Machine learning-based prediction of ventilator therapeutic pressure for optimized CPAP titration

Accurate prediction of therapeutic pressure for Continuous Positive Airway Pressure (CPAP) therapy is essential for effective treatment of Obstructive Sleep Apnea (OSA). Existing methods often rely on complex sleep-related parameters and small sample sizes, limiting their generalizability. This study aims to develop a more accessible, data-driven model using readily available demographic and physiological variables to predict CPAP pressure, improving both accuracy and scalability.

We employed a machine learning approach, integrating decision trees, gradient boosting algorithms (LightGBM, XGBoost, CatBoost), and neural networks to predict therapeutic pressure. Forward selection based on the Akaike Information Criterion (AIC) was used to identify the most relevant variables. The model was trained on a dataset of 2,092 patients, with model performance assessed using mean absolute error (MAE).

The most influential variables identified were BMI, neck circumference, and waist-to-hip ratio. Among the algorithms, LightGBM achieved the highest predictive accuracy, with the lowest MAE. Ensemble methods, such as voting, did not improve performance beyond LightGBM alone. Subsample analyses revealed that prediction accuracy varied across BMI ranges and ventilator brands.

The study demonstrates that BMI and other physical parameters play a pivotal role in determining CPAP pressure, offering a simplified yet effective prediction model. This approach has significant potential for clinical applications, particularly in resource-limited settings, where access to complex sleep studies may be restricted. Future research could enhance the model by incorporating real-time physiological data and expanding data collection to diverse populations.