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Abstract
Background
EEG-based prognostication studies in intensive care units often rely on a standard 21-electrode montage (stdEEG) requiring substantial human, technical, and financial resources. We here evaluate whether a simplified 4-frontal electrode montage (4-frontEEG) can detect EEG patterns associated with poor outcomes in adult patients under veno-arterial extracorporeal membrane oxygenation (VA-ECMO).
Methods
We conducted a reanalysis of EEG data from a prospective cohort on 118 adult patients under VA-ECMO, in whom EEG was performed on admission to intensive care. EEG patterns of interest included background rhythm, discontinuity, reactivity, and the Synek’s score. They were all reassessed by an intensivist on a 4-frontEEG montage, whose analysis was then compared to an expert’s interpretation made on stdEEG recordings. The main outcome measure was the degree of correlation between 4-frontEEG and stdEEG montages to identify EEG patterns of interest. The performance of the Synek scores calculated on 4-frontEEG and stdEEG montage to predict outcomes (i.e., 28-day mortality and 90-day Rankin score
Results
The detection of EEG patterns using 4-frontEEG was statistically similar to that of stdEEG for background rhythm (Spearman rank test, ρ = 0.66, p < 0.001), discontinuity (Cohen’s kappa,
Conclusion
In adult patients under VA-ECMO, a simplified 4-frontal electrode EEG montage interpreted by an intensivist, detected common EEG patterns associated with poor outcomes, with a performance similar to that of a standard EEG montage interpreted by expert neurophysiologists. This simplified montage could be implemented as part of a multimodal evaluation for bedside prognostication.
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Details

1 APHP, Lariboisière—Saint Louis Hospitals, Department of Anesthesiology and Intensive Care, Paris, France (GRID:grid.50550.35) (ISNI:0000 0001 2175 4109)
2 APHP, Lariboisière—Saint Louis Hospitals, Department of Anesthesiology and Intensive Care, Paris, France (GRID:grid.50550.35) (ISNI:0000 0001 2175 4109); Inserm, UMRS-942, Paris Diderot University, Paris, France (GRID:grid.50550.35)
3 Université de Paris, NeuroDiderot, Inserm, Paris, France (GRID:grid.50550.35); AP-HP, Bichat-Claude Bernard Hospital, Department of Clinical Physiology, Paris, France (GRID:grid.50550.35)
4 AP-HP, Bichat-Claude Bernard Hospital, Department of Intensive Care Medicine and Infectious Diseases, Paris, France (GRID:grid.50550.35)
5 Bichat-Claude Bernard Hospital, Department of Cardiac Surgery, Paris, France (GRID:grid.411119.d) (ISNI:0000 0000 8588 831X)
6 AP-HP, Bichat-Claude Bernard Hospital, Department of Intensive Care Medicine and Infectious Diseases, Paris, France (GRID:grid.411119.d)
7 AP-HP, Bichat-Claude Bernard Hospital, Department of Clinical Physiology, Paris, France (GRID:grid.411119.d)
8 INSERM UMR1148, Team 6, Université de Paris, Laboratory for Vascular Translational Science, Paris, France (GRID:grid.508487.6) (ISNI:0000 0004 7885 7602); APHP, Lariboisière - Saint Louis hospitals, DMU DREAM, Department of Clinical Physiology, Paris, France (GRID:grid.50550.35) (ISNI:0000 0001 2175 4109)
9 INSERM UMR1148, Team 6, Université de Paris, Laboratory for Vascular Translational Science, Paris, France (GRID:grid.508487.6) (ISNI:0000 0004 7885 7602); AP-HP, Bichat-Claude Bernard Hospital, Department of Intensive Care Medicine and Infectious Diseases, Paris, France (GRID:grid.508487.6)