Twice is nice? Test-retest reliability of the Multiple Sleep Latency Test in the central disorders of hypersomnolence

Narcolepsy Severity Scale-2 and Idiopathic Hypersomnia Severity Scale to better quantify symptoms severity and consequences in Narcolepsy type 2

STUDY OBJECTIVES

Narcolepsy type 2 (NT2) is an understudied central disorder of hypersomnolence sharing some similarities with narcolepsy type 1 and idiopathic hypersomnia (IH). We aimed: (1) to assess systematically the symptoms in patients with NT2, with self-reported questionnaires: Epworth Sleepiness Scale (ESS), Narcolepsy Severity Scale (NSS), IH Severity Scale (IHSS), and (2) to evaluate the responsiveness of these scales to treatment.

METHODS

One hundred and nine patients with NT2 (31.4 ± 12.2 years old, 47 untreated) diagnosed according to ICSD-3 were selected in a Reference Center for Narcolepsy. They all completed the ESS, subgroups completed the modified NSS (NSS-2, without cataplexy items) (n = 95) and IHSS (n = 76). Some patients completed the scales twice (before/during treatment): 42 ESS, 26 NSS-2, and 30 IHSS.

RESULTS

Based on NSS-2, all untreated patients had sleepiness, 58% disrupted nocturnal sleep, 40% hallucinations, and 28% sleep paralysis. On IHSS, 76% reported a prolonged nocturnal sleep, and 83% sleep inertia. In the independent sample, ESS and NSS-2 scores were lower in treated patients, with same trend for IHSS scores. After treatment, ESS, NSS-2, and IHSS total scores were lower, with a mean difference of 3.7 ± 4.1, 5.3 ± 6.7, and 4.1 ± 6.2, respectively. The minimum clinically important difference between untreated and treated patients were 2.1 for ESS, 3.3 for NSS-2, and 3.1 for IHSS. After treatment, 61.9% of patients decreased their ESS > 2 points, 61.5% their NSS-2 > 3 points, and 53.3% their IHSS > 3 points.

CONCLUSIONS

NSS-2 and IHSS correctly quantified symptoms' severity and consequences in NT2, with good performances to objectify response to medications. These tools are useful for monitoring and optimizing NT2 management, and for use in clinical trials.

Artificial Intelligence Models for the Automation of Standard Diagnostics in Sleep Medicine-A Systematic Review

Sleep disorders, prevalent in the general population, present significant health challenges. The current diagnostic approach, based on a manual analysis of overnight polysomnograms (PSGs), is costly and time-consuming. Artificial intelligence has emerged as a promising tool in this context, offering a more accessible and personalized approach to diagnosis, particularly beneficial for under-served populations. This is a systematic review of AI-based models for sleep disorder diagnostics that were trained, validated, and tested on diverse clinical datasets. An extensive search of PubMed and IEEE databases yielded 2114 articles, but only 18 met our stringent selection criteria, underscoring the scarcity of thoroughly validated AI models in sleep medicine. The findings emphasize the necessity of a rigorous validation of AI models on multimodal clinical data, a step crucial for their integration into clinical practice. This would be in line with the American Academy of Sleep Medicine's support of AI research.

Narcolepsies, update in 2023

Narcolepsy type 1 (NT1) and type 2 (NT2), also known as narcolepsy with and without cataplexy, are sleep disorders that benefited from major scientific advances over the last two decades. NT1 is caused by the loss of hypothalamic neurons producing orexin/hypocretin, a neurotransmitter regulating sleep and wake, which can be measured in the cerebrospinal fluid (CSF). A low CSF level of hypocretin-1/orexin-A is a highly specific and sensitive biomarker, sufficient to diagnose NT1. Orexin-deficiency is responsible for the main NT1 symptoms: sleepiness, cataplexy, disrupted nocturnal sleep, sleep-related hallucinations, and sleep paralysis. In the absence of a lumbar puncture, the diagnosis is based on neurophysiological tests (nocturnal and diurnal) and the presence of the pathognomonic symptom cataplexy. In the revised version of the International Classification of sleep Disorders, 3rd edition (ICSD-3-TR), a sleep onset rapid eye movement sleep (REM) period (SOREMP) (i.e. rapid occurrence of REM sleep) during the previous polysomnography may replace the diurnal multiple sleep latency test, when clear-cut cataplexy is present. A nocturnal SOREMP is very specific but not sensitive enough, and the diagnosis of cataplexy is usually based on clinical interview. It is thus of crucial importance to define typical versus atypical cataplectic attacks, and a list of clinical features and related degrees of certainty is proposed in this paper (expert opinion). The time frame of at least three months of evolution of sleepiness to diagnose NT1 was removed in the ICSD-3-TR, when clear-cut cataplexy or orexin-deficiency are established. However, it was kept for NT2 diagnosis, a less well-characterized disorder with unknown clinical course and absence of biolo biomarkers; sleep deprivation, shift working and substances intake being major differential diagnoses. Treatment of narcolepsy is nowadays only symptomatic, but the upcoming arrival of non-peptide orexin receptor-2 agonists should be a revolution in the management of these rare sleep diseases.

