Identifying time-resolved features of nocturnal sleep characteristics of narcolepsy using machine learning.

The differential diagnosis of narcolepsy type 1, a rare, chronic, central disorder of hypersomnolence, is challenging due to overlapping symptoms with other hypersomnolence disorders. While recent years have seen significant growth in our understanding of nocturnal polysomnography narcolepsy type 1 features, there remains a need for improving methods to differentiate narcolepsy type 1 nighttime sleep features from those of individuals without narcolepsy type 1. We aimed to develop a machine learning framework for identifying sleep features to discriminate narcolepsy type 1 from clinical controls, narcolepsy type 2 and idiopathic hypersomnia. The population included polysomnography data from 350 drug-free individuals (114 narcolepsy type 1, 90 narcolepsy type 2, 105 idiopathic hypersomnia, and 41 clinical controls) collected at the National Reference Centers for Narcolepsy in Montpelier, France. Several sets of nocturnal sleep features were explored, as well as the value of time-resolving sleep architecture by analysing sleep per quarter-night. Several patterns of nighttime sleep evolution emerged that differed between narcolepsy type 1, clinical controls, narcolepsy type 2 and idiopathic hypersomnia, with increased nighttime instability observed in patients with narcolepsy type 1. Using machine learning models, we identified rapid eye movement sleep onset as the best single polysomnography feature to distinguish narcolepsy type 1 from controls, narcolepsy type 2 and idiopathic hypersomnia. By combining multiple feature sets capturing different aspects of sleep across quarter-night periods, we were able to further improve between-group discrimination and could identify the most discriminative sleep features. Our results highlight salient polysomnography features and the relevance of assessing their time-dependent changes during sleep that could aid diagnosis and measure the impact of novel therapeutics in future clinical trials.

Effects of oxybate dose and regimen on disrupted nighttime sleep and sleep architecture.

Many components of sleep are disrupted in patients with narcolepsy, including sleep quality, sleep architecture, and sleep stability (ie, frequent awakenings/arousals and frequent shifts from deeper to lighter stages of sleep). Sodium oxybate, dosed twice nightly, has historically been used to improve sleep, and subsequent daytime symptoms, in patients with narcolepsy. Recently, new formulations have been developed to address the high sodium content and twice-nightly dosing regimen of sodium oxybate: low-sodium oxybate and once-nightly sodium oxybate. To date, no head-to-head trials have been conducted to compare the effects of each oxybate product. This review aims to give an overview of the existing scientific literature regarding the impact of oxybate dose and regimen on sleep architecture and disrupted nighttime sleep in patients with narcolepsy. Evidence from 5 key clinical trials, as well as supporting evidence from additional studies, suggests that sodium oxybate, dosed once- and twice-nightly, is effective in improving sleep, measures of sleep architecture, and disrupted nighttime sleep in patients with narcolepsy. Direct comparison of available efficacy and safety data between oxybate products is complicated by differences in trial designs, outcomes assessed, and statistical analyses; future head-to-head trials are needed to better understand the advantage and disadvantages of each agent.