Modeling Early Oxygenation Trajectory in PARDS From High-Frequency Mechanical Ventilation Signals Using Deep Sequence...

PARDS causes substantial morbidity in PICU. We investigated whether high-frequency ventilator waveforms from first hour of invasive ventilation contain predictive information for oxygenation trajectory.

We analyzed mechanically ventilated pediatric patients using engineered statistical features and learned representations from high-frequency ventilator signals, including breath-by-breath flow waveforms. Engineered features included early oxygenation indices and ventilator variables derived from time- and frequency-domain analyses. Learned representations were obtained from 1D-CNN embeddings with PCA-based dimensionality reduction. We evaluated two tasks: 12-hour OSI regression and classification of mild vs moderate-or-higher impairment (OSI). Five sequence models (RNN, LSTM, GRU, Transformer, Mamba) and nine feature configurations were assessed using repeated cross-validation and validation on held-out and temporally separated cohorts.

For classification, the best cross-validated AUROC was 0.819 ± 0.030. Performance remained consistent across independent cohorts, with AUROC values of 0.787-0.823 on the test cohort and 0.808-0.832 on the temporal validation cohort, and Brier scores $\sim$0.12-0.15. For regression, the best cross-validated RMSE was 2.45 ± 0.81. Test RMSE ranged from 2.99 to 3.48 OSI units and validation RMSE from 2.60 to 2.85.

High-frequency ventilator waveforms from the first hour of mechanical ventilation contain measurable information about short-horizon oxygenation trajectory in PARDS.

These findings support feasibility of modeling early oxygenation trajectory using routinely available ventilator waveform data and motivate prospective multi-institutional validation.