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.

PURPOSE

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).

PATIENTS AND METHODS

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.

RESULTS

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-TTR) was -8 days (IQR: -21 to +3 days). Restricting the algorithm to chemotherapy codes from oncology departments increased the positive predictive value from 87% to 94% without changing the negative predictive value (99%).

CONCLUSION

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.