■中风患者的预后受到营养不良的极大威胁。然而,没有模型来预测住院卒中患者营养不良的风险。这项研究开发了一种预测模型,用于识别中风患者的高危营养不良。
■选择两家三级医院的脑卒中患者作为研究对象。采用二元logistic回归建立模型。使用各种指标评估模型的性能,包括接收器工作特性曲线,Hosmer-Lemeshow测试,灵敏度,特异性,尤登指数,临床决策曲线,和风险分层。
■总共319名中风患者被纳入研究。其中,27%的人在医院中营养不良。预测模型包括所有独立变量,包括吞咽困难,肺炎,肠内营养,BarthelIndex,上臂周长,和小腿围(均p<0.05)。建模组的AUC面积为0.885,验证组,是0.797。当风险阈值概率在0%到80%之间时,预测模型产生更大的净临床效益,如临床决策曲线所示。Hosmer检验的所有p值均>0.05。该模型的最佳截断值为0.269,灵敏度为0.849,特异性为0.804。风险分层后,在建模组和验证组,MRS评分和营养不良发生率均随着风险水平的升高而显著增加(p<0.05).
■这项研究开发了一种中风患者营养不良的预测模型。实践证明,该模型具有良好的微分和标定性。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: This study developed a prediction model for malnutrition in stroke patients. It has been proven that the model has good differentiation and calibration.