• Wenfei Wang, Anup Das, Oanna Cole, Marc Chikhani, Jonathan G Hardman, and Declan G Bates.
• School of Engineering, University of Warwick, Coventry, CV4 7AL, UK. Wenfei.Wang@warwick.ac.uk.
• Intensive Care Med Exp. 2015 Dec 1; 3 (1): 33.

BackgroundA recent prospective trial using porcine models of severe acute respiratory distress syndrome (ARDS) indicated that positive-pressure ventilation delivered by a conventional intensive care ventilator at a moderately high frequency allows safe reduction of tidal volume below 6 ml/kg, leading to more protective ventilation. We aimed to explore whether these results would be replicated when implementing similar ventilation strategies in a high-fidelity computational simulator, tuned to match data on the responses of a number of human ARDS patients to different ventilator inputs.MethodsWe evaluated three different strategies for managing the trade-off between increasing respiratory rate and reducing tidal volume while attempting to maintain the partial pressure of carbon dioxide in arterial blood (PaCO2) constant on a computational simulator configured with ARDS patient datasets.ResultsFor a fixed sequence of stepwise increases in the respiratory rate, corresponding decreases in tidal volume to keep the alveolar minute ventilation and inspiratory flow constant were calculated according to standard formulae. When applied on the simulator, however, these sequences of ventilator settings failed to maintain PaCO2 adequately in the virtual patients considered. In contrast, an approach based on combining numerical optimisation methods with computational simulation allowed a sequence of tidal volume reductions to be computed for each virtual patient that maintained PaCO2 levels while significantly reducing peak airway pressures and dynamic alveolar strain in all patients.ConclusionsOur study supports the proposition that moderately high-frequency respiratory rates can allow more protective ventilation of ARDS patients and highlights the potential role of high-fidelity simulators in computing optimised and personalised ventilator settings for individual patients using this approach.

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