Predicting global and regional respiratory response in ARDS patients through individualized physics-based...

Lung-protective ventilation is essential in the management of acute respiratory distress syndrome (ARDS), but current approaches are limited by the heterogeneity of lung pathology and a lack of individualized strategies. Patient-specific computational models may overcome these limitations by providing predictive and physiologically interpretable insights into global and regional lung behavior.

Ten mechanically ventilated patients with ARDS were evaluated in a monocentric prospective pilot study. A predefined study protocol included ventilatory maneuvers with systematic changes in ventilator mode, tidal volume, positive end-expiratory pressure (PEEP), and driving pressure. For each patient, a personalized, regionally resolved physics-based computational lung model was generated and calibrated to reproduce the individual respiratory system mechanics. Model predictions of tidal volume, airway and transpulmonary pressures, end-expiratory lung volume (EELV), and electrical impedance tomography (EIT)-derived regional ventilation distribution were simulated for different PEEP levels and evaluated against clinically assessed reference values. Model predictions were compared to clinical data using Pearson correlation coefficients (r), root mean square error (RMSE), and Bland-Altman analyses.

A strong agreement was observed between simulated and clinical data for tidal volumes (r = 0.925, p < 0.001, RMSE = 0.053 l) and driving pressures (r = 0.940, p < 0.001, RMSE = 1.328 mbar) across all PEEP levels. At initial PEEP level, a high image correlation (r > 0.8) was found between measured and simulated regional ventilation in five of seven patients where EIT data were available.

This pilot study demonstrates the capability of the individualized, regionally resolved physics-based computational lung model to reproduce patient-specific respiratory mechanics and key features of regional ventilation patterns in ARDS. The strong agreement between simulated and measured data supports the potential of this approach to estimate the physiological response to ventilator adjustments and lays the groundwork for future studies integrating computational modeling into clinical decision-making.

Registered at the German Clinical Trials Register (DRKS00017151, https://drks.de/search/en/trial/DRKS00017151 ) at 12th April 2019.