Rationale: Defining lung recruitability is needed for safe positive end-expiratory pressure (PEEP) selection in mechanically ventilated patients. However, there is no simple bedside method including both assessment of recruitability and risks of overdistension as well as personalized PEEP titration. Objectives: To describe the range of recruitability using electrical impedance tomography (EIT), effects of PEEP on recruitability, respiratory mechanics and gas exchange, and a method to select optimal EIT-based PEEP . Methods: This is the analysis of COVID-19 patients , from an ongoing multicenter prospective physiological study including patients with moderate-severe ARDS of different causes. EIT, ventilator data, hemodynamics and arterial blood gases were obtained during PEEP titration maneuvers. EIT-based optimal PEEP was defined as the crossing point of the overdistension and collapse curves during a decremental PEEP trial. Recruitability was defined as the amount of modifiable collapse when increasing PEEP from 6 to 24 cmH2O (=ΔCollapse24-6). Patients were classified as low, medium or high recruiters based on the tertiles of ΔCollapse24-6. Measurements and main results: In 108 COVID-19 patients, recruitability varied from 0.3% to 66.9% and was unrelated to ARDS severity. EIT-based PEEP differed between groups: 10 vs. 13.5 vs. 15.5 cmH2O for low vs. medium vs. high recruitability (p<0.05). This approach assigned a different PEEP level than the highest compliance approach in 81% of patients. The protocol was well tolerated; in 4 patients the PEEP level did not reach 24 cmH2O due to hemodynamic instability. Conclusions: Recruitability varies widely among COVID-19 patients. EIT allows personalizing PEEP setting as a compromise between recruitability and overdistension. This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

Lung RecruitmEnt Assessed by EleCtRical Impedance Tomography (RECRUIT): A Multicenter Study of COVID-19 ARDS

Spadaro, Savino;Scaramuzzo, Gaetano;
2023

Abstract

Rationale: Defining lung recruitability is needed for safe positive end-expiratory pressure (PEEP) selection in mechanically ventilated patients. However, there is no simple bedside method including both assessment of recruitability and risks of overdistension as well as personalized PEEP titration. Objectives: To describe the range of recruitability using electrical impedance tomography (EIT), effects of PEEP on recruitability, respiratory mechanics and gas exchange, and a method to select optimal EIT-based PEEP . Methods: This is the analysis of COVID-19 patients , from an ongoing multicenter prospective physiological study including patients with moderate-severe ARDS of different causes. EIT, ventilator data, hemodynamics and arterial blood gases were obtained during PEEP titration maneuvers. EIT-based optimal PEEP was defined as the crossing point of the overdistension and collapse curves during a decremental PEEP trial. Recruitability was defined as the amount of modifiable collapse when increasing PEEP from 6 to 24 cmH2O (=ΔCollapse24-6). Patients were classified as low, medium or high recruiters based on the tertiles of ΔCollapse24-6. Measurements and main results: In 108 COVID-19 patients, recruitability varied from 0.3% to 66.9% and was unrelated to ARDS severity. EIT-based PEEP differed between groups: 10 vs. 13.5 vs. 15.5 cmH2O for low vs. medium vs. high recruitability (p<0.05). This approach assigned a different PEEP level than the highest compliance approach in 81% of patients. The protocol was well tolerated; in 4 patients the PEEP level did not reach 24 cmH2O due to hemodynamic instability. Conclusions: Recruitability varies widely among COVID-19 patients. EIT allows personalizing PEEP setting as a compromise between recruitability and overdistension. This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
2023
Jonkman, Annemijn H; Alcala, Glasiele C; Pavlovsky, Bertrand; Roca, Oriol; Spadaro, Savino; Scaramuzzo, Gaetano; Chen, Lu; Dianti, Jose; Sousa, Mayson L de A; Sklar, Michael C; Piraino, Thomas; Ge, Huiqing; Chen, Guang-Qiang; Zhou, Jian-Xin; Li, Jie; Goligher, Ewan C; Costa, Eduardo; Mancebo, Jordi; Mauri, Tommaso; Amato, Marcelo; Brochard, Laurent J
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2508990
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