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Development and validation of a nomogram-based model for predicting postoperative pulmonary complications after
Ying Ji1, Jingjing Liu2, Tao Shan1
1Department of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, 210001 China.
Insights
A new nomogram accurately predicts postoperative pulmonary complications (PPCs) after coronary artery bypass grafting (CABG) with cardiopulmonary bypass (CPB). This tool aids clinicians in rapid, objective risk assessment for better patient management.
Area of Science:
- Cardiology
- Thoracic Surgery
- Medical Informatics
Background:
- Patients undergoing coronary artery bypass grafting (CABG) with cardiopulmonary bypass (CPB) face a high risk of postoperative pulmonary complications (PPCs).
- Accurate prediction of PPCs is crucial for effective perioperative management and patient outcomes.
Purpose of the Study:
- To develop and validate a clinical prediction model for PPCs in patients undergoing CABG with CPB.
- To create a user-friendly nomogram for rapid risk assessment.
Main Methods:
- A cohort of 849 patients was randomly assigned to training (n=594) and validation (n=255) sets.
- Least absolute shrinkage and selection operator (LASSO) regression identified predictive variables, integrated into a multivariable logistic regression model and a nomogram.
- Model performance was evaluated using discrimination (AUC), calibration (calibration curves, Emax, Eavg), and clinical utility (decision curve analysis).
Main Results:
- Five key predictors were identified: age, smoking history, diabetes mellitus, emergent surgery, and anesthesia duration.
- The model demonstrated excellent predictive performance with an AUC of 0.902 in the training set and 0.864 in the validation set.
- Calibration analysis confirmed excellent agreement between predicted and observed outcomes, with non-significant P-values from the unreliability test.
Conclusions:
- An original nomogram effectively predicts PPCs after CABG with CPB.
- The nomogram provides clinicians with an objective tool for preoperative risk evaluation and perioperative decision-making.
- This facilitates informed consent discussions and personalized patient management strategies.
Objective:
Patients undergoing coronary artery bypass grafting (CABG) with cardiopulmonary bypass (CPB) are at high risk of developing postoperative pulmonary complications (PPCs). This study aimed to develop and validate a clinical prediction model for these complications after CABG.
Methods:
In total, 849 patients were randomly divided into training (n=594) and validation (n=255) sets in a 7:3 ratio. We used least absolute shrinkage and selection operator (LASSO) regression to identify predictive variables, incorporated them into a multivariable logistic regression model, and developed a nomogram. Model performance was assessed through discrimination (receiver operating characteristic (ROC) curve analysis, area under the curve (AUC)), calibration (calibration curves, maximum calibration error (Emax), average calibration error (Eavg)), and clinical utility assessment (decision curve analysis).
Results:
Five predictive indicators were selected: age, smoking history, diabetes mellitus, emergent surgery, and anesthesia duration. The model demonstrated excellent predictive performance, with an AUC of 0.902 (0.859-0.945) for the training set and 0.864 (0.811-0.917) for the validation set. Calibration curve results showed non-significant P-values from the unreliability test (P = 0.861 for training set, P = 0.741 for validation set), indicating excellent calibration. Emax and Eavg values were 0.042 and 0.013 for the training set, and 0.046 and 0.009 for the validation set, respectively, showing a strong agreement between the predicted values and actual observations.
Conclusion:
An original nomogram accurately predicted PPCs after CABG with CPB, which enables clinicians to rapidly assess PPC risk for individual patients without complex calculations, providing objective, quantitative evidence for preoperative risk evaluation, informed consent discussions, and perioperative management.
Supplementary Information:
The online version contains supplementary material available at 10.1007/s12055-025-02011-9.
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