关键词: 食管肿瘤, 呼吸功能不全, 比例危险度模型, SMOTE算法

Abstract:

Objective To construct a prediction model for postoperative acute respiratory failure （ARF） risk in patients with esophageal cancer based on the synthetic minority oversampling technique （SMOTE） algorithm. Methods A retrospective analysis was conducted on the clinical data of 450 patients who underwent surgical treatment for esophageal cancer in Jiangsu Province Hospital from March 2023 to March 2025. Based on the occurrence of ARF after surgery， the patients were divided into an ARF group （45 cases） and a non-ARF group （405 cases）. The clinical data were compared between patients in the two groups， and multivariate logistic regression analysis was used to identify influencing factors for ARF in patients with esophageal cancer after surgery. A logistic regression prediction model was constructed. A prediction model for the occurrence of ARF in patients with esophageal cancer after surgery was constructed using the SMOTE algorithm. Model calibration was assessed using Cox-Snell R². The predictive efficacy was evaluated using the receiver operator characteristic （ROC） curve. Results There were statistically significant differences in smoking history， surgery duration， anastomotic leakage， postoperative sputum obstruction， postoperative pneumonia， and postoperative mechanical ventilation duration between the ARF group and the non-ARF group （all P<0.05）. Multivariate analysis showed that smoking history （OR=3.57， 95%CI： 1.60-7.97， P=0.002）， surgery duration >4 h （OR=2.89， 95%CI： 1.49-5.98， P=0.002）， anastomotic leakage （OR=3.09， 95%CI： 1.04-9.17， P=0.042）， postoperative sputum obstruction （OR=2.69， 95%CI： 1.34-5.41， P=0.005）， postoperative pneumonia （OR=2.61， 95%CI： 1.24-5.50， P=0.011）， and postoperative mechanical ventilation duration >48 h （OR=4.26， 95%CI： 1.68-10.80， P=0.002） were independent risk factors for ARF in patients with esophageal cancer after surgery. The prediction model based on the SMOTE algorithm was logit（P）=-4.74+2.90×smoking history+2.52×surgery duration+1.69×anastomotic leakage+1.51×postoperative sputum obstruction+1.49×postoperative pneumonia+1.88×postoperative mechanical ventilation duration. The prediction model based on the SMOTE algorithm demonstrated good calibration （Cox-Snell R²=0.537； χ²=118.34， df=6， P<0.001）. The ROC curve analysis showed that the area under the curve of the prediction model based on the SMOTE algorithm was 0.835 （95%CI： 0.803-0.866）， which was higher than that of the logistic regression prediction model （the area under the curve was 0.783， 95%CI： 0.712-0.854； Z=2.35， P=0.019）. Conclusions The risk prediction model for ARF after esophageal cancer surgery based on the SMOTE algorithm exhibits excellent predictive efficacy， supporting its potential utility in identifying patients with high risk of ARF after esophageal cancer surgery.

Key words: Esophageal neoplasms, Respiratory insufficiency, Proportional hazards models, SMOTE algorithm