重症脑卒中病人肠内营养不耐受风险的预警模型构建与评估

doi:10.3969/j.issn.1003-9198.2022.09.018

摘要/Abstract

摘要： 目的通过分析重症脑卒中病人肠内营养不耐受的影响因素,建立动态列线图预测模型并检验预测效果。方法回顾性选取2018年1月至2020年6月我院收治的282例重症脑卒中病人作为建模组,采用多因素Logistic回归分析影响肠内营养不耐受的危险因素并建立列线图预测模型,对模型预测效果进行内部与外部评价。结果 282例重症脑卒中病人中,喂养不耐受发生率为37.94%(107例)。根据Logistic回归分析结果,将年龄≥60岁(OR=1.965)、使用2种以上抗菌药物(OR=2.749)、使用益生菌(OR=0.321)、实施机械通气(OR=3.035)4个因素纳入R软件建立预测脑卒中病人肠内营养不耐受的列线图模型,内部及外部验证显示,建模组与验证组的ROC曲线下面积分别为0.794、0.764,校准曲线均趋近于理想曲线;建模组ROC曲线的最大Youden指数0.449所对应的风险预测值为0.322,预测临界值为200分。决策曲线分析(DCA)阈概率范围为0.10～0.85时,模型表现为正的净效益。结论基于重症脑卒中病人肠内营养不耐受的危险因素建立的列线图模型具有良好的区分度、一致性与临床实用性,对于模型总分≥200分的高危病人应给予高度关注,尽早实施预防性护理干预,以减少肠内营养不耐受的发生。

关键词: 脑卒中, 肠内营养不耐受, 影响因素, 预测模型

Abstract: Objective To identify the influencing factors of enteral nutrition intolerance in the patients with severe stroke, and to build a dynamic prediction model and to verify the prediction effect. Methods A total of 282 patients with severe stroke admitted to our hospital from January 2018 to June 2020 were enrolled in this study. Single factor analysis and multivariate Logistic regression analysis were used to screen the risk factors of enteral nutrition intolerance, and a nomogram prediction model was established based on the risk factors. Internal and external evaluations of the model's predictive effect were conducted. Results Feeding intolerance occurred in 107 of the 282 patients with severe stroke, with an incidence rate of 37.94%. Based on the results of Logistic regression analysis, four factors including aged ≥60 years old(OR=1.965), use of 2 or more antibacterial drugs (OR=2.749), use of probiotics (OR=0.321), and implementation of mechanical ventilation (OR=3.035) were included in the R software to establish a predictive nomogram model of enteral nutrition intolerance in stroke patients. Internal and external verification showed that the areas under the receiver operating characteristic curve of the model group and the verification group were 0.794 and 0.764 respectively, and the calibration curves were all close to the ideal curve. The model group's maximum Youden index was 0.449, and the corresponding risk predictive value was 0.322, and the predictive threshold value was 200. When the decision curve analysis (DCA) threshold probability ranged 0.10-0.85, the model showed a positive net benefit. Conclusions The nomogram model established based on the risk factors of enteral nutrition intolerance in the patients with severe stroke shows good discrimination, consistency and clinical applicability. High-risk patients with a total score of ≥200 should be given great attention, and preventive care intervention should be implemented as soon as possible to reduce the occurrence of enteral nutrition intolerance.

Key words: stroke, enteral nutrition intolerance, influencing factor, predictive model

中图分类号:

R743.3

孙晓岚, 李占肖, 于晓雯, 柴会荣. 重症脑卒中病人肠内营养不耐受风险的预警模型构建与评估[J]. 实用老年医学, 2022, 36(9): 942-947.

SUN Xiao-lan, LI Zhan-xiao, YU Xiao-wen, CHAI Hui-rong. Construction and evaluation of dynamic nomogram chart of the risk of enteral nutrition intolerance in patients with severe stroke[J]. Practical Geriatrics, 2022, 36(9): 942-947.

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