ArticleRenal failure2023
Development and validation of a nomogram model for predicting low muscle mass in patients undergoing hemodialysis.
Article in Renal failure, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 8 citations in OpenAlex.
- 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
- Risk factors for pituitary apoplexy: a meta-analysis and development of a clinical prediction nomogram.Frontiers in neurology · 2026Article
- Development and Validation of a Diagnostic Nomogram Model for Predicting Cognitive Frailty in Acute Coronary Syndrome.Clinical interventions in aging · 2025Observational
- Developing and Validating a Nomogram for Non-Adherence to Inhaler Therapy Among Elderly Chronic Obstructive Pulmonary Disease Patients Based on the Social Ecological Model.Patient preference and adherence · 2024Article
- Development and validation of a nomogram to predict acute postoperative urinary retention in ischemic stroke patients following femoral artery puncture.Frontiers in neurology · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMuscle mass is important in determining patients' nutritional status. However, measurement of muscle mass requires special equipment that is inconvenient for clinical use. We aimed to develop and validate a nomogram model for predicting low muscle mass in patients undergoing hemodialysis (HD).
methodsA total of 346 patients undergoing HD were enrolled and randomly divided into a 70% training set and a 30% validation set. The training set was used to develop the nomogram model, and the validation set was used to validate the developed model. The performance of the nomogram was assessed using the receiver operating characteristic (ROC) curve, a calibration curve, and the Hosmer-Lemeshow test. A decision curve analysis (DCA) was used to evaluate the clinical practicality of the nomogram model.
resultsAge, sex, body mass index (BMI), handgrip strength (HGS), and gait speed (GS) were included in the nomogram for predicting low skeletal muscle mass index (LSMI). The diagnostic nomogram model exhibited good discrimination with an area under the ROC curve (AUC) of 0.906 (95% CI, 0.862-0.940) in the training set and 0.917 (95% CI, 0.846-0.962) in the validation set. The calibration analysis also showed excellent results. The nomogram demonstrated a high net benefit in the clinical decision curve for both sets.
conclusionsThe prediction model included age, sex, BMI, HGS, and GS, and it can successfully predict the presence of LSMI in patients undergoing HD. This nomogram provides an accurate visual tool for medical staff for prediction, early intervention, and graded management.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.