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

Abstract

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

CLC Number:

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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Establishment and validation of a Nomogram to predict the risk of pulmonary infection in elderly patients with stroke

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