Explainable machine learning for postoperative respiratory failure prediction in open-heart surgery patients - a...

Postoperative respiratory failure (PRF) is a severe complication after open-heart surgery, associated with increased mortality and prolonged ICU stays. While machine learning (ML) models have shown promise in predicting PRF, existing models often rely on fragmented data and lack interpretability. This study aimed to develop an interpretable ML model for early prediction of PRF using data from the first 24 h of ICU admission.

We analyzed data from the MIMIC-IV database, focusing on patients undergoing open-heart surgery with cardiopulmonary bypass (CPB). Patients with preoperative respiratory failure or significant missing data were excluded. Missing values (< 30%) were imputed using Predictive Mean Matching. Twelve features were selected through LASSO regression. We compared the performance of eight ML models using AUROC, AUPRC, and other metrics. The optimal model was further interpreted using Shapley Additive exPlanations (SHAP).

Of the 4,488 patients, 339 (7.6%) developed PRF. The Gradient Boosting Machine (GBM) model demonstrated the best performance with an AUROC of 0.808, AUPRC of 0.369, and Youden's index of 0.479, indicating balanced sensitivity (0.703) and specificity (0.776). SHAP analysis revealed that key predictors included minimum ionized calcium levels, vasopressor score, and central venous oxygen saturation (ScvO₂), with their impact varying across patient risk categories.

The GBM model, selected for its balanced performance across discrimination, calibration, and validation stability, provides a promising tool for early PRF risk stratification. The use of SHAP analysis enhances the interpretability of the model, highlighting the role of hemodynamic and metabolic markers in predicting PRF, thus improving clinical understanding and decision-making.