Risk factors and a prediction model for malnutrition after traumatic brain injury
10.3760/cma.j.cn421666-20240317-00199
- VernacularTitle:颅脑损伤患者营养不良的危险因素及预测模型分析
- Author:
Heping LI
1
;
Zhanmin DING
;
Xing ZHANG
;
Xuanxuan ZHOU
;
Shuya SONG
;
Peng LIU
;
Cuixia LAN
;
Ning WANG
Author Information
1. 郑州大学第一附属医院康复医学科，郑州 450000
- Publication Type:Journal Article
- Keywords:
Traumatic brain injury;
Malnutrition;
Prediction models;
Risk factors;
Dysphagia
- From:
Chinese Journal of Physical Medicine and Rehabilitation
2025;47(11):1011-1016
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the risk factors for malnutrition after a traumatic brain injury and to construct a model which usefully predicts that risk.Methods:This was a retrospective study of 374 patients with a craniocerebral injury for whom the relevant clinical data were available. Based on their nutritional status, they were stratified into a malnutrition group ( n=220) and a control group ( n=154). Univariate and multivariate logistic regressions were evaluated seeking to identify the independent risk factors associated with malnutrition, and a prediction model was constructed based on the results. The model′s discrimination ability and accuracy were assessed using a receiver operating characteristics (ROC) curve. Results:A total of 220 patients (58.8%) developed malnutrition. Multifactorial logistic regression analysis showed that the independent risk factors for malnutrition were: age ≥60 years, pulmonary infection, dysphagia, cognitive impairment, a GCS score ≤8, or a Barthel index ≤40. In the ROC curve analysis, the area under the curve quantifying the model′s ability to predict malnutrition was 0.924 (95% CI: 0.896, 0.951), with a sensitivity of 0.868 and a specificity of 0.857, indicating its good prediction performance. Conclusions:Age ≥60 years, pulmonary infection, dysphagia, cognitive impairment, a GCS score ≤8 or a Barthel index ≤40 are independent predictors of malnutrition after a traumatic brain injury. The prediction model constructed based on those risk factors has demonstrated useful predictive power for malnutrition.
