Dynamic driving pressure predicts ventilator-induced lung injury in mice with and without endotoxin-induced acute lung injury.

Abstract

Mechanical ventilation is a necessary lifesaving intervention for patients with Acute Respiratory Distress Syndrome (ARDS) but it can cause ventilator induced lung injury (VILI), which contributes to the high ARDS mortality rate (≈40%). Bedside determination of optimally lung-protective ventilation settings is challenging because the evolution of VILI is not immediately reflected in clinically available, patient-level, data. The goal of this work was therefore to test ventilation waveform-derived parameters that represent the degree of ongoing VILI and can serve as targets for ventilator adjustments. VILI was generated at three different positive end expiratory pressures in a murine inflammation-mediated (lipopolysaccharide, LPS) acute lung injury model and in initially healthy controls. LPS injury increased expression of proinflammatory cytokines and caused widespread atelectasis, predisposing the lungs to VILI as measured in structure, mechanical function, and inflammation. Changes in lung function were used as response variables in an elastic net regression model that predicted VILI severity from tidal volume, dynamic driving pressure (PDDyn), mechanical power calculated by integration during inspiration or the entire respiratory cycle, and power calculated according to Gattinoni' s equation. Of these, PDDyn best predicted functional outcomes of injury using either data from the entire dataset or from 5-minute time windows. The windowed data shows higher predictive accuracy after a ≈1-hour 'run in' period and worse accuracy immediately following recruitment maneuvers. This analysis shows that low driving pressure is a computational biomarker associated with better experimental VILI outcomes and supports the use of driving pressure to guide ventilator adjustments to prevent VILI.