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Development and validation of a nomogram for predicting stroke-associated sarcopenia: A prospective observational
Jianxiang Wang1,2, Xiaojiao Yin1,2, Maorong Mu1,2
1School of Nursing, Kunming Medical University, Kunming, Yunnan Province, China.
Medicine
|October 15, 2025
Summary
A new risk prediction model helps identify stroke patients at high risk for sarcopenia. This tool uses clinical factors to enable early intervention and improve patient outcomes after stroke.
Area of Science:
- Neurology
- Geriatrics
- Clinical Medicine
Background:
- Sarcopenia is a frequent complication following stroke.
- Effective tools for identifying high-risk stroke patients are limited.
Purpose of the Study:
- To develop and validate a clinical risk prediction model for sarcopenia in stroke patients.
- To identify key predictors of sarcopenia post-stroke.
Main Methods:
- Prospective data collection from 313 stroke patients across two medical centers.
- Logistic regression analysis to identify independent predictors.
- Nomogram construction and external validation.
Main Results:
- Key predictors identified: BMI, serum albumin, nasogastric tube, NIHSS score, and stroke history.
- The model demonstrated good discriminative ability (AUC 0.891 training, 0.743 validation).
- Calibration curves showed good agreement between predicted and observed outcomes.
Conclusions:
- A validated risk prediction model can aid clinicians in identifying stroke patients susceptible to sarcopenia.
- Early identification facilitates timely interventions, potentially improving clinical outcomes.
