Introduction: Ventilatory support in ARDS typically relies on lung-protective strategies, aimed at minimizing risk for ventilator-induced lung injury (VILI). In this context, the ability to anticipate changes in respiratory function may support individualized treatment, thus improving patient outcomes. Ventilator waveforms such as airway flow, pressure, and volume are continuously monitored and can be analyzed with machine learning techniques to identify patterns associated with key physiological derangements. In this study, we investigate the use of a convolutional neural network (CNN) to estimate respiratory system compliance. Our objective is to assess whether this approach can predict changes in compliance over time in a large animal model of ARDS.
Methods: Acute lung injury was induced via oleic acid infusion into the pulmonary artery. Following injury maturation, nine pigs were ventilated with volume-controlled, lung-protective conventional mechanical ventilation and monitored for nine hours. Airway pressure and flow waveforms were recorded at five timepoints: baseline (BL), immediately after injury maturation (T0), and 3, 6, and 9 hours post-injury (T1, T2, T3). Respiratory system mechanics parameters were estimated using multiple linear regression based on the equation of motion. A CNN was developed to predict respiratory system compliance at T1, T2, and T3.
Results: Model predictions of future compliance (i.e., 3 hours later) showed a strong alignment with observed compliances at all time points. At T1, predicted compliance was slightly higher than true compliance, with no significant difference (p = 0.471), achieving the highest correlation and lowest RMSE. At T2, predicted closely matched true compliance, with strong correlation. At T3, predicted matched true compliance, with significant correlation. (r² = 0.54, p = 0.025) and RMSE 2.89 mL cmH₂O-1.
Conclusions: Overall, our findings support the capability of a CNN to capture accurately individual compliance trajectories over time. Future research should aim to validate this approach in larger and more diverse populations, and to explore the practical use of ventilator waveform data for real-time patient monitoring at the bedside. Such developments could provide clinicians with valuable tools to support clinical decision-making.
