Group-Based Trajectory Modeling of Suppression Ratio After Cardiac Arrest
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ORIGINAL ARTICLE
Group-Based Trajectory Modeling of Suppression Ratio After Cardiac Arrest Jonathan Elmer1,2 • John J. Gianakas3 • Jon C. Rittenberger2 • Maria E. Baldwin4 • John Faro2 • Cheryl Plummer5 • Lori A. Shutter1,6,7 • Christina L. Wassel8 • Clifton W. Callaway2 • Anthony Fabio3 • The Pittsburgh Post-Cardiac Arrest Service
Ó Springer Science+Business Media New York 2016
Abstract Background Existing studies of quantitative electroencephalography (qEEG) as a prognostic tool after cardiac arrest (CA) use methods that ignore the longitudinal pattern of qEEG data, resulting in significant information loss and precluding analysis of clinically important temporal trends. We tested the utility of group-based trajectory modeling (GBTM) for qEEG classification, focusing on the specific example of suppression ratio (SR). Methods We included comatose CA patients hospitalized from April 2010 to October 2014, excluding CA from
Electronic supplementary material The online version of this article (doi:10.1007/s12028-016-0263-9) contains supplementary material, which is available to authorized users. & Jonathan Elmer [email protected]
trauma or neurological catastrophe. We used PersystÒv12 to generate SR trends and used semi-quantitative methods to choose appropriate sampling and averaging strategies. We used GBTM to partition SR data into different trajectories and regression associate trajectories with outcome. We derived a multivariate logistic model using clinical variables without qEEG to predict survival, then added trajectories and/or non-longitudinal SR estimates, and assessed changes in model performance. Results Overall, 289 CA patients had C36 h of EEG yielding 10,404 h of data (mean age 57 years, 81 % arrested out-of-hospital, 33 % shockable rhythms, 31 % overall survival, 17 % discharged to home or acute rehabilitation). We identified 4 distinct SR trajectories associated with survival (62, 26, 12, and 0 %, P < 0.0001 across groups) and CPC (35, 10, 4, and 0 %, P < 0.0001 across groups). Adding trajectories significantly improved model performance compared to adding non-longitudinal data. Conclusions Longitudinal analysis of continuous qEEG data using GBTM provides more predictive information than analysis of qEEG at single time-points after CA.
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Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
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Department of Emergency Medicine, University of Pittsburgh, Iroquois Building, Suite 400A, 3600 Forbes Avenue, Pittsburgh, PA 15213, USA
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Epidemiology Data Center, Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
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Department of Neurology, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA
5
Division of Clinical Neurophysiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
Introduction
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Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
7
Department of Neurosurgery, University of Pittsburgh, Pittsburgh, PA, USA
8
Department of Pathology and Laboratory Medicine, College of Medicine, University of Ve
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