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To construct and evaluate a novel nomogram for predicting the risk of dual dimensional frailty (comorbidity between physical frailty and social frailty) in older maintenance haemodialysis.
A cross-sectional investigation was conducted. A total of 386 older MHD patients were recruited between September and December 2024 from four haemodialysis centres in four tertiary hospitals in Sichuan Province, China. LASSO regression and binary logistic regression were employed to determine the predictors of dual dimensional frailty. The prediction performance of the model was evaluated by discrimination and calibration. The decision curve was utilised to estimate the clinical utility. Internal validation with 1000 bootstrap samples was conducted to minimise overfitting.
In the overall sample (386 cases), a total of 92 (23.8%) of patients exhibited dual dimensional frailty. Five relevant predictors, including physical activity, self-perceived health status, ADL impairment, malnutrition, and self-perceptions of aging, were identified for constructing the nomogram. Internal validation indicated excellent discriminatory power and calibration of the model, while the clinical decision curve demonstrated its remarkable clinical utility.
The novel nomogram constructed in this study holds promise for aiding healthcare professionals in identifying physical and social frailty risks among older patients on maintenance haemodialysis, potentially informing early and targeted interventions.
This nomogram enables nurses to efficiently stratify dual-dimensional frailty risk during routine assessments, facilitating early identification of high-risk patients. Its visual output can guide tailored interventions, such as exercise programmes, nutritional support, and counselling, while optimising resource allocation.
Data were collected from self-reported conditions and patients' clinical information.
STROBE checklist was employed.
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