老年脑卒中肺部感染风险预测模型的建立及验证

doi:10.3969/j.issn.1003-9198.2024.05.005

摘要/Abstract

摘要： 目的分析老年脑卒中肺部感染的危险因素,建立预测老年脑卒中肺部感染风险的列线图模型。方法纳入2020年1月至2022年12月于我院收治的138例老年脑卒中病人为研究对象,根据是否发生肺部感染分为感染组和未感染组。应用单因素、多因素Logistic回归模型分析老年脑卒中肺部感染的危险因素,应用R软件建立预测老年脑卒中肺部感染的列线图模型。采用ROC曲线分析该模型预测老年脑卒中肺部感染的效能。结果 138例老年脑卒中病人中发生肺部感染32例(23.2%)。感染组年龄>70岁的比例,吸烟史、糖尿病、侵入操作、吞咽功能障碍、意识障碍比例高于未感染组(P<0.05)。Logistic回归分析显示年龄、吸烟史、糖尿病、侵入操作、吞咽功能障碍、意识障碍均是脑卒中病人发生肺部感染的独立影响因素(P<0.05)。Hosmer-Lemeshow拟合优度检验结果显示,基于Logistic回归分析结果构建的列线图模型的预测值与实际值差异无统计学意义(P>0.05)。ROC曲线显示,列线图模型预测肺部感染的AUC为0.860 (95%CI:0.796~0.925)。结论年龄、吸烟史、糖尿病、侵入操作、吞咽功能障碍、意识障碍是老年脑卒中病人发生肺部感染的独立危险因素,以上述指标建立的列线图模型具有良好的预测效能。

关键词: 脑卒中, 肺部感染, 列线图模型, 预测效能, 危险因素

Abstract: Objective To analyze the risk factors of pulmonary infection in the elderly patients with stroke, and to establish a Nomogram to predict the risk of pulmonary infection in the elderly patients with stroke. Methods A total of 138 elderly patients with stroke admitted to our hospital from January 2020 to December 2022 were enrolled in the study. All patients were divided into infection group and non-infection group according to whether they presented with pulmonary infection. Single factor and multiple factor Logistic regression models were used to analyze the risk factors of pulmonary infection in the elderly patients with stroke,and R software was used to establish a Nomogram to predict the incidence of pulmonary infection. Receiver operating characteristic (ROC) curve was used to analyze the effectiveness of the Nomogram in predicting the incidence of pulmonary infection in the elderly patients with stroke. Results There were 32 cases (23.2%) in the infection group and 106 cases (76.8%) in the non-infection group. The proportions of the cases aged more than 70 years old, smoking history, diabetes, invasive procedures, swallowing dysfunction and disturbance of consciousness in the infection group were higher than those in the non-infection group. Logistic regression analysis showed that age, smoking history, diabetes, invasive procedures, dysphagia and disturbance of consciousness were independent influencing factors for pulmonary infection in the elderly patients with stroke (P<0.05). Hosmer-Lemeshow goodness-of-fit test showed that the Nomogram model exhibited satisfactory concordance between predicted outcome and actual outcome (P>0.05). ROC curve analysis showed that the area under the curve of the Nomogram model in predicting lung infection was 0.860 (95% CI: 0.796-0.925). Conclusions Age, smoking history, diabetes, invasive procedures, dysphagia and disturbance of consciousness are independent risk factors for pulmonary infection in the elderly patients with stroke. The Nomogram model established with the above indicators shows good predictive efficacy.

Key words: stroke, lung infection, Nomogram model, predictive effectiveness, risk factors

中图分类号:

徐金燕, 夏聪聪, 杨红美. 老年脑卒中肺部感染风险预测模型的建立及验证[J]. 实用老年医学, 2024, 38(5): 452-437.

XU Jinyan, XIA Congcong, YANG Hongmei. Establishment and validation of a Nomogram to predict the risk of pulmonary infection in elderly patients with stroke[J]. Practical Geriatrics, 2024, 38(5): 452-437.

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