Lan Jingling, Lin Xia, Liu Li
Department of Cardiothoracic Surgery, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui City, Zhejiang Province, 323000, People's Republic of China.
Ther Clin Risk Manag. 2024 Oct 1;20:701-709. doi: 10.2147/TCRM.S464307. eCollection 2024.
This study aims to explore the influencing factors of cough after pulmonary resection (CAP) after thoracoscopic lung resection in lung cancer patients and to develop a predictive model.
A total of 374 lung cancer patients who underwent lung resection in our hospital from March 2020 to October 2023 were randomly divided into a modeling group (n=262) and a validation group (n=112). Based on the occurrence of CAP in the modeling group, the patients were divided into a CAP group (n=85) and a non-CAP group (n=177). Multivariate Logistic regression analysis was used to identify the influencing factors of CAP in lung cancer patients. A nomogram model for predicting the risk of CAP was constructed using R4.3.1. The consistency of the model's predictions was evaluated, and a clinical decision curve (DCA) was drawn to assess the clinical utility of the nomogram. The predictive performance of the model was evaluated using ROC curves and the Hosmer-Lemeshow test.
Multivariate Logistic regression analysis showed that smoking history (OR=6.285, 95% CI: 3.031-13.036), preoperative respiratory function training (OR=20.293, 95% CI: 7.518-54.779), surgical scope (OR=20.667, 95% CI: 7.734-55.228), and peribronchial lymph node dissection (OR=5.883, 95% CI: 2.829-12.235) were significant influencing factors of CAP in lung cancer patients (P<0.05). ROC curves indicated good discriminatory power of the model, and the Hosmer-Lemeshow test showed a high degree of agreement between predicted and actual probabilities. The DCA curve revealed that the nomogram model had high clinical value when the high-risk threshold was between 0.08 and 0.98.
The nomogram model based on smoking history, preoperative respiratory function training, surgical scope, and peribronchial lymph node dissection has high predictive performance for CAP in lung cancer patients. It is useful for clinical prediction, guiding preoperative preparation, and postoperative care.
本研究旨在探讨肺癌患者胸腔镜肺切除术后肺切除后咳嗽（CAP）的影响因素，并建立预测模型。
选取2020年3月至2023年10月在我院行肺切除的374例肺癌患者，随机分为建模组（n = 262）和验证组（n = 112）。根据建模组中CAP的发生情况，将患者分为CAP组（n = 85）和非CAP组（n = 177）。采用多因素Logistic回归分析确定肺癌患者CAP的影响因素。使用R4.3.1构建预测CAP风险的列线图模型。评估模型预测的一致性，并绘制临床决策曲线（DCA）以评估列线图的临床实用性。使用ROC曲线和Hosmer-Lemeshow检验评估模型的预测性能。
多因素Logistic回归分析显示，吸烟史（OR = 6.285，95%CI：3.031 - 13.036）、术前呼吸功能训练（OR = 二十点二九三，95%CI：7.518 - 54.779）、手术范围（OR = 二十点六六七，95%CI：7.734 - 55.228）和支气管周围淋巴结清扫（OR = 5.883，95%CI：2.829 - 12.235）是肺癌患者CAP的显著影响因素（P < 0.05）。ROC曲线表明模型具有良好的区分能力，Hosmer-Lemeshow检验显示预测概率与实际概率高度一致。DCA曲线显示，当高风险阈值在0.08至0.98之间时，列线图模型具有较高的临床价值。
基于吸烟史、术前呼吸功能训练、手术范围和支气管周围淋巴结清扫的列线图模型对肺癌患者的CAP具有较高的预测性能。它有助于临床预测、指导术前准备和术后护理。
