Acute brain injury risk prediction models in venoarterial extracorporeal membrane oxygenation patients with tree-based machine learning: An Extracorporeal Life Support Organization Registry analysisCentral MessagePerspective

Objective: We aimed to determine if machine learning can predict acute brain injury and to identify modifiable risk factors for acute brain injury in patients receiving venoarterial extracorporeal membrane oxygenation. Methods: We included adults (age ≥18 years) receiving venoarterial extracorporeal...

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Bibliographic Details

Published in:	JTCVS Open
Main Authors:	Andrew Kalra, BS, Preetham Bachina, BS, Benjamin L. Shou, BS, Jaeho Hwang, MD, MPH, Meylakh Barshay, BS, Shreyas Kulkarni, BS, Isaac Sears, BS, Carsten Eickhoff, PhD, Christian A. Bermudez, MD, Daniel Brodie, MD, Corey E. Ventetuolo, MD, MS, Bo Soo Kim, MD, Glenn J.R. Whitman, MD, Adeel Abbasi, MD, ScM, Sung-Min Cho, DO, MHS, Bo Soo Kim, David Hager, Steven P. Keller, Errol L. Bush, R. Scott Stephens, Shivalika Khanduja, Jin Kook Kang, Ifeanyi David Chinedozi, Zachary Darby, Hannah J. Rando, Trish Brown, Jiah Kim, Christopher Wilcox, Albert Leng, Andrew Geeza, Armaan F. Akbar, Chengyuan Alex Feng, David Zhao, Marc Sussman, Pedro Alejandro Mendez-Tellez, Philip Sun, Karlo Capili, Ramon Riojas, Diane Alejo, Scott Stephen, Harry Flaster
Format:	Article
Language:	English
Published:	Elsevier 2024-08-01
Subjects:	acute brain injury Extracorporeal Life Support Organization extracorporeal membrane oxygenation machine learning neurological complications
Online Access:	http://www.sciencedirect.com/science/article/pii/S266627362400158X