Objective
The primary aim of this study was to develop and validate a machine learning prediction model for respiratory deterioration in mechanically ventilated Intensive Care Unit (ICU) patients. The secondary aim was to identify physiological parameters associated with respiratory failure during mechanical ventilation.

Methods
Two distinct prediction models were developed using data from ICU patients admitted to the Leiden University Medical Centre (LUMC) between 2018 and 2023. Patients receiving invasive mechanical ventilation (IMV) for at least 48 hours with a PaO2/FiO2 ratio below 40 kPa were included and allocated to COVID training, COVID test, or non-COVID test sets. Model 1 predicts respiratory deterioration within six hours after switching from controlled to assisted ventilation. Model 2 is an hourly updating model predicting respiratory deterioration occurring more than six hours after this switch. XGBoost models were cross-validated on the COVID training set to identify the optimal observation windows and prediction horizons, after which feature selection and hyperparameter optimisation were performed. Model 1 was optimised for the area under the receiver operating characteristic (AUROC) and Model 2 for the area under the precision-recall curve (AUPRC). Discriminative performance, generalisability, and clinical utility were evaluated on the COVID and non-COVID test sets.

Results
A total of 296 patients were included in the COVID training set, 78 in the COVID test set, and 755 to the non-COVID test set. For Model 1, a one-hour observation window was selected. The most important features were the mean fraction of inspired oxygen (FiO2), propofol infusion rate, and peripheral oxygen saturation (SpO2). This model achieved an AUROC of 0.78 on the COVID test and 0.76 on the non-COVID test set. For model 2, a two-hour observation window and a six-hour prediction horizon were selected, with the SpO2/FiO2 ratio as the most important input feature. This model achieved an AUPRC of 0.05 on the COVID test set and 0.03 on the non-COVID test set.

Conclusion
Model 1 demonstrated moderate discriminative performance but limited clinical utility at relevant operating points. Model 2 showed very limited predictive value, primarily due to extreme class imbalance. Consequently, neither model is currently suitable for clinical implementation. With larger datasets and more advanced modelling techniques, Model 1 may have the potential to become a clinically useful decision support tool to support decisions on switching from controlled to assisted ventilation.