Application of a predictive model for postoperative pulmonary complications in patients with non-small cell lung...

In patients with non-small cell lung cancer (NSCLC), postoperative pulmonary complications (PPCs) significantly increase morbidity and healthcare costs. To improve upon models based solely on static variables, this study aimed to develop a preoperative nomogram integrating cardiopulmonary exercise testing (CPET) parameters for predicting PPCs. The primary objective of this study was to develop a nomogram for predicting PPCs in NSCLC patients using preoperative CPET parameters combined with clinical variables, and to validate its discriminatory power and predictive.

Data, including clinical and CPET results, were collected from patients who underwent CPET before video-assisted thoracic surgery (VATS) at the Department of Thoracic Surgery, Xuzhou Central Hospital between August 2019 and November 2023. Independent risk factors for PPCs were identified through univariate and multivariate stepwise logistic regressions, and a nomogram prediction model was constructed. The model's discriminatory power and accuracy were assessed using the concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and area under the curve (AUC) in the validation cohort.

Data from 607 patients were used to construct the nomogram, which included age, intraoperative blood loss, chronic obstructive pulmonary disease (COPD), peak oxygen uptake (VO₂ peak), and the minute ventilation/carbon dioxide production (VE/VCO₂) slope as predictive factors. The model demonstrated good discrimination and accuracy, with a C-index of 0.790 [95% confidence interval (95% CI): 0.743-0.853]. The calibration plot showed strong agreement between predicted and actual PPC probabilities. The ROC curve confirmed the model's acceptable discrimination ability [area under the curve (AUC): 0.790, 95% CI: 0.605-0.829] in internal validation.

The predictive model for PPCs in patients with NSCLC exhibits strong discrimination and accuracy. It offers valuable support for clinicians in making informed treatment decisions.