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  • 标题:Detection of Different COVID-19 Pneumonia Phenotypes with Estimated Alveolar Collapse and Overdistention by Bedside Electrical Impedance Tomography ⁎
  • 本地全文:下载
  • 作者:Rongqing Chen ; András Lovas ; Balázs Benyó
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2021
  • 卷号:54
  • 期号:15
  • 页码:269-274
  • DOI:10.1016/j.ifacol.2021.10.267
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractCOVID-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.
  • 关键词:Keywordsbiomedical imaging systemsdecision support systems for the control of physiologicalclinical variablescontrol of voluntary movementsrespirationlocomotion
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