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IntroductionDelirium is a potentially preventable disorder characterised by acute disturbances in attention and cognition with fluctuating severity. Postoperative delirium is associated with prolonged intensive care unit and hospital stay, cognitive decline and mortality. The development of biomarkers for tracking delirium could potentially aid in the early detection, mitigation and assessment of response to interventions. Because sleep disruption has been posited as a contributor to the development of this syndrome, expression of abnormal electroencephalography (EEG) patterns during sleep and wakefulness may be informative. Here we hypothesise that abnormal EEG patterns of sleep and wakefulness may serve as predictive and diagnostic markers for postoperative delirium. Such abnormal EEG patterns would mechanistically link disrupted thalamocortical connectivity to this important clinical syndrome.Methods and analysisP-DROWS-E (Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography) is a 220-patient prospective observational study. Patient eligibility criteria include those who are English-speaking, age 60 years or older and undergoing elective cardiac surgery requiring cardiopulmonary bypass. EEG acquisition will occur 1–2 nights preoperatively, intraoperatively, and up to 7 days postoperatively. Concurrent with EEG recordings, two times per day postoperative Confusion Assessment Method (CAM) evaluations will quantify the presence and severity of delirium. EEG slow wave activity, sleep spindle density and peak frequency of the posterior dominant rhythm will be quantified. Linear mixed-effects models will be used to evaluate the relationships between delirium severity/duration and EEG measures as a function of time.Ethics and disseminationP-DROWS-E is approved by the ethics board at Washington University in St. Louis. Recruitment began in October 2018. Dissemination plans include presentations at scientific conferences, scientific publications and mass media.Trial registration numberNCT03291626.

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Introduction Sleep is a fundamental necessity for health and is commonly disrupted in the perioperative period. Technological improvements leveraging dry electroencephalographic (EEG) sensors have opened the door for large-scale quantitative assessments of sleep in relation to perioperative outcomes. Methods Patients utilized the Dreem (Rhythm, New York USA), a wireless EEG headband, to acquire their own preoperative nocturnal sleep records at home. Following cardiac surgery, postoperative recordings were obtained with staff assistance until postoperative night 7. Sleep records were scored as rapid eye movement (REM) and non-rapid eye movement (NREM) stages N1-N3, using modified American Academy of Sleep Medicine guidelines. Results Of 100 patients enrolled for perioperative sleep recordings, 74 patients provided 132 preoperative records; 80% were scorable with a median total sleep time (TST) of 209.8 minutes. TST was distributed as 8.3% N1, 70.6% N2, 2.1% N3 and 19% REM, consistent with expected sleep structure in geriatric populations. EEG markers for staging sleep were evaluated in the scorable records: 92% with sleep spindles, 98% with K-complexes, 69% with slow waves, 92% with sawtooth waves, and 80% with rapid eye movements. Among 26 patients with multiple preoperative sleep recordings, no significant within-subject differences in sleep structure were observed (all p > 0.05, paired Wilcoxon sign-rank test). 270 postoperative nocturnal sleep recordings were obtained from 83 patients, 70% of which were scorable. TST in scorable postoperative records was distributed as 14.9% N1, 78.6% N2, 0.9% N3 and 5.6% REM. Durations of REM and N3 sleep were significantly reduced in postoperative (POD 1-4) overnight recordings compared to preoperative measurements (Skillings–Mack test, p < 0.001 and p = 0.02 for REM and N3, respectively). Conclusion Wireless EEG devices enhance the feasibility of assaying perioperative sleep. A single night of unattended, ambulatory sleep monitoring is sufficient to establish a preoperative baseline. Multiple preoperative and postoperative sleep studies were tolerated by patients, which showed reductions of N3 and REM sleep in the early postoperative period. This study demonstrates the feasibility of using the Dreem for monitoring sleep macro- and microstructural EEG elements in the perioperative setting. Support (if any):

IntroductionElectroconvulsive therapy (ECT) is an effective intervention for patients with major depressive disorder (MDD). Despite longstanding use, the underlying mechanisms of ECT are unknown, and there are no objective prognostic biomarkers that are routinely used for ECT response. Two electroencephalographic (EEG) markers, sleep slow waves and sleep spindles, could address these needs. Both sleep microstructure EEG markers are associated with synaptic plasticity, implicated in memory consolidation, and have reduced expression in depressed individuals. We hypothesize that ECT alleviates depression through enhanced expression of sleep slow waves and sleep spindles, thereby facilitating synaptic reconfiguration in pathologic neural circuits.MethodsCorrelating ECT Response to EEG Markers (CET-REM) is a single-center, prospective, observational investigation. Wireless wearable headbands with dry EEG electrodes will be utilized for at-home unattended sleep studies to allow calculation of quantitative measures of sleep slow waves (EEG SWA, 0.5–4 Hz power) and sleep spindles (density in number/minute). High-density EEG data will be acquired during ECT to quantify seizure markers.DiscussionThis innovative study focuses on the longitudinal relationships of sleep microstructure and ECT seizure markers over the treatment course. We anticipate that the results from this study will improve our understanding of ECT.