Predicting sarcopenia risk in stroke patients: a comprehensive nomogram incorporating demographic, anthropometric,...

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.

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.

In this study, we identified several predictive factors for sarcopenia: Gender, Body Mass Index (kg/m²), Standing Height (cm), Alkaline Phosphatase (ALP) (IU/L), Total Calcium (mg/dL), Creatine Phosphokinase (CPK) (IU/L), Hemoglobin (g/dL), and Waist Circumference (cm). Notably, female patients with stroke exhibited a higher risk of sarcopenia. The variables positively associated with increasing risk included Alkaline Phosphatase, Body Mass Index, Waist Circumference, and Hemoglobin, while those negatively associated with risk included Height, Total Calcium, and Creatine Phosphokinase. The nomogram model demonstrated remarkable accuracy in distinguishing between training and validation sets, with areas under the curve of 0.97 and 0.90, respectively. The calibration curve showcased outstanding calibration, and the analysis of the decision curve revealed a broad spectrum of beneficial clinical outcomes.

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.