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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.
To construct a symptom network of maintenance hemodialysis patients and identify the core symptoms and core symptom clusters. Finally, this study provides a reference for accurate symptom management.
A correlational cross-sectional design. A total of 368 patients who underwent maintenance hemodialysis were enrolled from two hemodialysis centers in Chengdu, Sichuan Province, China. A symptom network was constructed with the R coding language to analyze the centrality index. Symptom clusters were extracted by exploratory factor analysis, and core symptom clusters were preliminarily determined according to the centrality index of the symptom network.
The most common symptoms in maintenance hemodialysis patients were fatigue, dry skin, and pruritus. In the symptom network, headache had the highest mediation centrality (rB = 2.789) and closeness centrality (rC = 2.218) and the greatest intensity of numbness or tingling in the feet (rS = 1.952). A total of six symptom clusters were extracted, including pain and discomfort symptom clusters, emotional symptom clusters, gastrointestinal symptom clusters, sleep disorder symptom clusters, dry symptom clusters, and sexual dysfunction symptom clusters. The cumulative variance contribution rate was 69.269%.
Fatigue, dry skin, and pruritus are the sentinel symptoms of maintenance hemodialysis patients, headache is the core symptom and bridge symptom, and pain symptom clusters are the core symptom clusters of MHD patients. Nurses can develop interventions based on core symptoms and symptom clusters to improve the effectiveness of symptom management in maintenance hemodialysis patients.
Understanding the core symptoms and symptom groups that plague maintenance hemodialysis patients is critical to providing accurate symptom management. To ensure that maintenance hemodialysis patients receive effective support during treatment, reduce the adverse effects of symptoms, and improve the quality of life of patients.
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