Analysis of Influencing Factors and Construction of Predictive Model for Persistent Cough After Lung Cancer Resection Under Thoracoscopy.

Abstract

Objective: 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.

Methods: 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.

Results: 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.

Conclusion: 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.