Influencing factors and prediction model construction of intraoperative hypoxemia in patients with benign central airway stenosis
10.3760/cma.j.cn211501-20240722-01953
- VernacularTitle:良性中心气道狭窄患者术中低氧血症的影响因素及预测模型构建与验证
- Author:
Lihua MENG
1
;
Ying XIA
1
;
Shan LI
1
;
Chong BAI
1
;
Haidong HUANG
1
;
Qin WANG
1
Author Information
1. 中国人民解放军海军军医大学第一附属医院呼吸与危重症医学科，上海 200433
- Publication Type:Journal Article
- Keywords:
Hyoxemia;
Benign central airway stenosis;
Machine learning algorithm;
Prediction model
- From:
Chinese Journal of Practical Nursing
2025;41(24):1890-1897
- CountryChina
- Language:Chinese
-
Abstract:
Objective:The influencing factors of intraoperative hypoxemia in patients with benign central airway stenosis were investigated by machine learning algorithm, and the prediction model of hypoxemia was constructed and verified.Methods:A case-control study was used in this study. The clinical data of 650 patients with benign central airway stenosis who who received surgical treatment in the First Affiliated Hospital of PLA Naval Medical University from June 2022 to April 2024 were retrospectively analyzed. And they were divided into a training set ( n=455) and a test set ( n=195) according to 7:3. The training set was used for establishing Logistic regression model and conducting internal verification, and the test set was used for external verification. The least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were used to select the factors affecting intraoperative hypoxemia in patients with benign central airway stenosis. A Logistic regression prediction model was constructed, and the model was evaluated using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA) and calibration curve. Shapley additive interpretation (SHAP) were used to analyze the importance of influencing factors. Results:Among 650 patients, 279 were males and 371 were females, aged (37.86 ± 8.82) years. Nine feature variables were screened by LASSO regression, while 7 feature variables were screened by Boruta algorithm, the intersection of the two was operation time, complications, degree of airway stenosis, thermal ablation therapy, balloon dilation, and airway stent, respectively, based on this, a logistic regression prediction model was constructed.The AUC values of the training set, validation set and test set of the model were 0.928 (95% CI 0.903-0.954), 0.922 (95% CI 0.843-0.995) and 0.919 (95% CI 0.872-0.965), respectively. The calibration curve showed that the predicted results of the model were in good agreement with the actual results, and the DCA curve showed that the model had clinical application value. SHAP analysis showed that the importance of variables affecting intraoperative hypoxemia in benign central airway stenosis patients was ranked as operation time, thermal ablation therapy, degree of airway stenosis, comorbidification, balloon dilation, and airway stent. Conclusions:The Logistic regression prediction model of intraoperative hypoxemia built based on machine learning algorithm has good prediction efficiency, which is helpful to early identification of risk groups and prevention of hypoxemia.
