A Nomogram for Predicting Postoperative Pulmonary Complications after Cardiac Surgery with Cardiopulmonary Bypass: A Retrospective Study

Background Postoperative pulmonary complications (PPCs) are among the most common complications following cardiac surgery with cardiopulmonary bypass (CPB) and can adversely affect patient outcomes. This study aimed to identify risk factors for PPCs following cardiac surgery with CPB and to develop and validate a nomogram for individualized risk prediction. Methods This retrospective cohort study enrolled patients undergoing cardiac surgery with cardiopulmonary bypass. Participants were grouped based on occurrence of PPCs. Univariate analysis identified candidate predictors, and multivariable logistic regression determined independent risk factors. A nomogram was developed and internally validated using bootstrap resampling. Model performance was assessed via receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). Results A total of 502 patients were included, of whom 277 (55.18%) developed PPCs. Multivariable logistic regression analysis identified the following independent risk factors for PPCs: age (OR = 1.117, 95% CI: 1.083–1.152, P  < 0.001), preoperative atrial fibrillation (OR = 3.881, 95% CI: 1.846–8.159, P  < 0.001), underlying lung diseases (OR = 5.524, 95% CI: 2.218–13.76, P  < 0.001), ASA physical status (OR = 5.131, 95% CI: 1.601–16.441, P  = 0.006), duration of CPB (OR = 1.021, 95% CI: 1.011–1.032, P  < 0.001), and duration of intraoperative hypotension (OR = 1.006, 95% CI: 1.001–1.011, P  = 0.026). A nomogram model constructed based on these factors demonstrated excellent discriminative ability upon internal validation (adjusted area under the curve (AUC) = 0.879). Calibration curves and decision curve analysis confirmed its good calibration and clinical utility, respectively. Conclusions This study identified six independent predictors for PPCs and developed an individualized prediction nomogram. This tool may assist clinicians in identifying high-risk patients, thereby optimizing perioperative management strategies to prevent and reduce the incidence of PPCs.