Prediction model of malnutrition in hospitalized patients with acute stroke

ABSTRACT

Objective

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.

Methods

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.

Results

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 p < 0.05). The AUC area in the modeling group was 0.885, while in the verification group, it was 0.797. The prediction model produces greater net clinical benefit when the risk threshold probability is between 0% and 80%, as revealed by the clinical decision curve. All p values of the Hosmer test were > 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 (p < 0.05) in both modeling and validation groups.

Conclusions

This study developed a prediction model for malnutrition in stroke patients. It has been proven that the model has good differentiation and calibration.

KEYWORDS:

• Stroke
• malnutrition
• risk
• evaluation model
• prediction model

Supplementary Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10749357.2024.2377521

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Study design: Liangnan Zeng, Tangming Peng, Bi Guan. Data collection, analysis and interpretation: Rong Tang and Liangnan Zeng. Drafting of the manuscript: Rong Tang, Jiaoe Xie, Liangnan Zeng. Approval of the final version for publication: all coauthors.

Additional information

Funding

This study was funded by the Sichuan nursing research project plan [No. H22043] and Chengdu Medical Research Project [No. 2023357]. Chengdu Science and Technology Bureau [No. 2024-YF05-00804-SN].