Patient-specific lung simulation incorporating regional elastance and recruitment to guide mechanical ventilation in acute respiratory distress syndrome.

Mechanical ventilation (MV) plays a vital role in intensive care, ensuring sufficient gas exchange in patients with acute respiratory distress syndrome (ARDS). However, ventilator-induced lung injury (VILI) remains a frequent complication associated with MV, arising due to local lung tissue hyperinflation (HI) and cyclic alveolar recruitment/derecruitment (R/D). Determining optimal ventilator settings is a clinical challenge, since the full spectrum of local lung mechanics in a heterogenous lung cannot be assessed with overall mechanical measurements nor with routine imaging modalities. Computational modeling offers a promising approach for personalizing mechanical ventilation settings, by predicting the local lung mechanical behavior. We propose an in silico model of the respiratory system of a mechanically ventilated patients with ARDS, which integrates local patient-specific lung characteristics. These include both structural (airway tree and lung morphology) and functional (regional lung elastance and R/D dynamics) information, inferred from computed tomography (CT) data obtained at two different respiratory pressure instances. Our proof-of-principle simulations indicate that the model plausibly estimates the global respiratory pressure-volume curve, as well as regional lung biomechanical behavior, under positive pressure ventilation. Furthermore, we show that this model can be used to simulate the effect of changes in ventilator settings such as positive end-expiratory pressure (PEEP) or to simulate an impaired lung with worsening biomechanics. This model thereby provides a mechanistic foundation to eventually support clinicians in delivering more precise, patient-specific therapies, by offering a supplementary tool for optimizing ventilator settings.NEW & NOTEWORTHY This study presents a computational model for simulating ventilatory response in mechanically ventilated patients with acute respiratory distress syndrome and introduces a novel method to estimate patient-specific lung mechanical properties from a dual-volume computed tomography (CT) protocol. The key purpose of the in silico model proposed in this paper is to provide clinicians with a complementary modality that supports decision-making surrounding mechanical ventilation.