A Multiple-Center Nomogram to Predict Pneumonectomy Complication Risk for Non-Small Cell Lung Cancer Patients.

Abstract

OBJECTIVE: This study aimed to construct a nomogram to quantitatively predict pneumonectomy complication risks for non-small cell lung cancer (NSCLC) patients. METHODS: Data from 1052 NSCLC patients who underwent pneumonectomy were retrospectively retrieved from the databases of three thoracic centers. Multivariable logistic regression was used to investigate postoperative morbidity predictors. Clinical parameters and operative features were analyzed using univariable and multivariable logistic regression analyses, and a nomogram to predict the risk of postoperative complications was constructed using bootstrap resampling. A receiver operating characteristic (ROC) curve was used to estimate the discrimination power for the nomogram. RESULTS: A total of 212 patients (20.2%) had major complications. After regression analysis, forced expiratory volume in 1 s, Charlson Comorbidity Index score, male sex, and right-sided pneumonectomy were identified and entered into the nomogram. The nomogram showed a robust discrimination, with an area under the ROC curve of 0.753 (95% confidence interval 0.604-0.818). The calibration curves for the probability of postoperative complications showed optimal agreement between the nomogram and the actual probability. CONCLUSIONS: Based on preoperative data, we developed a nomogram for predicting complication risks after pneumonectomy. This model may be helpful for thoracic surgeons in selecting appropriate patients for adopting prophylactic measures after surgery.