A computational physiological model of acute respiratory distress syndrome patients for positive end-expiratory pressure titration

Background and objective: Patients with acute respiratory distress syndrome (ARDS) experience alveolar collapse, leading to reduced ventilation and blood oxygenation. Clinical treatment with mechanical ventilation involves the use of positive end-expiratory pressure (PEEP) to recruit collapsed alveoli. The identification of an optimal PEEP level has remained a subject of debate in critical care for over 50 years. This work proposes a 0D car diopulmonary computational physiological model (CPM) to investigate the issue of PEEP titration by studying the impact of PEEP on the cardiopulmonary system of ARDS patients. Methods: The 0D cardiopulmonary CPM presented in this work was implemented using Mathworks® Simulink. In the respiratory system model, the lung was divided into aerated alveoli and collapsed alveoli, allowing the latter to open when the pressure exceeds the recruitment threshold. Conversely, when the pressure falls below the collapse threshold, the recruited alveoli close. In the circulatory system model, the lung was divided into non- perfused (zone 1), partially perfused (zone 2), and perfused alveoli (zone 3), depending on the increased alve olar pressure caused by PEEP. Pulmonary ventilation and perfusion were combined using gas exchange equations to determine blood oxygenation. The model was tested on a dataset of 1000 synthetic patients generated from ARDS patient input data reported in the literature. Results: A statistical validation of the 0D cardiopulmonary CPM was conducted by calculating the Wasserstein distance between the distributions of the model’s calculated outputs and the reference output distributions. The Wasserstein distance values obtained were 0.15, 0.43, 0.78, and 0.63 respectively for the outputs of the gas exchange equations, the respiratory system model, the circulatory system model, and the complete model. Conclusions: The 0D cardiopulmonary CPM developed in this study integrates a novel alveolar recruitment and collapse mechanism, the simulation of pulmonary capillary disengagement, and gas exchange equations to predict the impact of PEEP on blood oxygenation. The model demonstrates potential applicability for PEEP titration in clinical practice, providing the ability to replicate ARDS patient conditions using clinical data, combined with high reliability and low computational cost.