Novel Objective Measures of Hypersomnolence

Purpose of review

To provide a brief overview of current objective measures of hypersomnolence, discuss proposed measure modifications, and review emerging measures.

Recent findings

There is potential to optimize current tools using novel metrics. High-density and quantitative EEG-based measures may provide discriminative informative. Cognitive testing may quantify cognitive dysfunction common to hypersomnia disorders, particularly in attention, and objectively measure pathologic sleep inertia. Structural and functional neuroimaging studies in narcolepsy type 1 have shown considerable variability but so far implicate both hypothalamic and extra-hypothalamic regions; fewer studies of other CDH have been performed. There is recent renewed interest in pupillometry as a measure of alertness in the evaluation of hypersomnolence.

Summary

No single test captures the full spectrum of disorders and use of multiple measures will likely improve diagnostic precision. Research is needed to identify novel measures and disease-specific biomarkers, and to define combinations of measures optimal for CDH diagnosis.

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective

BACKGROUND

The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI.

METHOD

The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature.

RESULTS

Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice.

CONCLUSION

Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.

The Psychomotor Vigilance Test as a measure of alertness and sleep inertia in people with central disorders of hypersomnolence

STUDY OBJECTIVES

The central disorders of hypersomnolence (CDH) manifest with daytime sleepiness, often accompanied by cognitive symptoms. Objective tests characterizing cognitive dysfunction may have diagnostic utility. Further, because some people with CDH report worsening cognition upon awakening, cognitive testing before and after napping may provide additional diagnostic information.

METHODS

Patients with CDH with idiopathic hypersomnia (n = 76), narcolepsy type 1 (n = 19), narcolepsy type 2 (n = 22), and self-reported excessive daytime sleepiness not meeting current diagnostic criteria (n = 76) and nonsleepy controls (n = 33) underwent testing with the Psychomotor Vigilance Test (PVT), a 10-minute reaction-time test. A subset of participants underwent repeat testing during a Multiple Sleep Latency Test, before and immediately after naps 2 and 4.

RESULTS

Most PVT metrics were significantly better in controls than in patients with CDH. Minimal group differences in PVT performance were observed by CDH diagnosis. PVT performance was weakly correlated to Epworth Sleepiness Scale and Multiple Sleep Latency Test mean sleep latency in the CDH group. Before and after naps, PVT metrics were minimally different for controls, while PVT performance generally worsened following naps in the CDH group, with significant worsening compared with controls for nap 2 mean, median, lapses, and fastest 10% of responses and nap 4 lapses and slowest 10% of responses. Change in performance did not differ based on CDH diagnostic group for any metric on either nap.

CONCLUSIONS

The PVT, at baseline and following a short nap, may provide adjunctive diagnostic utility in separating individuals with CDH from controls.

CITATION

Trotti LM, Saini P, Bremer E, et al. The Psychomotor Vigilance Test as a measure of alertness and sleep inertia in people with central disorders of hypersomnolence. J Clin Sleep Med. 2022;18(5):1395-1403.

Reliability and Efficacy of the Epworth Sleepiness Scale: Is There Still a Place for It?

The Epworth sleepiness scale (ESS) is a commonly used questionnaire to evaluate patients for excessive daytime sleepiness (EDS). The ESS has been validated as a measure of EDS, but a number of studies have shown more test-retest variability in clinical settings compared to the original validation study. This observation of higher-than-expected test-retest variability has called into question the utility of the ESS as a clinical tool to assess EDS. The purpose of this review article is to summarize how studies of test-retest variability in clinical populations compare to the original validation study of Johns and to highlight where they differ. Furthermore, use of the ESS as a continuous variable (with no specified cutoff value) versus a categorical variable (normal versus high) is described. These observations are put into a clinical context by comparing the test-retest variability observed on the ESS with that of the multiple sleep latency test (MSLT). Finally, how contributors to ESS scores differ within certain subpopulations is described. The ESS remains an important tool to measure EDS in patient populations, but an awareness of its limitations needs to be considered.