Development and validation of a nomogram to predict postoperative pulmonary complications following thoracoscopic surgery

Background. Postoperative pulmonary complications (PPCs) after thoracoscopic surgery are common. This retrospective study aimed to develop a nomogram to predict PPCs in thoracoscopic surgery. Methods. A total of 905 patients who underwent thoracoscopy were randomly enrolled and divided into a training cohort and a validation cohort at 80%:20%. The training cohort was used to develop a nomogram model, and the validation cohort was used to validate the model. Univariate and multivariable logistic regression were applied to screen risk factors for PPCs, and the nomogram was incorporated in the training cohort. The discriminative ability and calibration of the nomogram for predicting PPCs were assessed using C-indices and calibration plots. Results. Among the patients, 207 (22.87%) presented PPCs, including 166 cases in the training cohort and 41 cases in the validation cohort. Using backward stepwise selection of clinically important variables with the Akaike information criterion (AIC) in the training cohort, the following seven variables were incorporated for predicting PPCs: American Society of Anesthesiologists (ASA) grade III/IV, operation time longer than 180 min, one-lung ventilation time longer than 60 min, and history of stroke, heart disease, chronic obstructive pulmonary disease (COPD) and smoking. With incorporation of these factors, the nomogram achieved good C-indices of 0.894 (95% confidence interval (CI) [0.866-0.921]) and 0.868 (95% CI [0.811-0.925]) in the training and validation cohorts, respectively, with well-fitted calibration curves. Conclusion. The nomogram offers good predictive performance for PPCs after thoracoscopic surgery. This model may help distinguish the risk of PPCs and make reasonable treatment choices.

Automated summary

A nomogram model was developed to predict postoperative pulmonary complications (PPCs) in thoracoscopic surgery, using data from 905 patients. The model incorporated seven key variables related to PPCs and showed good predictive performance in distinguishing the risk of PPCs, aiding in making reasonable treatment choices. With high C-indices and well-fitted calibration curves, the nomogram offers a reliable tool for assessing and managing PPCs in thoracoscopic surgery.