ArticleBMC nephrology2022
Development of a risk prediction nomogram for sarcopenia in hemodialysis patients.
Article in BMC nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 5 of them syntheses that pooled it.
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14 citing papers in PubMed, 5 syntheses or guidelines pooled it, 23 citations in OpenAlex.
- Risk prediction model performances for sarcopenia in middle-aged and elderly people: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2026Pooled it
- The risk prediction models for sarcopenia in older adults: a systematic review and critical appraisal.Frontiers in public health · 2026Pooled it
- Risk prediction models for sarcopenia in maintenance hemodialysis patients: a systematic review and meta-analysis.BMC nephrology · 2025Pooled it
- Prediction models for sarcopenia risk in dialysis patients: a systematic review and critical appraisal.Aging clinical and experimental research · 2025Pooled it
- Prevalence and Risk Factors of Sarcopenia in People Receiving Dialysis: A Systematic Review and Meta-Analysis.Seminars in dialysisPooled it
- Development and validation of a predictive nomogram for myopenia in patients with decompensated cirrhosis.Frontiers in medicine · 2026Article
- Association Between Nutritional Status and Severe Sarcopenia in Maintenance Hemodialysis Patients: A Multicenter Cross-Sectional Study with Diagnostic Model Development.International journal of general medicine · 2026Article
- Explainable machine learning models for predicting of protein-energy wasting in patients on maintenance haemodialysis.BMC nephrology · 2025Article
- Sarcopenia in Hemodialysis Patients: Prevalence, Independent Risk Factors, and Functional Implications-A Multicenter Cross-Sectional Study.Journal of clinical medicine · 2025Article
- A nomogram to predict sarcopenia in older adults: a multi-center study in rural China.BMC geriatrics · 2025Article
- Personalised screening tool for early detection of sarcopenia in stroke patients: a machine learning-based comparative study.Aging clinical and experimental research · 2025Article
- Predictive model for sarcopenia in chronic kidney disease: a nomogram and machine learning approach using CHARLS data.Frontiers in medicine · 2025Article
- Development and validation of a predictive model for the risk of sarcopenia in the older adults in China.European journal of medical research · 2024Article
- Observational
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSarcopenia is associated with various adverse outcomes in hemodialysis patients. However, current tools for assessing and diagnosing sarcopenia have limited applicability. In this study, we aimed to develop a simple and reliable nomogram to predict the risk of sarcopenia in hemodialysis patients that could assist physicians identify high-risk patients early.
methodsA total of 615 patients undergoing hemodialysis at the First Affiliated Hospital College of Medicine Zhejiang University between March to June 2021 were included. They were randomly divided into either the development cohort (n = 369) or the validation cohort (n = 246). Multivariable logistic regression analysis was used to screen statistically significant variables for constructing the risk prediction nomogram for Sarcopenia. The line plots were drawn to evaluate the effectiveness of the nomogram in three aspects, namely differentiation, calibration, and clinical net benefit, and were further validated by the Bootstrap method.
resultsThe study finally included five clinical factors to construct the nomogram, including age, C-reactive protein, serum phosphorus, body mass index, and mid-upper arm muscle circumference, and constructed a nomogram. The area under the ROC curve of the line chart model was 0.869, with a sensitivity and specificity of 77% sensitivity and 83%, the Youden index was 0.60, and the internal verification C-statistic was 0.783.
conclusionsThis study developed and validated a nomogram model to predict the risk of sarcopenia in hemodialysis patients, which can be used for early identification and timely intervention in high-risk groups.
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