EEG for good outcome prediction after cardiac arrest: a multicentre cohort study.

AIM: Assess the prognostic ability of a non-highly malignant and reactive EEG to predict good outcome after cardiac arrest (CA). METHODS: Prospective observational multicentre substudy of the "Targeted Hypothermia versus Targeted Normothermia after Out-of-hospital Cardiac Arrest Trial", also known as the TTM2-trial. Presence or absence of highly malignant EEG patterns and EEG reactivity to external stimuli were prospectively assessed and reported by the trial sites. Highly malignant patterns were defined as burst-suppression or suppression with or without superimposed periodic discharges. Multimodal prognostication was performed 96 hours after CA. Good outcome at 6 months was defined as a modified Rankin Scale score of 0-3. RESULTS: 873 comatose patients at 59 sites had an EEG assessment during the hospital stay. Of these, 283 (32%) had good outcome. EEG was recorded at a median of 69 hours (IQR 47-91) after CA. Absence of highly malignant EEG patterns was seen in 543 patients of whom 255 (29% of the cohort) had preserved EEG reactivity. A non-highly malignant and reactive EEG had 56% (CI 50-61) sensitivity and 83% (CI 80-86) specificity to predict good outcome. Presence of EEG reactivity contributed (p<0.001) to the specificity of EEG to predict good outcome compared to only assessing background pattern without taking reactivity into account. CONCLUSION: Nearly one-third of comatose patients resuscitated after CA had a non-highly malignant and reactive EEG that was associated with a good long-term outcome. Reactivity testing should be routinely performed since preserved EEG reactivity contributed to prognostic performance.

Detection of EEG burst-suppression in neurocritical care patients using an unsupervised machine learning algorithm.

OBJECTIVE: The burst suppression pattern in clinical electroencephalographic (EEG) recordings is an important diagnostic tool because of its association with comas of various etiologies, as with hypoxia, drug related intoxication or deep anesthesia. The detection of bursts and the calculation of burst/suppression ratio are often used to monitor the level of anesthesia during treatment of status epilepticus. However, manual counting of bursts is a laborious process open to inter-rater variation and motivates a need for automatic detection. METHODS: We describe a novel unsupervised learning algorithm that detects bursts in EEG and generates burst-per-minute estimates for the purpose of monitoring sedation level in an intensive care unit (ICU). We validated the algorithm on 29 hours of burst annotated EEG data from 29 patients suffering from status epilepticus and hemorrhage. RESULTS: We report competitive results in comparison to neural networks learned via supervised learning. The mean absolute error (SD) in bursts per minute was 0.93 (1.38). CONCLUSION: We present a novel burst suppression detection algorithm that adapts to each patient individually, reports bursts-per-minute quickly, and does not require manual fine-tuning unlike previous approaches to burst-suppression pattern detection. SIGNIFICANCE: Our algorithm for automatic burst suppression quantification can greatly reduce manual oversight in depth of sedation monitoring.

Sleep deprivation before stroke is neuroprotective: a pre-ischemic conditioning related to sleep rebound.

BACKGROUND AND AIM: We have previously shown in a rat model of focal cerebral ischemia that sleep deprivation after stroke onset aggravates brain damage. Others reported that sleep deprivation prior to stroke is neuroprotective. The main aim of this study was to test the hypothesis that the neuroprotection may be related to an increase in sleep (sleep rebound) during the acute phase of stroke. METHODS: Male Sprague Dawley rats (n=36) were subjected to continuous polygraphic recordings for baseline, total sleep deprivation (TSD), and 24h after ischemia. TSD for 6h was performed by gentle handling and immediately followed by ischemia. Focal cerebral ischemia was induced by permanent occlusion of distal branches of the middle cerebral artery. Control experiments included ischemia without SD (nSD) and sham surgery with TSD (n=6/group). RESULTS: Shortly after stroke, the amount of slow wave sleep (SWS) and paradoxical sleep (PS) increased significantly (p<0.05) in the TSD/ischemia, resulting in an increase in the total sleep time by 30% compared to baseline, or by 20% compared with the nSD/ischemia group. The infarct volume decreased significantly by 50% in the TSD/ischemia compared to nSD group (p<0.02). Removal of sleep rebound by allowing TSD-rats sleep for 24h before ischemia eliminated the reduction in the infarct size. CONCLUSION PRESTROKE: Sleep deprivation results in sleep rebound and reduces brain damage. Sleep rebound may be causally related to the neuroprotection.