Abstract
BACKGROUND: Postoperative pulmonary complications (PPCs) remain a prevalent concern among elderly patients undergoing surgery, with a notably higher incidence observed in elderly patients undergoing thoracic surgery. This study aimed to develop a nomogram to predict the risk of PPCs in this population. METHODS: A total of 2963 elderly patients who underwent thoracic surgery were enrolled and randomly divided into a training cohort (80%, n = 2369) or a validation cohort (20%, n = 593). Univariate and multivariate logistic regression analyses were conducted to identify risk factors for PPCs, and a nomogram was developed based on the findings from the training cohort. The validation cohort was used to validate the model. The predictive accuracy of the model was evaluated by receiver operating characteristic (ROC) curve, area under ROC (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: A total of 918 (31.0%) patients reported PPCs. Nine independent risk factors for PPCs were identified: preoperative presence of chronic obstructive pulmonary disease (COPD), elevated leukocyte count, higher partial pressure of arterial carbon dioxide (PaCO(2)) level, surgical site, thoracotomy, intraoperative hypotension, blood loss > 100 mL, surgery duration > 180 min, and malignant tumor. The AUC value for the training cohort was 0.739 (95% CI: 0.719-0.762), and it was 0.703 for the validation cohort (95% CI: 0.657-0.749). The P-values for the Hosmer-Lemeshow test were 0.633 and 0.144 for the training and validation cohorts, respectively, indicating a notable calibration curve fit. The DCA curve indicated that the nomogram could be applied clinically if the risk threshold was between 12% and 84%, which was found to be between 8% and 82% in the validation cohort. CONCLUSION: This study highlighted the pressing need for early detection of PPCs in elderly patients undergoing thoracic surgery. The nomogram exhibited promising predictive efficacy for PPCs in elderly patients undergoing thoracic surgery, enabling the identification of high-risk patients and consequently aiding in the implementation of preventive interventions.