Estimation of transpulmonary driving pressure using a lower assist maneuver (LAM) during synchronized ventilation in patients with acute respiratory failure: a physiological study.

Journal: Intensive Care Medicine Experimental
Published:
Abstract

Background: We previously showed in animals that transpulmonary driving pressure (PL) can be estimated during Neurally Adjusted Ventilatory Assist (NAVA) and Neural Pressure Support (NPS) using a single lower assist maneuver (LAM). The aim of this study was to test the LAM-based estimate of PL (PL_LAM) in patients with acute respiratory failure.

Methods: This was a prospective, physiological, and interventional study in intubated patients with acute respiratory failure. During both NAVA and simulated NPS (high and low levels of assist), a LAM was performed every 3 min by manually reducing the assist to zero for one single breath (by default, ventilator still provides 2 cmH2O). Following NAVA and NPSSIM periods, patients were sedated and passively ventilated in volume control and pressure control ventilation, to obtain PL during controlled mechanical ventilation (PL_CMV). PL using an esophageal balloon (PL_Pes) was also compared to PL_LAM and PL_CMV. We measured diaphragm electrical activity (Edi), ventilator pressure (PVent), esophageal pressure (Pes) and tidal volume. PL_LAM and PL_Pes were compared to themselves, and to PL_CMV for matching flows and volumes.

Results: Ten patients were included in the study. For the group, PL_LAM was closely similar to PL_CMV, with a high correlation (R2 = 0.88). Bland-Altman analysis revealed a low Bias of 0.28 cmH2O, and 1.96SD of 5.26 cmH2O. PL_LAM vs PL_Pes were also tightly related (R2 = 0.77).

Conclusions: This physiological study in patients confirms our previous pre-clinical data that PL_LAM is as good an estimate as PL_Pes to determine PL, in spontaneously breathing patients on assisted mechanical ventilation. Trial registration The study was registered at clinicaltrials.gov (ID NCT05378802) on November 6, 2021.

Authors
Ling Liu, Hao He, Meihao Liang, Jennifer Beck, Christer Sinderby