Identifying Recurrences Among Non-Metastatic Colorectal Cancer Patients Using National Health Data Registries: Validation and Optimization of a Registry-Based Algorithm in a Modern Danish Cohort.
chemotherapy
oncology
surveillance
time to recurrence
Journal
Clinical epidemiology
ISSN: 1179-1349
Titre abrégé: Clin Epidemiol
Pays: New Zealand
ID NLM: 101531700
Informations de publication
Date de publication:
2023
2023
Historique:
received:
03
11
2022
accepted:
20
02
2023
entrez:
6
3
2023
pubmed:
7
3
2023
medline:
7
3
2023
Statut:
epublish
Résumé
Colorectal cancer (CRC) recurrence is not routinely recorded in Danish health data registries. Here, we aimed to revalidate a registry-based algorithm to identify recurrences in a contemporary cohort and to investigate the accuracy of estimating the time to recurrence (TTR). We ascertained data on 1129 patients operated for UICC TNM stage I-III CRC during 2012-2017 registered in the CRC biobank at the Department of Molecular Medicine, Aarhus University Hospital, Denmark. Individual-level data were linked with data from the Danish Colorectal Cancer Group database, Danish Cancer Registry, Danish National Registry of Patients, and Danish Pathology Registry. The algorithm identified recurrence based on diagnosis codes of local recurrence or metastases, the receipt of chemotherapy, or a pathological tissue assessment code of recurrence more than 180 days after CRC surgery. A subgroup was selected for validation of the algorithm using medical record reviews as a reference standard. We found a 3-year cumulative recurrence rate of 20% (95% CI: 17-22%). Manual medical record review identified 80 recurrences in the validation cohort of 522 patients. The algorithm detected recurrence with 94% sensitivity (75/80; 95% CI: 86-98%) and 98% specificity (431/442; 95% CI: 96-99%). The positive and negative predictive values of the algorithm were 87% (95% CI: 78-93%) and 99% (95% CI: 97-100%), respectively. The median difference in TTR (TTR The algorithm detected recurrence and TTR with high precision in this contemporary cohort. Restriction to chemotherapy codes from oncology departments using department classifications improves the algorithm. The algorithm is suitable for use in future observational studies.
Identifiants
pubmed: 36874205
doi: 10.2147/CLEP.S396140
pii: 396140
pmc: PMC9983442
doi:
Types de publication
Journal Article
Langues
eng
Pagination
241-250Informations de copyright
© 2023 Nors et al.
Déclaration de conflit d'intérêts
Mr Jesper Nors reports personal fees from Aarhus University, during the conduct of the study. Prof. Dr Claus Lindbjerg Andersen reports grants from The Novo Nordic Foundation and The Danish Cancer Society, during the conduct of the study. The authors report no other conflicts of interest in this work.
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