Wavelet and time-based cerebral autoregulation analysis using diffuse correlation spectroscopy on adults undergoing extracorporeal membrane oxygenation therapy.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2024
Historique:
received: 21 02 2024
accepted: 23 09 2024
medline: 29 10 2024
pubmed: 29 10 2024
entrez: 29 10 2024
Statut: epublish

Résumé

Adult patients who have suffered acute cardiac or pulmonary failure are increasingly being treated using extracorporeal membrane oxygenation (ECMO), a cardiopulmonary bypass technique. While ECMO has improved the long-term outcomes of these patients, neurological injuries can occur from underlying illness or ECMO itself. Cerebral autoregulation (CA) allows the brain to maintain steady perfusion during changes in systemic blood pressure. Dysfunctional CA is a marker of acute brain injury and can worsen neurologic damage. Monitoring CA using invasive modalities can be risky in ECMO patients due to the necessity of anticoagulation therapy. Diffuse correlation spectroscopy (DCS) measures cerebral blood flow continuously, noninvasively, at the bedside, and can monitor CA. In this study, we compare DCS-based markers of CA in veno-arterial ECMO patients with and without acute brain injury. Adults undergoing ECMO were prospectively enrolled at a single tertiary hospital and underwent DCS and arterial blood pressure monitoring during ECMO. Neurologic injuries were identified using brain computerized tomography (CT) scans obtained in all patients. CA was calculated over a twenty-minute window via wavelet coherence analysis (WCA) over 0.05 Hz to 0.1 Hz and a Pearson correlation (DCSx) between cerebral blood flow measured by DCS and mean arterial pressure. Eleven ECMO patients who received CT neuroimaging were recruited. 5 (45%) patients were found to have neurologic injury. CA indices WCOH, the area under the curve of the WCA, were significantly higher for patients with neurological injuries compared to those without neurological injuries (right hemisphere p = 0.041, left hemisphere p = 0.041). %DCSx, percentage of time DCSx was above a threshold 0.4, were not significantly higher (right hemisphere p = 0.268, left hemisphere p = 0.073). DCS can be used to detect differences in CA for ECMO patients with neurological injuries compared to uninjured patients using WCA.

Identifiants

pubmed: 39471182
doi: 10.1371/journal.pone.0299752
pii: PONE-D-24-05125
pmc: PMC11521301
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0299752

Informations de copyright

Copyright: © 2024 Dar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Irfaan A Dar (IA)

Department of Biomedical Engineering, University of Rochester, Rochester, New York, United States of America.

Imad R Khan (IR)

Department of Neurology, University of Rochester Medical Center, Rochester, New York, United States of America.

Thomas W Johnson (TW)

Department of Neurology, University of Rochester Medical Center, Rochester, New York, United States of America.

Samantha Marie Helmy (SM)

School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, New York, United States of America.

Jeronimo I Cardona (JI)

School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, New York, United States of America.

Samantha Escobar (S)

Clinical and Translational Sciences Program, University of Rochester, Rochester, New York, United States of America.

Olga Selioutski (O)

Department of Neurology, University of Rochester Medical Center, Rochester, New York, United States of America.
Department of Neurology, University of Mississippi, Jackson, Mississippi, United States of America.

Mark A Marinescu (MA)

Department of Medicine, University of Rochester Medical Center, Rochester, New York, United States of America.

Chloe T Zhang (CT)

Department of Biomedical Engineering, University of Rochester, Rochester, New York, United States of America.

Ashley R Proctor (AR)

Department of Neurology, University of Rochester Medical Center, Rochester, New York, United States of America.

Noura AbdAllah (N)

Department of Biology, University of Rochester, Rochester, New York, United States of America.

David R Busch (DR)

Departments of Anesthesiology and Pain Management, Neurology and Biomedical Engineering, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.

Ross K Maddox (RK)

Department of Biomedical Engineering, University of Rochester, Rochester, New York, United States of America.
Department of Neuroscience, University of Rochester Medical Center, Rochester, New York, United States of America.

Regine Choe (R)

Department of Biomedical Engineering, University of Rochester, Rochester, New York, United States of America.
Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York, United States of America.

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