Predicting Successful Weaning from Mechanical Ventilation by Reduction in Positive End-expiratory Pressure Level Using Machine Learning

Weaning patients from mechanical ventilation (MV) is a critical and resource intensive process in the Intensive Care Unit (ICU) that impacts patient outcomes and healthcare expenses. Weaning methods vary widely among providers. Prolonged MV is associated with adverse events and higher healthcare expenses. Predicting weaning readiness is a non-trivial process in which the positive end-expiratory pressure (PEEP), a crucial component of MV, has potential to be indicative but has not yet been used as the target. We aimed to predict successful weaning from mechanical ventilation by targeting changes in the PEEP-level using a supervised machine learning model. This retrospective study included 12,153 mechanically ventilated patients from Medical Information Mart for Intensive Care (MIMIC-IV) and eICU collaborative research database (eICU-CRD). Two machine learning models (Extreme Gradient Boosting and Logistic Regression) were developed using a continuous PEEP reduction as target. The data is splitted into 80% as training set and 20% as test set. The model’s predictive performance was reported using 95% confidence interval (CI), based on evaluation metrics such as area under the receiver operating characteristic (AUROC), area under the precision-recall curve (AUPRC), F1-Score, Recall, positive predictive value (PPV), and negative predictive value (NPV). The model’s descriptive performance was reported as the variable ranking using SHAP (SHapley Additive exPlanations) algorithm. The best model achieved an AUROC of 0.84 (95% CI 0.83–0.85) and an AUPRC of 0.69 (95% CI 0.67–0.70) in predicting successful weaning based on the PEEP reduction. The model demonstrated a Recall of 0.85 (95% CI 0.84–0.86), F1-score of 0.86 (95% CI 0.85–0.87), PPV of 0.87 (95% CI 0.86–0.88), and NPV of 0.64 (95% CI 0.63–0.66). Most of the variables that SHAP algorithm ranked to be important correspond with clinical intuition, such as duration of MV, oxygen saturation (SaO2), PEEP, and Glasgow Coma Score (GCS) components. This study demonstrates the potential application of machine learning in predicting successful weaning from MV based on continuous PEEP reduction. The model’s high PPV and moderate NPV suggest that it could be a useful tool to assist clinicians in making decisions regarding ventilator management.Author summary: The weaning of patients from mechanical ventilation (MV) in the intensive care unit is crucial for patient outcomes and healthcare costs. This retrospective study explores the application of machine learning to predict successful weaning from MV, focusing on the positive end-expiratory pressure (PEEP), a key component of MV, as a potential predictor for successful weaning. Analyzing data of 12,153 patients from eICU-CRD and MIMIC-IV, we developed models using Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR), targeting PEEP reduction. Our best model showed an AUROC of 0.84, an AUPRC of 0.69, and practical utility in predicting weaning success, validated by metrics such as Recall, F1-score, PPV, and NPV. Important variables identified by the SHAP algorithm, such as duration of MV and oxygen saturation aligned with clinical intuition. These findings highlight the potential of machine learning in enhancing ICU ventilator management, contributing to improved patient care and resource utilization efficiency.