Chen Binlie, Ke Weiqi, Li Meizhen
Department of Gastrointestinal Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Department of Anesthesiology, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.
Front Med (Lausanne). 2025 Jun 11;12:1553163. doi: 10.3389/fmed.2025.1553163. eCollection 2025.
Pneumonia is a common complication following esophagectomy, which is related with an increased risk of mortality and hospitalization. This condition not only prolongs hospital stays but also raises healthcare costs. The aim of this study was to identify risk variables and develop a nomogram for predicting postoperative pneumonia (PP).
A total of 647 individuals who had esophageal cancer surgery between January 1, 2010, and December 31, 2020, were involved in this study. We used least absolute shrinkage and selection operator (LASSO) regression for screening the optimal predictive factors and subsequently developed a nomogram using the selected factors. Verification through the use of 500 bootstrap resampling techniques. To assess the nomogram's discriminating power, we used the calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).
According to the standard error criteria of non-zero coefficients of LASSO and multivariate logistic regression analyses, age, smoking, double-lumen endotracheal tube (DLET), combined intravenous and inhalation anesthesia (CIIA), and vasoactive drugs usage are independent risk indicators of PP. Based on these five predictors we created a nomogram. The area under the of nomogram for the ROC curve was 0.665 (95% CI: 0.620-0.704) in development and 0.691 (95%CI: 0.654-0.726) in 500 bootstraps resample validation. Additionally, the calibration curves showed a high degree of agreement between the actual and predicted probabilities. DCA displayed that the predictive model had a net benefit when the risk thresholds were 0.17-0.61.
This study developed an intuitive nomogram model to predict postoperative pneumonia in esophageal cancer patients based on age, smoking history, DLET, CIIA, and vasoactive medication usage. Proper anesthesia, ETT type, smoking cessation, and timely vasoactive medication use can lower risks. Further external validation and large-scale studies are needed.
肺炎是食管癌切除术后常见的并发症，与死亡率和住院风险增加相关。这种情况不仅延长住院时间，还会增加医疗费用。本研究的目的是识别风险变量并建立一个预测术后肺炎（PP）的列线图。
本研究纳入了2010年1月1日至2020年12月31日期间接受食管癌手术的647例患者。我们使用最小绝对收缩和选择算子（LASSO）回归筛选最佳预测因素，随后使用所选因素建立列线图。通过500次自助重采样技术进行验证。为评估列线图的鉴别能力，我们使用了校准图、受试者操作特征（ROC）曲线和决策曲线分析（DCA）。
根据LASSO非零系数的标准误差标准以及多因素逻辑回归分析，年龄、吸烟、双腔气管导管（DLET）、静吸复合麻醉（CIIA）和血管活性药物的使用是PP的独立风险指标。基于这五个预测因素，我们创建了一个列线图。在开发阶段，列线图的ROC曲线下面积为0.665（95%CI：0.620 - 0.704），在500次自助重采样验证中为0.691（95%CI：0.654 - 0.726）。此外，校准曲线显示实际概率与预测概率之间具有高度一致性。DCA显示，当风险阈值为0.17 - 0.61时，预测模型具有净效益。
本研究基于年龄、吸烟史、DLET、CIIA和血管活性药物的使用，开发了一种直观的列线图模型来预测食管癌患者术后肺炎。适当的麻醉、气管导管类型、戒烟和及时使用血管活性药物可降低风险。需要进一步的外部验证和大规模研究。
