Development and validation of a predictive model for delayed emergence in general anesthesia patients undergoing thoracoscopic radical lung cancer surgery
10.3760/cma.j.cn115682-20240615-03373
- VernacularTitle:胸腔镜肺癌根治术全身麻醉患者苏醒延迟预测模型的构建及验证
- Author:
Yingna SHI
1
;
Xuehua ZHU
;
Xiaoying XU
;
Lili SHEN
;
Sujuan YE
Author Information
1. 浙江大学医学院附属第一医院麻醉复苏室，杭州 310000
- Publication Type:Journal Article
- Keywords:
Lung neoplasm;
Delayed emergence;
Risk factors;
Predictive model;
Postanesthesia Care Unit
- From:
Chinese Journal of Modern Nursing
2025;31(18):2499-2507
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop and validate a predictive model for delayed emergence in patients undergoing thoracoscopic radical lung cancer surgery with general anesthesia.Methods:A total of 1 468 patients admitted to the anesthesia recovery room after thoracoscopic radical lung cancer surgery at the First Affiliated Hospital of Zhejiang University School of Medicine from August 2020 to August 2021 were selected via convenience sampling. Patients who underwent surgery between August 2020 and June 2021 ( n=1 213) were assigned to the modeling group, while those from July to August 2021 ( n=255) were used as the validation group. Logistic regression analysis was used to identify risk factors for delayed emergence and to establish a predictive model. The performance of the model was evaluated using the area under the receiver operating characteristic curve ( AUC) and the Hosmer-Lemeshow goodness-of-fit test. Results:Among the modeling group, 200 patients experienced delayed emergence, with an incidence of 16.49% (200/1 213). Logistic regression analysis revealed that the use of reversal agents, use of neostigmine, albumin level, presence of shivering, pain score≥4 points, extubation time, partial pressure of CO 2, partial pressure of oxygen, serum potassium level, and intraoperative fentanyl dosage were significant influencing factors ( P<0.05). The predictive model demonstrated good performance with an AUC of 0.864 [95% CI (0.828, 0.899) ], a Hosmer-Lemeshow test χ 2=5.299 ( P=0.725), cut-off value of 0.442, sensitivity of 0.794, and specificity of 0.769. In the validation group, delayed emergence occurred in 44 cases (17.25%). The model showed good validation performance with an AUC of 0.852 [95% CI (0.826, 0.878) ], Hosmer-Lemeshow χ 2=5.912 ( P=0.336), cut-off value of 0.754, sensitivity of 0.674, and specificity of 0.877. Conclusions:The predictive model constructed in this study demonstrates strong performance and can assist clinicians in the early identification of patients at risk of delayed emergence following thoracoscopic radical lung cancer surgery under general anesthesia.
