预测认知障碍营养风险的Nomogram。

IF 2.8 Q2 NEUROSCIENCES
Journal of Alzheimer's disease reports Pub Date : 2025-01-13 eCollection Date: 2025-01-01 DOI:10.1177/25424823241309262
Yuhang Chen, Junlin Diao, Xuezhuang Ren, Chunxiang Wei, Xue Zhou
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引用次数: 0

摘要

背景:认知障碍患者易发生营养不良,营养不良进一步促进认知能力下降。认知障碍患者无法准确回答营养筛查量表中的主观问题。因此,建立一种采用客观评价指标的营养风险预测模型来评价认知功能障碍患者住院期间的营养状况至关重要。目的:建立一种预测认知功能障碍患者营养风险的形态图。方法:采用最小绝对收缩选择算子(LASSO)进行回归分析,通过10倍交叉验证选择预测因子。然后,对选取的预测因素进行多变量logistic回归分析,得到最终的临床预测模型。通过对接收机工作特性曲线、标定曲线和决策曲线的分析,对模型的性能进行了评价。通过内部验证进行进一步评估。结果:通过LASSO从20个变量中筛选出6个预测因素,包括体重指数、年龄、甘油三酯、服用认知改善药物、控制营养状况、老年营养风险指数。训练组受试者工作特征曲线下面积为0.91,验证组受试者工作特征曲线下面积为0.88,说明6个预测因子构建的模型具有中等预测能力。决策曲线分析显示,两组的阈值范围均为0.00-0.80,其中培训组净效益最高,为0.76,验证组净效益最高,为0.77。结论:引入6个预测因素,风险nomogram可用于预测认知障碍患者的营养风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nomogram for predicting nutritional risk of cognitive impairment.

Background: Cognitive impairment patients are prone to malnutrition, which further promotes cognitive decline. Cognitive impairment patients are unable to accurately answer subjective questions in the nutrition screening scale. Therefore, it is crucial to establish a nutritional risk prediction model using objective evaluation indicators to evaluate the nutritional status of cognitive impairment patients during hospitalization.

Objective: To develop a nomogram for prediction of the nutritional risk in cognitive impairment patients.

Methods: The least absolute shrinkage and selection operator (LASSO) was used for regression analysis, and predictive factors were selected based on 10-fold cross validation. Then, using the selected predictive factors, multivariable logistic regression analysis was performed to obtain the final clinical prediction model. Moreover, the performance of the model was evaluated from receiver operating characteristic curve, calibration curve, and decision curve analysis. Further assessment was conducted through internal validation.

Results: Six predictive factors were selected from 20 variables through LASSO, including body mass index, age, triglyceride, taking cognitive-improving drugs, controlling nutritional status, and geriatric nutritional risk index. The area under the receiver operating characteristic curve of the training cohort was 0.91, while the validation cohort was 0.88, indicating that the model constructed with 6 predictors had moderate predictive ability. The decision curve analysis showed that the threshold range for both groups was 0.00-0.80, with the highest net benefit 0.76 for training cohort, while 0.77 for validation cohort.

Conclusions: Introducing six predictive factors, the risk nomogram is useful for predicting nutritional risk of cognitive impairment.

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