An enhanced cerebral recovery index for coma prognostication following cardiac arrest
Name
An Enhanced Cerebral Recovery Index for Coma Prognostication Following Cardiac Arrest.pdf
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806.01 KB
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Checksum (MD5)
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Author(s)
Amorim, Edilberto
Pati, Sandipan B.
Purdon, Patrick L.
Westover, M. Brandon
Ghassemi, Mohammad Mahdi
Mark, Roger G
Brown, Emery Neal
Date Issued
November 2015
Journal
Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Ghassemi, Mohammad M.; Amorim, Edilberto; Pati, Sandipan B.; Mark, Roger G.; Brown, Emery N.; Purdon, Patrick L. and Westover, M. Brandon. “An Enhanced Cerebral Recovery Index for Coma Prognostication Following Cardiac Arrest.” 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), August 25-29 2015, Milan, Italy, Institute of Electrical and Electronics Engineers (IEEE), November 2015
Version
Author's final manuscript
Abstract
Prognostication of coma outcomes following cardiac arrest is both qualitative and poorly understood in current practice. Existing quantitative metrics are powerful, but lack rigorous approaches to classification. This is due, in part, to a lack of available data on the population of interest. In this paper we describe a novel retrospective data set of 167 cardiac arrest patients (spanning three institutions) who received electroencephalography (EEG) monitoring. We utilized a subset of the collected data to generate features that measured the connectivity, complexity and category of EEG activity. A subset of these features was included in a logistic regression model to estimate a dichotomized cerebral performance category score at discharge. We compared the predictive performance of our method against an established EEG-based alternative, the Cerebral Recovery Index (CRI) and show that our approach more reliably classifies patient outcomes, with an average increase in AUC of 0.27.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/EMBC.2015.7318417
