasmussen,1 Henry Jensen,1 Line Flytkjaer Virgilsen,1 Lisbet Rosenkrantz Hölmich,2,3 Peter Vedsted1 1Research Centre for Cancer Diagnosis in Primary Care (CaP), Research Unit for General Practice, Aarhus, Denmark; 2Department of Plastic Surgery, Herlev and Gentofte Hospital, Herlev, Denmark; 3Department of Clinical Medicine, Copenhagen University, Copenhagen, DenmarkCorrespondence: Linda Aagaard RasmussenResearch Unit for General Practice, Aarhus, DenmarkTel +45 8716 8369Fax +45 8612 4788Email linda.rasmussen@ph.au.dkPurpose: Information on cancer recurrence is rarely available outside clinical trials. Wide exclusion criteria used in clinical trials tend to limit the generalizability of findings to the entire population of people living beyond a cancer disease. Therefore, population-level evidence is needed. The aim of this study was to develop and validate a register-based algorithm to identify patients diagnosed with recurrence after curative treatment of malignant melanoma.Patients and Methods: Indicators of recurrence were diagnosis and procedure codes recorded in the Danish National Patient Register and pathology results recorded in the Danish National Pathology Register. Medical records on recurrence status and recurrence date in the Danish Melanoma Database served as the gold standard to assess the accuracy of the algorithm.Results: The study included 1747 patients diagnosed with malignant melanoma; 95 (5.4%) were diagnosed with recurrence of malignant melanoma according to the gold standard. The algorithm reached a sensitivity of 93.7% (95% confidence interval (CI) 86.8– 97.6), a specificity of 99.2% (95% CI: 98.6– 99.5), a positive predictive value of 86.4% (95% CI: 78.2– 92.4), and negative predictive value of 99.6% (95% CI: 99.2– 99.9). Lin’s concordance correlation coefficient was 0.992 (95% CI: 0.989– 0.996) for the agreement between the recurrence dates generated by the algorithm and by the gold standard.Conclusion: The algorithm can be used to identify patients diagnosed with recurrence of malignant melanoma and to establish the timing of recurrence. This can generate population-level evidence on disease-free survival and diagnostic pathways for recurrence of malignant melanoma.Keywords: melanoma, recurrence, algorithms, validation study, registries, Denmark

Linda Aagaard Rasmussen,1 Henry Jensen,1 Line Flytkjær Virgilsen,1 Jørgen Bjerggaard Jensen,2,3 Peter Vedsted1 1Research Centre for Cancer Diagnosis in Primary Care (CaP), Research Unit for General Practice, Department of Public Health, Aarhus University, 8000 Aarhus C, Denmark; 2Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark; 3Department of Urology, Aarhus University Hospital, 8200 Aarhus N, Denmark Purpose: Recurrence of cancer is not routinely registered in the national registers in Denmark. The aim of this study was to develop and validate a register-based algorithm to identify patients diagnosed with recurrence of invasive bladder cancer (BC). Materials and methods: We performed a cohort study based on data from Danish national health registers. Diagnosis codes and procedural codes in the Danish National Patient Register and Systematized Nomenclature of Medicine codes in the Danish National Pathology Register were used as indicators of cancer recurrence. Status and date of recurrence as registered in the Danish Bladder Cancer Database (DaBlaCa-data) were used as the gold standard of BC recurrence to ascertain the accuracy of the algorithm. Results: The algorithm reached a sensitivity of 85% (95% CI: 78–91), a specificity of 90% (95% CI: 79–96), and a positive predictive value of 95% (95% CI: 89–98). The algorithm demonstrated superior performance in patients undergoing cystectomy compared to patients undergoing radiotherapy as primary BC treatment. The concordance correlation coefficient for the agreement between the recurrence dates generated by the algorithm and the gold standard was 0.96 (95% CI: 0.95–0.98), and the estimated date was set within 90 days of the gold standard date for 90% of patients. Conclusion: The proposed algorithm to identify patients diagnosed with BC recurrence from Danish national registries showed excellent performance in terms of ascertaining occurrence and the timing of BC recurrence. Keywords: urinary bladder neoplasms, recurrence, algorithms, validation studies, registers, Denmark