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Using machine learning to predict neurologic injury in venovenous extracorporeal membrane oxygenation recipients: An ELSO Registry analysis

Authors :
Kalra, Andrew
Bachina, Preetham
Shou, Benjamin L.
Hwang, Jaeho
Barshay, Meylakh
Kulkarni, Shreyas
Sears, Isaac
Eickhoff, Carsten
Bermudez, Christian A.
Brodie, Daniel
Ventetuolo, Corey E.
Whitman, Glenn J.R.
Abbasi, Adeel
Cho, Sung-Min
Kim, Bo Soo
Hager, David
Keller, Steven P.
Bush, Errol L.
Stephens, R. Scott
Khanduja, Shivalika
Kang, Jin Kook
Chinedozi, Ifeanyi David
Darby, Zachary
Rando, Hannah J.
Brown, Trish
Kim, Jiah
Wilcox, Christopher
Leng, Albert
Geeza, Andrew
Akbar, Armaan F.
Feng, Chengyuan Alex
Zhao, David
Sussman, Marc
Mendez-Tellez, Pedro Alejandro
Sun, Philip
Capili, Karlo
Riojas, Ramon
Alejo, Diane
Stephen, Scott
Flaster, Harry
Source :
JTCVS Open; October 2024, Vol. 21 Issue: 1 p140-167, 28p
Publication Year :
2024

Abstract

Venovenous extracorporeal membrane oxygenation (VV-ECMO) is associated with acute brain injury (ABI), including central nervous system (CNS) ischemia (defined as ischemic stroke or hypoxic-ischemic brain injury [HIBI]) and intracranial hemorrhage (ICH). Data on prediction models for neurologic outcomes in VV-ECMO are limited.

Details

Language :
English
ISSN :
26662736
Volume :
21
Issue :
1
Database :
Supplemental Index
Journal :
JTCVS Open
Publication Type :
Periodical
Accession number :
ejs66810700
Full Text :
https://doi.org/10.1016/j.xjon.2024.06.013