ArticleSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2024
Development and validation of a prediction model for frailty in breast cancer patients with extended survival.
Article in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk factors for frailty in older adults with cancer: a systematic review.BMC geriatrics · 2026Pooled it
- The application value of frailty assessment tools in the perioperative period: A narrative review.Journal of anesthesia and translational medicine · 2026Review
- Prevalence of preoperative multidimensional frailty and associated factors in middle-aged and older patients with newly diagnosed breast cancer: a cross-sectional study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Development and validation of a risk prediction model for postoperative frailty in breast cancer patients.PeerJ · 2026Article
- Development of a Prediction Model for Combined Assessment of SII, Albumin, and Fibrinogen to Evaluate Frailty Risk in Patients with Inflammatory Bowel Disease.Journal of inflammation research · 2025Article
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Authors and funding
12 authors.
Funding
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Abstract
backgroundBreast cancer (BC) patients with extended survival show a higher incidence of frailty. This study aimed to develop and validate a novel model combining sociodemographic factors (SF) and disease-related factors (DRF) to identify frailty in BC patients with extended survival.
methodsThis cross-sectional study examined data from 1167 patients admitted to a large urban academic medical centre. Three types of predictive models were constructed in the training set (817 patients): the SF model, the DRF model, and the SF + DRF model (combined model). The model performance and effectiveness were assessed using receiver operating characteristic (ROC) curves, calibration plots and decision curves analysis (DCA). Then the model was subsequently validated on the validation set.
resultsThe incidence of frailty in BC patients with extended survival was 35.8%. We identified six independent risk factors including age, health status, chemotherapy, endocrine therapy, number of comorbidities and oral medications. Ultimately, we constructed an optimal model (combined model C) for frailty. The predictive model showed significantly high discriminative accuracy in the training set AUC: 0.754, (95% CI, 0.719-0.789; sensitivity: 76.8%, specificity: 62.2%) and validation set AUC: 0.805, (95% CI, 0.76-0.85), sensitivity: 60.8%, specificity: 87.1%) respectively. A prediction nomogram was constructed for the training and validation sets. Calibration and DCA were performed, which indicated that the clinical model presented satisfactory calibration and clinical utility. Ultimately, we implemented the prediction model into a mobile-friendly web application that provides an accurate and individualized prediction for BC.
conclusionsThe present study demonstrated that the prevalence of frailty in BC patients with extended survival was 35.8%. We developed a novel model for screening frailty, which may provide evidence for frailty screening and prevention.
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