External validation of a data-driven algorithm to identify breast cancer recurrences from administrative healthcare data
Silvia Mancini, Fabiola Giudici, Lauro Bucchi, Annibale Biggeri, Orietta Giuliani, Flavia Baldacchini, Alessandra Ravaioli, Federica Zamagni, Federica Toffolutti, Stefano Guzzinati, Luigino Dal Maso, Falcini Fabio, Rosa Vattiato · The Breast · 2026
INTRODUCTION: Population-based data on the incidence of breast cancer (BC) recurrence are scarce, because tracking the relevant information is resource-consuming. We report the external validation of a data-driven case-finding algorithm to identify selected treatment and procedure codes relating to BC recurrences from administrative healthcare data. METHODS: We linked a cancer registry-based cohort of BC patients from the Emilia-Romagna (northern Italy) regional hospital discharge and outpatient care databases, and we ran the algorithm. We compared the algorithm-determined incidence and temporal pattern of recurrences during 10 years of follow-up with those obtained with a manual clinical chart review. RESULTS: The algorithm had a 92.3% (95% confidence interval: 87.2%; 95.8%) sensitivity, a 98.0% (97.0%; 98.8%) specificity, a 88.1% (82.4%; 92.5%) positive predictive value and a 98.8% (97.9%; 99.3%) negative predictive value. The two curves of subhazard rates of total recurrences showed a good degree of overlapping and both exhibited the expected bimodal temporal pattern, with a peak between the 2nd and the 3rd year of follow-up and another between the 8th and the 9th year. The algorithm-based 10-year cumulative incidence function of recurrence was 15.8% (13.7%; 18.1%) versus 15.0% (13.0%; 17.3%). The algorithm captured with good precision the temporal pattern of recurrence by molecular profile. The design of the algorithm excluded the recurrences (11.1% of the total) detected during the first 12 months (HER2-negative BC) and 24 months (HER2-positive BC) after the diagnosis. CONCLUSIONS: This validation study demonstrates that identifying breast cancer recurrences from administrative healthcare data is feasible and sufficiently reliable.