Work

Stage-stratified prognostic impact of comorbidities on breast cancer-specific survival: A population-based flexible parametric modelling study

Year: 2026

Type: article

Abstract: Background: Comorbidities are common among women with breast cancer, yet their independent contribution to prognosis remains poorly characterised, particularly across disease stages. Most prognostic models prioritise tumour-related factors and when comorbidities are included, they are often summarised using aggregate indices, limiting evaluation of specific conditions. We investigated whether individual comorbidities exert effects on breast cancer-specific survival stratified by stage. Methods: We analysed 3323 women aged 20 – 79 years diagnosed with invasive breast cancer in Queensland, Australia (2010–2013) with a follow-up to December 2020. Comorbidities were self-reported and mapped to ICD-10. Flexible parametric survival models were fitted separately for early (I-II) and late (III-IV) stages, adjusting for established clinical factors. Prognostic performance was quantified using Royston’s D-statistic, R D 2 and Harrell’s concordance index. Results: Comorbidities had no prognostic impact on early-stage disease. In late-stage breast cancer, chronic bronchitis independently predicted poorer breast cancer-specific survival after adjusting for subtype, grade, age, family history and detection mode. The final late-stage model demonstrated good discrimination (C-statistic 0.71) and explained 26% of the variation in survival. Model-based predictions showed clinically meaningful reductions in 5- and 7-year survival among women with bronchitis, partially mitigated by a family history of breast/ovarian cancer. Conclusion: Stage stratification revealed prognostic signals that were obscured in pooled analyses. Chronic bronchitis worsened survival in late-stage disease, highlighting the value of assessing individual comorbidities during treatment planning. These findings support guideline recommendations for comorbidity informed management and underscore the need for stage-specific prognostic modelling frameworks.

Source: Cancer Epidemiology

DOI: 10.1016/j.canep.2026.103146