Although geriatric assessment (GA) has been used for a long time in the field of geriatrics and internal medicine, there are few studies on its application in the field of breast surgery. Therefore, the utility of specific GA domains for the assessment of older patients with breast cancer remains unclear. The aim of the present study was to evaluate the association between specific GA domains and the survival rate of older patients with breast cancer.
We used the database of Peking Union Medical College Hospital to identify older patients who were newly diagnosed with breast cancer between 2012 and 2018 and retrospectively analysed the data of 541 patients aged ≥65 years. Patients with metastatic cancer and those with missing vital status data were excluded. The primary outcomes were overall survival (OS) and breast cancer-specific survival. The GA domains used in this study included functional status, comorbidities, and psychological state. Multivariate regression analysis was used to estimate hazard ratios for these three domains.
After a median follow-up of 72 months, we observed a significant relationship between functional impairment and mortality (adjusted HR: 3.06, 95% confidence interval [CI]: 1.83-5.10, P<0.001). Similarly, patients with severe comorbidities (adjusted HR: 2.35; 95% CI: 1.16-4.75, P=0.017) and an impaired psychological state (adjusted HR: 2.82, 95% CI: 1.45-5.50, P=0.002) showed worse OS rates. Accordingly, addition of the three GA domains to the basic model, which included age, tumour stage, lymph node stage, and intrinsic molecular subtype as baseline variables, yielded higher C‐statistics for mortality analysis (from 0.713 to 0.740).
To our knowledge, this is the first study to include specific GA domains in a prognostic model for older patients with breast cancer in China. Three domains, namely functional status, comorbidities, and psychological state, should be considered for survival analyses in this particular population. The full model including these three GA domains may be more accurate in predicting the survival of older patients with breast cancer.