Prediction model of malnutrition in hospitalized patients with acute stroke

• Published in: Topics in stroke rehabilitation 2024-07, p.1-15
• Main Authors: Tang, Rong, Guan, Bi, Xie, Jiaoe, Xu, Ying, Yan, Shu, Wang, Jianghong, Li, Yan, Ren, Liling, Wan, Haiyan, Peng, Tangming, Zeng, Liangnan
• Format: Article
• Language: English
• ISSN: 1074-9357; 1945-5119; 1945-5119
• DOI: 10.1080/10749357.2024.2377521

The prognosis of stroke patients is greatly threatened by malnutrition. However, there is no model to predict the risk of malnutrition in hospitalized stroke patients. This study developed a predictive model for identifying high-risk malnutrition in stroke patients. Stroke patients from two tertiary hospitals were selected as the objects. Binary logistic regression was used to build the model. The model's performance was evaluated using various metrics including the receiver operating characteristic curve, Hosmer-Lemeshow test, sensitivity, specificity, Youden index, clinical decision curve, and risk stratification. A total of 319 stroke patients were included in the study. Among them, 27% experienced malnutrition while in the hospital. The prediction model included all independent variables, including dysphagia, pneumonia, enteral nutrition, Barthel Index, upper arm circumference, and calf circumference (all   0.05. The optimal cutoff value for the model was 0.269, with a sensitivity of 0.849 and a specificity of 0.804. After risk stratification, the MRS scores and malnutrition incidences increased significantly with escalating risk levels (