Peer-Reviewed Publication
Crit Care Explor2025;7(4):e1232.April 1, 2025Journal Article
Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning Score.
Matthew M Churpek1,2, Kyle A Carey3, Ashley Snyder4, Christopher J Winslow5, Emily Gilbert6, Nirav S Shah5, Brian W Patterson2,7, Majid Afshar1,2, Alan Weiss8, Devendra N Amin8, Deborah J Rhodes9, Dana P Edelson3,4
1Department of Medicine, University of Wisconsin-Madison, Madison, WI.
2Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI.
3Department of Medicine, University of Chicago, Chicago, IL.
4AgileMD, San Francisco, CA.
5Department of Medicine, Endeavor Health, Evanston, IL.
6Department of Medicine, Loyola University Medical Center, Chicago, IL.
7Department of Emergency Medicine, University of Wisconsin-Madison, Madison WI.
8BayCare, Clearwater, FL.
9Department of Medicine, Yale University, New Haven, CT.
Abstract
BACKGROUND: Early detection of clinical deterioration using machine-learning early warning scores may improve outcomes. However, most implemented scores were developed using logistic regression, only underwent retrospective validation, and were not tested in important subgroups.
OBJECTIVE: The objective of our multicenter retrospective and prospective observational study was to develop and prospe…
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