Development and validation of a nomogram model for predicting the risk of ventilator-associated pneumonia in patients with mechanical ventilation
10.3760/cma.j.issn.1671-0282.2025.01.008
- VernacularTitle:机械通气患者呼吸机相关性肺炎风险预测列线图模型的构建及验证
- Author:
Jiaying LI
1
;
Guifang LI
;
Ziqing LIU
;
Hongxiao YANG
;
Jincong WANG
;
Xingyu YANG
;
Qiuyan YANG
;
Yao BIAN
;
Rong MA
Author Information
1. 宁夏医科大学护理学院，银川 750004
- Keywords:
Mechanical ventilation;
Ventilator-associated pneumonia;
Risk prediction model;
Predictors;
Nomogram
- From:
Chinese Journal of Emergency Medicine
2025;34(1):47-54
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a nomogram model for predicting the risk of ventilator-associated pneumonia (VAP) in patients with mechanical ventilation (MV) and to validate the stability of the prediction performance of the model.Methods:The patients with MV admitted to the Department of Critical Care Medicine of General Hospital of Ningxia Medical University from January 2019 to December 2022 were retrospectively selected according to the order of admission. The patients with MV were divided into the non-VAP group and the VAP group according to whether VAP occurred. The clinical data of the two groups, including general information, disease, medication, condition, and operation-related indicators were collected as candidate predictors of the model for comparison. Multivariate logistic stepwise forward regression analysis was used to screen the predictors that finally entered the model, and a nomogram model was constructed. The model discrimination was evaluated by the area under the receiver operating characteristic curve (AUC), the diagnostic test results of the model at the predicted threshold were calculated, the Hosmer-Lemeshow test was used to evaluate the model fit, and the Bootstrap resampling was used 1 000 times for internal validation, and model calibration and clinical applicability were evaluated by calibration curve and decision analysis curve, respectively.Results:A total of 1 250 patients with MV were included, including 1 102 patients in the non-VAP group and 148 patients in the VAP group, and the prevalence of VAP was 11.8%. The detection of multidrug-resistant organisms, chronic kidney disease, brain injury, oxygenation index, the place of tracheal intubation, reintubation, use of bronchoscopy, use of antibiotics, and MV duration were model predictors of VAP. The AUC of the nomogram model was 0.917 (95% CI: 0.895-0.939), the maximum Youden index of 0.697 corresponded to a prediction threshold of 0.096. The model accuracy, sensitivity and specificity were 0.836, 0.865, and 0.832, respectively. The positive predictive value and the negative predictive value were 0.409 and 0.979, respectively. The Hosmer- Lemeshow test indicated that the model fit well ( P=0.938). The results of the internal validation of the model showed that the predicted risk of the calibration curve was generally consistent with the actual risk, and the decision threshold probability of the decision analysis curve ranged from 2% to 90%. Conclusions:The nomogram model developed in this study is simple, convenient and has relatively stable prediction performance, which can be externally validated to evaluate the extrapolation of the model, and provide a basis for individualized clinical prediction of the risk of VAP in patients with MV.
