A deep neural network model integrating preoperative and intraoperative variables accurately predicted postoperative respiratory failure following cardiac surgery with an AUROC of 0.855.
Cohort (n=1,016)
No
A deep learning model integrating perioperative variables accurately predicts postoperative respiratory failure after cardiac surgery and provides individualized risk interpretation.
Effect estimate: AUROC 0.855 (95% CI 0.796-0.906)
Postoperative respiratory failure following cardiac surgery (CS-PRF) remains a critical complication with substantial morbidity and mortality. Current risk prediction models are limited by static assessments and suboptimal accuracy. This study aimed to develop and validate a dynamic, machine learning–based model to enhance perioperative risk stratification for CS-PRF. We retrospectively analyzed 1,016 adult patients who underwent cardiac surgery. Feature selection was conducted via the Least Absolute Shrinkage and Selection Operator (LASSO) and Boruta algorithms. Five machine learning models, including logistic regression, multilayer perceptron, extreme gradient boosting, categorical boosting, and deep neural network (DNN), were trained using preoperative and intraoperative variables. Model performance was evaluated by the area under the receiver operating characteristic curve (AUROC), area under the precision–recall curve (AUPRC), and calibration metrics. Model interpretability was evaluated via SHapley additive exPlanation (SHAP), and restricted cubic spline (RCS) analyses were used to explore nonlinear associations. The incidence of CS-PRF was 16.3%. In the validation cohort, the DNN model achieved superior performance, with an AUROC of 0.782 (95% CI 0.703–0.852) and an AUPRC of 0.496 based on preoperative variables, which improved to an AUROC of 0.855 (95% CI 0.796–0.906) and an AUPRC of 0.549 with the addition of intraoperative data. Calibration analysis demonstrated good agreement between predicted and observed risk. SHAP analysis of the preoperative model identified pulmonary artery pressure, age, and preoperative creatinine as key contributors. In the combined model, intraoperative features such as cardiopulmonary bypass duration and autologous blood transfusion volume emerged as additional important predictors. RCS analysis revealed a nonlinear association between age and CS-PRF. A web-based risk calculator integrating DNN predictions and individualized SHAP interpretation was deployed to support clinical decision-making. We developed a deep learning model that integrates perioperative variables to predict postoperative respiratory failure following cardiac surgery. Demonstrating high accuracy and interpretability, the model has been deployed as an accessible web-based calculator, offering a practical tool for personalized perioperative risk assessment. Not applicable. This study is a retrospective observational study and was not registered as a clinical trial.
Xu et al. (Tue,) conducted a cohort in Postoperative respiratory failure following cardiac surgery (n=1,016). Deep neural network (DNN) model vs. Traditional logistic regression model was evaluated on Prediction of postoperative respiratory failure (AUROC) (AUROC 0.855, 95% CI 0.796-0.906). A deep neural network model integrating preoperative and intraoperative variables accurately predicted postoperative respiratory failure following cardiac surgery with an AUROC of 0.855.