Construction and validation of a predictive model for postoperative respiratory failure in esophageal cancer patients.

IF 1.9 3区 医学 Q3 RESPIRATORY SYSTEM
Journal of thoracic disease Pub Date : 2025-07-31 Epub Date: 2025-07-15 DOI:10.21037/jtd-2024-2114
Bo Yang, Yue Bai, Lili Lang, Jijun Xue, Qun Cao, Yong Ao
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引用次数: 0

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

食管癌患者术后呼吸衰竭预测模型的构建与验证。
背景: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中PRF的危险因素,建立预测模型,并验证其性能。方法:回顾性分析2020年1月至2024年6月中山大学肿瘤中心甘肃医院手术治疗的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名在2024年1月至2024年6月期间在中山大学癌症中心接受手术的EC患者被用作外部验证集。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。结果:吸烟指数≥400、1秒用力呼气量(FEV1)、术前血清白蛋白水平、手术时间、术后吻合口瘘是EC患者发生PRF的危险因素。预测模型的曲线下面积(AUC)值为:训练集(0.856),内部验证集(0.839),外部验证集(0.773),说明模型具有较好的判别能力。校正曲线和Hosmer-Lemeshow检验表明该模型具有良好的预测精度,决策曲线分析(DCA)表明该模型具有相当的临床实用性。结论:使用LASSO-logistic回归建立的预测模型在内部和外部验证中都表现出很强的性能和临床适用性,具有帮助临床医生识别高危患者进行早期个体化干预的潜力。
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来源期刊
Journal of thoracic disease
Journal of thoracic disease RESPIRATORY SYSTEM-
CiteScore
4.60
自引率
4.00%
发文量
254
期刊介绍: The Journal of Thoracic Disease (JTD, J Thorac Dis, pISSN: 2072-1439; eISSN: 2077-6624) was founded in Dec 2009, and indexed in PubMed in Dec 2011 and Science Citation Index SCI in Feb 2013. It is published quarterly (Dec 2009- Dec 2011), bimonthly (Jan 2012 - Dec 2013), monthly (Jan. 2014-) and openly distributed worldwide. JTD received its impact factor of 2.365 for the year 2016. JTD publishes manuscripts that describe new findings and provide current, practical information on the diagnosis and treatment of conditions related to thoracic disease. All the submission and reviewing are conducted electronically so that rapid review is assured.
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