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

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 techni...

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Detalles Bibliográficos
Autores principales: Lovas, András, Chen, Rongqing, Molnár, Tamás, Benyó, Balázs, Szlávecz, Ákos, Hawchar, Fatime, Krüger-Ziolek, Sabine, Möller, Knut
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9161711/
https://www.ncbi.nlm.nih.gov/pubmed/35665323
http://dx.doi.org/10.3389/fmed.2022.747570
Descripción
Sumario: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.” CLINICAL TRIAL REGISTRATION: www.ClinicalTrials.gov, identifier: NCT04360837.