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Clinical Advances in Extracorporeal Membrane Oxygenation (ECMO)—Second Edition

A Special Issue of Journal of Clinical Medicine (ISSN 2077-0383) belonging to the section "Intensive Care".

Deadline for manuscript submissions: 20 March 2027 | Viewed by 516

Editors


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Guest Editor
Divisions of Neurosciences Critical Care and Cardiac Surgery, Departments of Neurology, Surgery, Anesthesiology, and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Interests: neurocritical care; brain injury; neurological outcome; stroke; ECMO; mechanical circulatory support device; ARDS
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Guest Editor
Department of Anesthesiology, Division of Critical Care, University of Virginia, Charlottesville, VA 22903, USA
Interests: critical care medicine; intensive care medicine; sepsis; resuscitation; cardiopulmonary resuscitation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are thrilled to announce the release of the second volume of our Special Issue, entitled "Clinical Advances in Extracorporeal Membrane Oxygenation (ECMO)—Second Edition". The use of extracorporeal membrane oxygenation (ECMO) has dramatically increased in the last decade. Veno-Arterial (V-A) ECMO is the most common strategy for the rescue of refractory cardiac failure, and Veno-Venous (VV) ECMO is employed for refractory respiratory failure. Innovative and new therapies, monitoring strategies, and clinical evidence/research are being continually developed in the field of ECMO. As advances in therapeutics, technology, and management strategy have improved outcomes in the field of ECMO, improving adverse events such as on-ECMO complications and organ dysfunction is of the utmost importance in patients with ECMO support. Therefore, it is critical that ECMO providers are up to date on these new developments and how they are related to outcomes to provide appropriate critical care for our patients. In this Special Issue, we welcome authors to submit papers on the recent advances in clinical research in V-A and V-V ECMO.

Please visit the following link to access the first volume of our Special Issue: https://www.mdpi.com/journal/jcm/special_issues/B7FD562Z4Z. We look forward to receiving your submissions.

Dr. Sung-Min Cho
Dr. Akram Zaaqoq
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • extracorporeal membrane oxygenation
  • ECMO
  • multi-organ failure
  • outcomes
  • V-A ECMO
  • ECPR
  • V-V ECMO
  • translational research
  • clinical research

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Published Papers (1 paper)

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Research

21 pages, 1392 KB  
Article
Critical Care Monitoring in Comatose Patients Receiving ECMO: A Cohort Study of Machine Learning on Quantitative EEG for Diagnosing Acute Brain Injury and Prognosticating Mortality
by Mingfeng Cao, Jeffrey B. Wang, Beichen Shen, Zoe Soule, Kotaro Noda, Jaeho Hwang, Eva Ritzl, Yaman B. Ahmed, Hyun-Yi Woo, Siyu Wang, Tianyue Zhu, Leon Fan, Nirma Carballido Martinez, Glenn Whitman, Nitish Thakor and Sung-Min Cho
J. Clin. Med. 2026, 15(17), 6761; https://doi.org/10.3390/jcm15176761 - 31 Aug 2026
Viewed by 285
Abstract
Background: Acute brain injury (ABI) is a major cause of mortality and morbidity during extracorporeal membrane oxygenation (ECMO), yet early diagnosis remains challenging because neuroimaging is often impractical in critically ill patients. We evaluated whether quantitative electroencephalography (qEEG) combined with machine learning could [...] Read more.
Background: Acute brain injury (ABI) is a major cause of mortality and morbidity during extracorporeal membrane oxygenation (ECMO), yet early diagnosis remains challenging because neuroimaging is often impractical in critically ill patients. We evaluated whether quantitative electroencephalography (qEEG) combined with machine learning could identify ABI and predict mortality in patients receiving ECMO. Methods: Consecutive adult ECMO patients who underwent a standardized neuromonitoring protocol with continuous EEG during sedation interruption were retrospectively analyzed. Quantitative EEG features and clinical variables were extracted and used to train multiple machine-learning classifiers with leave-one-subject-out cross-validation. Results: Fifty-seven patients were included (mean age 56 years; 54% male), including 41 supported with venoarterial ECMO, 15 with venovenous ECMO, and one with venoarterial-venous ECMO. ABI occurred in 21 patients (37%), of whom 70% had ischemic injury. Models incorporating qEEG achieved higher point estimates than those using clinical variables alone for ABI detection (best area under the curve (AUC) 0.769, 95% confidence interval (CI) 0.638–0.883, vs. 0.681), although the difference did not reach statistical significance. Frontal theta power and interhemispheric asymmetry were the EEG features most strongly associated with ABI. qEEG features also carried prognostic information for 30-day mortality (best AUC 0.864). Conclusions: Machine-learning analysis of continuous qEEG acquired during standardized sedation interruption may provide a noninvasive bedside approach for identifying ECMO patients at increased risk of ABI and short-term mortality and may help prioritize urgent neuroimaging and neurological intervention. Full article
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