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
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.
ABSTRACT
COVID-19 induced acute respiratory distress syndrome (ARDS) could have two different phenotypes, which was reported to have different response and outcome to the typical ARDS positive end-expiration pressure (PEEP) treatment. The identification of the different phenotypes in terms of the recruitability can help improve the patient outcome. In this contribution we conducted alveolar overdistention and collapse analysis with the long term electrical impedance tomography monitoring data on two severe COVID-19 pneumonia patients. The result showed different patient reactions to the PEEP trial, revealed the progressive change in the patient status, and indicted a possible phenotype transition in one patient. It might suggest that EIT can be a practical tool to identify phenotypes and to provide progressive information of COVID-19 pneumonia. Copyright (C) 2021 The Authors.
ABSTRACT
Introduction COVID-19 induced acute respiratory distress syndrome (ARDS) could have two different phenotypes, namely L-type and H-type. Different phenotypes were reported to have different responses and outcomes to the typical ARDS positive end-expiration pressure (PEEP) treatment. The identification of the different phenotypes in terms of the PEEP recruitment difference can help to improve the patient outcome. Methods This contribution was conducted with a retrospective patient dataset. The patient was diagnosed with COVID-19 pneumonia and admitted to intensive care unit. PEEP trial was applied on the patient with electrical impedance tomography (EIT) monitoring for seven days. Conductivity distribution difference images between the PEEP level 25 and 10 cmH2O were obtained from tidal variation images on day 1, day 3 and day 7, respectively. The conductivity distribution difference images were used to detect the recruitment areas during the PEEP trial. Results A clear course developing trend in terms of decreasing recruitment area can be observed in conductivity distribution difference images, which is complied with the medical record stating a deteriorating condition of the patient. This analysis indicate that the patient is a L-type with low recruitability. If this is the case, the traditional recruitment maneuvers used in common ARDS patients might not provide the expected outcome, on the contrary might introduce an increased risk of structural damage of the lung due to the high PEEP. Conclusion The course of the COVID-19 pneumonia is still poorly understood and has shown to develop very fast. In this contribution, a rather clear development of a L-type patient can be observed from the long-term EIT monitoring. EIT might develop into a useful and practical tool to assist with the classification of the different phenotypes of the COVID-19 patients in addition to the CT, and might provide additional information about progression of the disease and the evaluation of its treatment strategies.
ABSTRACT
Introduction In November 2019, a new Coronavirus strain appeared in Wuhan, province of Hubei, China. As for April 2021, more than 146 million COVID-19 confirmed cases and 3 million deaths have been reported by the WHO. From the beginning of the pandemic, patients have been reacting differently to the same treatment, thus suggesting that phenotype differences may be worth investigating. Back in 2020, a first phenotype-based study, in which L-type and H-type COVID-19 types were described, was published and different ventilation approaches suggested. Following this study, the aim of this project is to differentiate COVID-19 phenotypes by analysing lung compliances from EIT monitoring during ventilation maneuvers. Methods Two COVID-19 infected and mechanically ventilated patients have been analysed for more than 7 days. Each patient has undergone a PEEP trial on a daily basis, thus obtaining incremental and decremental PEEP steps (basal PEEP was 10 cmH2O and maximum PEEP was 25 cmH2O) for each EIT recording. Those recordings have been used to obtain EIT based compliance images. Finally, a comparison between initial and final compliances has been carried out to analyse the effects that the manoeuvres have had in the patients. ABG analyses have supported EIT recordings. Results Both patients have reacted differently to the PEEP manoeuvres. Differences in the obtained regional compliance maps suggest that both patients belong to different types of COVID-19 infection types: the patient that shows a bigger recruitable area might belong to the H-type, while the other is speculated belongs to the L-type. Conclusion EIT technology is a valid tool to provide useful information regarding COVID-19 patients and their respective types of disease progression (L-type and H-type).
ABSTRACT
Purpose: To evaluate the lung compliance variation over the course of COVID-19 pneumonia, and to classify the patients into different types described as recruitable and non-recruitable, which lead to different ventilator support treatment. Method: Two ICU admitted COVID-19 patients, who were mechanically ventilated for more than 7 days, were included into this investigation. During a daily recruitment maneuver - a PEEP trial - they were monitored by Electrical Impedance Tomography (EIT). Deflation time constants were calculated offline from EIT data to determine the type of patient and to observe the transition of different types over the course of pneumonia. Result: The first patient was recruitable and had the tendency of transition to the other type. The second patient is non-recruitable. Both patients showed low lung compliance, but the first patient started in a better condition (higher compliance). Conclusion: EIT-based breath-by-breath time constant analysis can classify COVID-19 pneumonia into different classes of patients. The deterioration of lung mechanics can be monitored online by EIT which may help to find proper ventilation treatment. © 2021, Springer Nature Switzerland AG.