Dexmedetomidine-induced deep sedation mimics non-rapid eye movement stage 3 sleep: large-scale validation using machine learning

Abstract

Study objectives Dexmedetomidine induced electroencephalogram (EEG) patterns during deep sedation is comparable with natural sleep patterns. Using large scale EEG recordings and machine learning techniques, we investigated whether dexmedetomidine induced deep sedation indeed mimics natural sleep patterns. Methods We used EEG recordings from three sources in this study: 8707 overnight sleep EEG and 30 dexmedetomidine clinical trial EEG. Dexmedetomidine induced sedation levels were assessed using the Modified Observer’s Assessment of Alertness/ Sedation (MOAA/S) score. We extracted twenty-two spectral features from each EEG recording using a multitaper spectral estimation method. Elastic-net regularization method was used for feature selection. We compared the performance of several machine learning algorithms (logistic regression, support vector machine and random forest), trained on individual sleep stages, to predict different levels of the MOAA/S sedation state. Results The random forest algorithm trained on non-rapid eye movement stage 3 (N3) predicted dexmedetomidine induced deep sedation (MOAA/S = 0) with AUC > 0.8 outperforming other machine learning models. Power in the delta band (0-4Hz) was selected as an important feature for prediction in addition to power in theta (4-8 Hz) and beta (16-30Hz) bands. Conclusions Using a large scale EEG data-driven approach and machine learning framework, we show that dexmedetomidine induced deep sedation state mimics N3 sleep EEG patterns.

Intranasal Dexmedetomidine for the Treatment of Pre-operative Anxiety and Insomnia: A Prospective, Randomized, Controlled, and Clinical Trial

“…DEX exerts its hypnotic effect by selectively activating presynaptic and postsynaptic central α-2 adrenergic in locus coeruleus. DEX-induced deep sedation mimics stages 2 and 3 of NREM sleep ( 21 , 22 ).…”

“…Although no previous study has investigated the effectiveness of intranasal DEX for adult patients with insomnia, there is a report of a case of successful long-term home use of intranasal DEX for insomnia in pediatric palliative care of a 10-year-old women with dystrophic epidermolysis bullosa and severe sleep disorders, where treatment resulted in an increase in sleep duration from 2 to 3 consecutive hours to 6–8 consecutive hours ( 20 ). Studies suggested that DEX-induced deep sedation mimics stages 2 and 3 of non-rapid eye movement (NREM) sleep ( 21 , 22 ). In particular, it does not cause respiratory depression ( 23 ).…”

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

“…and pharmacokinetic features of drugs to provide the optimal drug dosage. The role of pharmacological robots and even more intelligent autonomous systems (i.e., cognitive robot, which can recognize crucial clinical state that requires human intervention) in the anesthesia field has been comprehensively overviewed by Cédrick et al 432 Besides the robotic systems, ML applications assisted the clinicians 433 to monitor the drug‐specific anesthetic states 434–436 and predict the adverse outcomes in anesthesia patients 437–439 …”

A randomized placebo-controlled double-blind study of dexmedetomidine on postoperative sleep quality in patients with endoscopic sinus surgery

“…Dexmedetomidine (Dex), as a highly selective α2 adrenergic receptor agonist, has inhibition on postoperative immunosuppression and implementation on a sedative, analgesic, and anti-anxiety effects by acting on the peripheral and central nervous system [ 15 ]. Dexmedetomidine induces deep sedation simulation N3 sleep EEG patterns mimicking natural sleep [ 16 ] and could increase non-REM 2 sleep, decreased REM sleep [ 17 ]. In vivo, dexmedetomidine can effectively alleviate postoperative hippocampal inflammation, improve cognitive function or increase splenic TFF2 expression in sleep deprivation rats [ 18 , 19 ].…”