Predicting extubation failure in patients with cardiorespiratory decompensation: development and validation of an interpretable machine learning-based risk calculator.

Objective: This study aimed to develop an interpretable machine learning model for predicting extubation failure in mechanically ventilated patients with combined respiratory failure and cardiac dysfunction. Methods: A retrospective cohort of 350 patients receiving invasive mechanical ventilation between January 2022 and January 2024 was analyzed. After rigorous data preprocessing ≤5% missing values were imputed using mean, median, or mode based on distribution type. The dataset was partitioned into training ( Results: The ISGA-optimized AutoML framework (converging to LightGBM as the optimal algorithm) demonstrated superior performance on the independent test set, achieving a ROC-AUC of 0.9289 and a PR-AUC of 0.8892. Decision curve analysis confirmed clinical utility across threshold probabilities of 0.3-0.7, outperforming treat-all and treat-none strategies, and the calibration curve yielded a low Brier score of 0.114. Key predictors identified included brain Natriuretic Peptide (BNP), lactate, left ventricular ejection fraction (LVEF), troponin, and central venous pressure (CVP). Conclusion: The interpretable prediction tool accurately stratifies extubation failure risk using clinically available parameters, offering a prototype tool for personalized ventilation management.