Differentiating Phenotypes of Coronavirus Disease-2019 Pneumonia by Electric Impedance Tomography

Frontiers in Medicine

article

Tamás Molnár¹  Balázs Benyó²  Ákos Szlávecz²  Fatime Hawchar¹  Sabine Krüger-Ziolek³  Knut Möller³  András Lovas⁴  Rongqing Chen³ 

[1] Department of Anesthesiology and Intensive Therapy, University of Szeged;Department of Control Engineering and Information Technology, Budapest University of Technology and Economics;Institute of Technical Medicine, Furtwangen University;Department of Anesthesiology and Intensive Therapy, Kiskunhalas Semmelweis Hospital

关键词: acute lung injury;    compliance;    Coronavirus-COVID-19;    electric impedance tomography;    recruitment maneuver;   

DOI  :  10.3389/fmed.2022.747570

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Introduction Coronavirus disease-2019 (COVID-19) pneumonia has different phenotypes. Selecting the patient individualized and optimal respirator settings for the ventilated patient is a challenging process. Electric impedance tomography (EIT) is a real-time, radiation-free functional imaging technique that can aid clinicians in differentiating the “low” (L-) and “high” (H-) phenotypes of COVID-19 pneumonia described previously. Methods Two patients (“A” and “B”) underwent a stepwise positive end-expiratory pressure (PEEP) recruitment by 3 cmH 2 O of steps from PEEP 10 to 25 and back to 10 cmH 2 O during a pressure control ventilation of 15 cmH 2 O. Recruitment maneuvers were performed under continuous EIT recording on a daily basis until patients required controlled ventilation mode. Results Patients “A” and “B” had a 7- and 12-day long trial, respectively. At the daily baseline, patient “A” had significantly higher compliance: mean ± SD = 53 ± 7 vs . 38 ± 5 ml/cmH 2 O ( p < 0.001) and a significantly higher physiological dead space according to the Bohr–Enghoff equation than patient “B”: mean ± SD = 52 ± 4 vs . 45 ± 6% ( p = 0.018). Following recruitment maneuvers, patient “A” had a significantly higher cumulative collapse ratio detected by EIT than patient “B”: mean ± SD = 0.40 ± 0.08 vs . 0.29 ± 0.08 ( p = 0.007). In patient “A,” there was a significant linear regression between the cumulative collapse ratios at the end of the recruitment maneuvers ( R 2 = 0.824, p = 0.005) by moving forward in days, while not for patient “B” ( R 2 = 0.329, p = 0.5). Conclusion Patient “B” was recognized as H-phenotype with high elastance, low compliance, higher recruitability, and low ventilation-to-perfusion ratio; meanwhile patient “A” was identified as the L-phenotype with low elastance, high compliance, and lower recruitability. Observation by EIT was not just able to differentiate the two phenotypes, but it also could follow the transition from L- to H-type within patient “A.”.

【 授权许可】

CC BY