인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Extracorporeal Carbon Dioxide Removal (ECCO<sub>2</sub>R) is used in acute respiratory distress syndrome (ARDS) patients to facilitate lung-protective ventilatory strategies. Electrical Impedance Tomography (EIT) allows individual, non-invasive, real-time, bedside, radiation-free imaging of the lungs, providing global and regional dynamic lung analyses. To provide new insights for future ECCO2R research in ARDS, we propose a potential application of EIT to personalize End-Expiratory Pressure (PEEP) following each reduction in tidal volume (VT), as demonstrated in an illustrative case. A 72-year-old male with COVID-19 was admitted to the ICU for moderate ARDS. Monitoring with EIT was started to determine the optimal PEEP value (PEEP<sub>EIT</sub>), defined as the intersection of the collapse and overdistention curves, after each reduction in VT during ECCO<sub>2</sub>R. The identified PEEP<sub>EIT</sub> values were notably low (< 10 cmH2O). The decrease in VT associated with PEEP<sub>EIT</sub> levels resulted in improved lung compliance, reduced driving pressure and a more uniform ventilation pattern. Despite current Randomized Controlled Trials showing that ultra-protective ventilation with ECCO<sub>2</sub>R does not improve survival, the applicability of universal ultra-protective ventilation settings for all patients remains a subject of debate. Inappropriately set PEEP levels can lead to alveolar collapse or overdistension, potentially negating the benefits of VT reduction. EIT facilitates real-time monitoring of derecruitment associated with VT reduction, guiding physicians in determining the optimal PEEP value after each decrease in tidal volume. This original description of using EIT under ECCO<sub>2</sub>R to adjust PEEP at a level compromising between recruitability and overdistention could be a crucial element for future research on ECCO<sub>2</sub>R.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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