Yang Bo, Bai Yue, Lang Lili, Xue Jijun, Cao Qun, Ao Yong
Department of Thoracic Surgery, Sun Yat-sen University Cancer Center Gansu Hospital, Lanzhou, China.
Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.
J Thorac Dis. 2025 Jul 31;17(7):4978-4989. doi: 10.21037/jtd-2024-2114. Epub 2025 Jul 15.
Postoperative respiratory failure (PRF) is one of the most severe complications following esophageal cancer (EC) surgery, closely associated with high mortality and poor prognosis. Early diagnosis and intervention are crucial. This study aimed to explore the risk factors for PRF in EC, develop a predictive model, and validate its performance.
The clinical data of 265 EC patients who underwent surgery at the Sun Yat-sen University Cancer Center Gansu Hospital between January 2020 and June 2024 were retrospectively analyzed. The patients were randomly divided 7:3 into a training set (n=185) and an internal validation set (n=80). Another 80 EC patients who underwent surgery at the Sun Yat-sen University Cancer Center between January 2024 and June 2024 were employed as an external validation set. Feature selection was optimized using least absolute shrinkage and selection operator (LASSO)-logistic regression, and a predictive model was constructed and internally and externally validated.
Smoking index ≥400, forced expiratory volume in one second (FEV1), preoperative serum albumin level, surgical time, and postoperative anastomotic fistula were identified as risk factors for PRF in EC patients. The area under the curve (AUC) values of the predictive model were as follows: training set (0.856), internal validation set (0.839), and external validation set (0.773), indicating that the model had good discriminatory power. A calibration curve and Hosmer-Lemeshow test demonstrated that the model had favorable predictive accuracy and decision curve analysis (DCA) showed that the model had considerable clinical utility.
The predictive model developed using LASSO-logistic regression exhibited strong performance and clinical applicability in both internal and external validations, with the potential to assist clinicians in identifying high-risk patients for early individualized intervention.
术后呼吸衰竭（PRF）是食管癌（EC）手术后最严重的并发症之一，与高死亡率和不良预后密切相关。早期诊断和干预至关重要。本研究旨在探讨EC患者PRF的危险因素，建立预测模型，并验证其性能。
回顾性分析2020年1月至2024年6月在中山大学肿瘤防治中心甘肃医院接受手术的265例EC患者的临床资料。患者按7:3随机分为训练集（n = 185）和内部验证集（n = 80）。另外80例2024年1月至2024年6月在中山大学肿瘤防治中心接受手术的EC患者作为外部验证集。使用最小绝对收缩和选择算子（LASSO）-逻辑回归优化特征选择，构建预测模型并进行内部和外部验证。
吸烟指数≥400、一秒用力呼气容积（FEV1）、术前血清白蛋白水平、手术时间和术后吻合口瘘被确定为EC患者PRF的危险因素。预测模型的曲线下面积（AUC）值如下：训练集（0.856）、内部验证集（0.839）和外部验证集（0.773），表明该模型具有良好的区分能力。校准曲线和Hosmer-Lemeshow检验表明该模型具有良好的预测准确性，决策曲线分析（DCA）表明该模型具有相当的临床实用性。
使用LASSO-逻辑回归开发的预测模型在内部和外部验证中均表现出强大的性能和临床适用性，有可能帮助临床医生识别高危患者以便进行早期个体化干预。
