ArticleFrontiers in neurology2024
Predicting sarcopenia risk in stroke patients: a comprehensive nomogram incorporating demographic, anthropometric, and biochemical indicators.
Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Associated factors for stroke-related sarcopenia: a scoping review.Frontiers in neurology · 2026Article
- Association between the ratio of uric acid to high-density lipoprotein cholesterol (UHR) and the abnormal risk of sarcopenia: Evidence from two large population-based surveys and interpretable machine learning-driven sarcopenia screening.Therapeutic advances in endocrinology and metabolism · 2026Article
- Development and validation of a sarcopenia risk prediction model for community-dwelling older adults.BMC public health · 2025Article
- Personalised screening tool for early detection of sarcopenia in stroke patients: a machine learning-based comparative study.Aging clinical and experimental research · 2025Article
- Construct prediction models for low muscle mass with metabolic syndrome using machine learning.PloS one · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Although there is a strong correlation between stroke and sarcopenia, there has been a lack of research into the potential risks associated with post-stroke sarcopenia. Predictors of sarcopenia are yet to be identified. We aimed at developing a nomogram able to predict sarcopenia in patients with stroke. Methods: The National Health and Nutrition Examination Survey (NHANES) cycle year of 2011 to 2018 was divided into two groups of 209 participants-one receiving training and the other validation-in a random manner. The Lasso regression analysis was used to identify the risk factors of sarcopenia, and a nomogram model was created to forecast sarcopenia in the stroke population. The model was assessed based on its discrimination area under the receiver operating characteristic curve, calibration curves, and clinical utility decision curve analysis curves. Results: In this study, we identified several predictive factors for sarcopenia: Gender, Body Mass Index (kg/m Conclusion: This study creates a new nomogram which can be used to predict pre-sarcopenia in stroke. The new screening device is accurate, precise, and cost-effective, enabling medical personnel to identify patients at an early stage and take action to prevent and treat illnesses.
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Registered trials
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.