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Published on: September 22, 2020
Mismatch negativity to predict subsequent awakening in deeply sedated critically ill patients
E Azabou1, B Rohaut2, R Porcher3
1Department of Physiology, Assistance Publique-Hôpitaux de Paris, Raymond-Poincaré Hospital, INSERM U 1179, University of Versailles Saint-Quentin en Yvelines, Garches, Paris, France; General Intensive Care Unit, Assistance Publique-Hôpitaux de Paris, Raymond-Poincaré Hospital, INSERM U1173, University of Versailles Saint-Quentin en Yvelines, Garches, France.
Background:
Mismatch negativity (MMN) is the neurophysiological correlate of cognitive integration of novel stimuli. Although MMN is a well-established predictor of awakening in non-sedated comatose patients, its prognostic value in deeply sedated critically ill patients remains unknown. The aim of this prospective, observational pilot study was to investigate the prognostic value of MMN for subsequent awakening in deeply sedated critically ill patients.
Methods:
MMN was recorded in 43 deeply sedated critically ill patients on Day 3 of ICU admission using a classical 'odd-ball' paradigm that delivers rare deviant sounds in a train of frequent standard sounds. Individual visual analyses and a group level analysis of recordings were performed. MMN amplitudes were then analysed according to the neurological status (awake vs not awake) at Day 28.
Results:
Median (inter-quartile range) Richmond Assessment Sedation Scale (RASS) at the time of recording was -5 (range, from -5 to -4.5). Visual detection of MMN revealed a poor inter-rater agreement [kappa=0.17, 95% confidence interval (0.07-0.26)]. On Day 28, 30 (70%) patients had regained consciousness while 13 (30%) had not. Quantitative group level analysis revealed a significantly greater MMN amplitude for patients who awakened compared with those who had not [mean (standard deviation) = -0.65 (1.4) vs 0.08 (0.17) μV, respectively; P=0.003).
Conclusions:
MMN can be observed in deeply sedated critically ill patients and could help predict subsequent awakening. However, visual analysis alone is unreliable and should be systematically completed with individual level statistics.
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