EMBC Special Issue: Modeling Early Oxygenation Trajectory in PARDS from High-Frequency Mechanical Ventilation Signals Using Deep Sequence Architectures

OBJECTIVE: Pediatric acute respiratory distress syndrome (PARDS) causes substantial morbidity in the pediatric intensive care unit. We investigated whether high-frequency ventilator waveforms collected during the first hour of invasive ventilation contain predictive information about subsequent oxygenation trajectory. METHODS: Mechanically ventilated pediatric patients were analyzed using engineered statistical features and learned representations derived from high-frequency ventilator signals, including breath-by-breath flow waveforms from bedside ventilators. Engineered statistical features included early oxygenation indices and ventilator variables from time- and frequency-domain analyses, while learned representations were obtained from 1D-CNN embeddings with PCA-based dimensionality reduction. Two prediction tasks (12-hour OSI regression and classification of mild vs. moderate-or-higher impairment, OSI 7. 5) were evaluated using five sequence architectures (RNN, LSTM, GRU, Transformer, Mamba) across nine feature configurations, with repeated cross-validation and evaluation on held-out test and temporally separated validation cohorts. RESULTS: 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 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. CONCLUSION: High-frequency ventilator waveforms acquired during the first hour of mechanical ventilation contain measurable information about short-horizon oxygenation trajectory in PARDS. SIGNIFICANCE: These findings demonstrate the feasibility of modeling early oxygenation trajectory using routinely available ventilator waveform data and support further prospective and multi-institutional validation prior to clinical translation.