ABSTRACT
Due to the coronavirus pandemic, portable electrical impedance tomography (EIT) systems [1]-[3] have been considered as the only variable wearable medical lung imaging solution for monitoring the treatment of pneumonia patients and their recovery. Generally, the EIT system is classified into passive EIT (P-EIT) [3]-[6] or active electrode EIT (AE-EIT) [2]. The AE-EIT system is preferred as it amplifies and digitalizes the small signals while minimizing the noises incurred by motion artifacts, complex long wire connection, large variation in electrode contact, and stray capacitance problems, which is important for high-performance imaging applications. © 2022 IEEE.
ABSTRACT
It was reported that COVID-19 induced acute respiratory distress syndrome (ARDS) comes at least in two different phenotypes. Different responses and outcomes to typical positive end-expiration pressure (PEEP) trial are found in those different phenotypes. Lung recruitability during a PEEP trial can be used to identify different phenotypes to help improve the patient outcome. In this study, we analysed overdistention and collapse ratio with electrical impedance tomography (EIT) monitoring data on four severe COVID-19 pneumonia patients to identify their phenotypes. Results demonstrate the different patient responses to a PEEP trial, and showed the developing change in patient status over time. In one patient a possible phenotype transition was identified. We suggest that EIT may be a practical tool to identify phenotypes and to provide information about COVID-19 pneumonia progression. © 2022 The Author(s), published by De Gruyter.
ABSTRACT
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 cmH2O of steps from PEEP 10 to 25 and back to 10 cmH2O during a pressure control ventilation of 15 cmH2O. 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/cmH2O (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.
ABSTRACT
The COVID-19 is a viral infection that causes respiratory complications. Infected lungs often present ground glass opacities, thus suggesting that medical imaging technologies could provide useful information for the disease diagnosis, treatment, and posterior recovery. The Electrical Impedance Tomography (EIT) is a non-invasive, radiationfree, and continuous technology that generates images by using a sequence of current injections and voltage measurements around the body, making it very appropriate for the study to monitor the regional behaviour of the lung. Moreover, this tool could also be used for a preliminary COVID-19 phenotype classification of the patients. This study is based on the monitoring of lung compliances of two COVID-19-infected patients: the results indicate that one of them could belong to the H-type, while the other is speculated belongs to L-type. It has been concluded that the EIT is a useful tool to obtain information regarding COVID-19 patients and could also be used to classify different phenotypes. © 2021 by Walter de Gruyter Berlin/Boston.
ABSTRACT
COVID-19 induced acute respiratory distress syndrome (ARDS) could have two different phenotypes, which might have different response and outcome to the traditional ARDS positive end-expiration pressure (PEEP) treatment. The identification of the different phenotypes in terms of the PEEP recruitment can help improve the patients' outcome. In this contribution we reported a COVID-19 patient with seven-day electrical impedance tomography monitoring. From the conductivity distribution difference image analysis of the data, a clear course developing trend can be observed in addition to the phenotype identification. This case might suggest that EIT can be a practical tool to identify phenotypes and to provide progressive information of COVID-19 pneumonia. © 2021 by Walter de Gruyter Berlin/Boston.