Calculation of Transpulmonary Pressure From Regional Ventilation Displayed by Electrical Impedance Tomography in Acute Respiratory Distress Syndrome.

Transpulmonary driving pressure (DPL) corresponds to the cyclical stress imposed on the lung parenchyma during tidal breathing and, therefore, can be used to assess the risk of ventilator-induced lung injury (VILI). Its measurement at the bedside requires the use of esophageal pressure (Peso), which is sometimes technically challenging. Recently, it has been demonstrated how in an animal model of ARDS, the transpulmonary pressure (PL) measured with Peso calculated with the absolute values method (PL = Paw-Peso) is equivalent to the transpulmonary pressure directly measured using pleural sensors in the central-dependent part of the lung. We hypothesized that, since the PL derived from Peso reflects the regional behavior of the lung, it could exist a relationship between regional parameters measured by electrical impedance tomography (EIT) and driving PL (DPL). Moreover, we explored if, by integrating airways pressure data and EIT data, it could be possible to estimate non-invasively DPL and consequently lung elastance (EL) and elastance-derived inspiratory PL (PI). We analyzed 59 measurements from 20 patients with ARDS. There was a significant intra-patient correlation between EIT derived regional compliance in regions of interest (ROI1) (r = 0.5, p = 0.001), ROI2 (r = -0.68, p < 0.001), and ROI3 (r = -0.4, p = 0.002), and DPL. A multiple linear regression successfully predicted DPL based on respiratory system elastance (Ers), ideal body weight (IBW), roi1%, roi2%, and roi3% (R 2 = 0.84, p < 0.001). The corresponding Bland-Altmann analysis showed a bias of -1.4e-007 cmH2O and limits of agreement (LoA) of -2.4-2.4 cmH2O. EL and PI calculated using EIT showed good agreement (R 2 = 0.89, p < 0.001 and R 2 = 0.75, p < 0.001) with the esophageal derived correspondent variables. In conclusion, DPL has a good correlation with EIT-derived parameters in the central lung. DPL, PI, and EL can be estimated with good accuracy non-invasively combining information coming from EIT and airway pressure.