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Early EEG for Prognostication Under Venoarterial Extracorporeal Membrane Oxygenation
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International audience. Background/objectives: Tools for prognostication of neurologic outcome of adult patients under venoarterial ECMO (VA-ECMO) have not been thoroughly investigated. We aimed to determine whether early standard electroencephalography (stdEEG) can be used for prognostication in adults under VA-ECMO.Methods: Prospective single-center observational study conducted in two intensive care units of a university hospital, Paris, France. Early stdEEG was performed on consecutive adult patients treated with VA-ECMO for refractory cardiogenic shock or refractory cardiac arrest. The association between stdEEG findings and unfavorable outcome was investigated. The primary endpoint was 28-day mortality. The secondary endpoint was severe disability or death at 90 days, defined by a score of 4-6 on the modified Rankin scale.Results: A total of 122 patients were included, of whom 35 (29%) received cardiopulmonary resuscitation before VA-ECMO cannulation. Main stdEEG findings included low background frequency ≤ 4 Hz (n = 27, 22%) and background abnormalities, i.e., a discontinuous (n = 20, 17%) and/or an unreactive background (n = 12, 10%). Background abnormalities displayed better performances for prediction of unfavorable outcomes, as compared to clinical parameters at time of recording. An unreactive stdEEG background in combination with a background frequency ≤ 4 Hz had a false positive rate of 0% for prediction of unfavorable outcome at 28 days and 90 days, with sensitivities of 8% and 6%, respectively. After adjustment for confounders, a lower background frequency was independently associated with unfavorable outcome at 28 days (adjusted odds ratio per 1-Hz increment, 95% CI 0.71, 0.52-0.97), whereas no such independent association was observed at 90 days.Conclusion: Standard EEG abnormalities recorded at time of VA-ECMO initiation are predictive of unfavorable outcomes. However, the low sensitivity of these parameters highlights the need for a multimodal evaluation for improving management of care and prognostication.
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