Cataplexy response with extended-release once-nightly sodium oxybate: Post hoc responder analyses from the phase 3 REST-ON clinical trial.

Background: Once-nightly sodium oxybate (ON-SXB), an extended-release oxybate formulation, yielded significant (P < 0.001 at 6 g, 7.5 g, and 9 g) reductions in cataplexy episodes in participants in the phase 3 REST-ON clinical trial (NCT02720744). This post hoc analysis from REST-ON further characterized changes in cataplexy episodes in participants with narcolepsy type 1 (NT1). Methods: Participants with narcolepsy aged ≥16 years received ON-SXB (1 wk, 4.5 g; 2 wk, 6 g; 5 wk, 7.5 g; 5 wk, 9 g) or placebo. Percentages of participants with NT1 who had ≥25%, ≥50%, ≥75%, and 100% reductions from baseline in mean number of weekly cataplexy episodes were determined. Two-sided P values comparing ON-SXB vs placebo were calculated with Fisher exact test. Results: Participants with NT1 (ON-SXB, n = 73; placebo, n = 72; modified intent-to-treat population) had a baseline mean number of weekly cataplexy episodes of 18.9 (ON-SXB) and 19.8 (placebo). Of participants receiving the highest doses of ON-SXB (7.5 and 9 g), approximately half had a 50% reduction, one-third had a 75% reduction, and one-tenth had a 100% reduction in their cataplexy episodes vs placebo. Significantly greater proportions of participants receiving ON-SXB vs placebo had respective reductions in weekly cataplexy episodes of ≥25% at weeks 1 (4.5 g; P < 0.05), 3 (6 g; P < 0.001), 8 (7.5 g; P < 0.001), and 13 (9 g; P = 0.001). Conclusions: A significantly greater proportion of participants receiving ON-SXB vs placebo experienced reductions in weekly cataplexy episodes at all tested doses. Approximately 10% of participants taking the 2 highest ON-SXB doses had complete elimination of their cataplexy.

Sleep macro-architecture in patients with Parkinson's disease does not change during the first night of neurostimulation in a pilot study.

STUDY OBJECTIVES: A growing body of literature suggests that deep brain stimulation (DBS) to treat motor symptoms of Parkinson's disease (PD) may also ameliorate certain sleep deficits. Many foundational studies have examined the impact of stimulation on sleep following several months of therapy, leaving an open question regarding the time course for improvement. It is unknown whether sleep improvement will immediately follow onset of therapy or accrete over a prolonged period of stimulation. The objective of our study was to address this knowledge gap by assessing the impact of DBS on sleep macro-architecture during the first nights of stimulation. METHODS: Polysomnograms were recorded for three consecutive nights in 14 patients with advanced PD (10 male, 4 female; age: 53-74 years), with intermittent, unilateral subthalamic nucleus DBS on the final night or two. Sleep scoring was determined manually by a consensus of four experts. Sleep macro-architecture was objectively quantified using the percentage, latency, and mean bout length of wake after sleep onset (WASO) and on each stage of sleep (REM and NREM stages N1, N2, N3). RESULTS: Sleep was found to be highly disrupted in all nights. Sleep architecture on nights without stimulation was consistent with prior results in treatment naive patients with PD. No statistically significant difference was observed due to stimulation. CONCLUSIONS: These objective measures suggest that one night of intermittent subthreshold stimulation appears insufficient to impact sleep macro-architecture. CLINICAL TRIAL REGISTRATION: Name: Adaptive Neurostimulation to Restore Sleep in Parkinson's Disease; URL: https://clinicaltrials.gov/ct2/show/NCT04620551; Identifier: NCT04620551.

Proteomic insights into the pathophysiology of periodic limb movements and restless legs syndrome.

OBJECTIVES: We used a high-throughput assay of 5000 plasma proteins to identify biomarkers associated with periodic limb movements (PLM) and restless legs syndrome (RLS) in adults. METHODS: Participants (n = 1410) of the Stanford Technology Analytics and Genomics in Sleep (STAGES) study had blood collected, completed a sleep questionnaire, and underwent overnight polysomnography with the scoring of PLMs. An aptamer-based array (SomaScan) was used to quantify 5000 proteins in plasma. A second cohort (n = 697) that had serum assayed using a previous iteration of SomaScan (1300 proteins) was used for replication and in a combined analysis (n = 2107). A 5% false discovery rate was used to assess significance. RESULTS: Multivariate analyses in STAGES identified 68 proteins associated with the PLM index after correction for multiple testing (ie, base model). Most significantly decreased proteins were iron-related and included Hepcidin (LEAP-1), Ferritin, and Ferritin light chain. Most significantly increased proteins included RANTES, Cathepsin A, and SULT 1A3. Of 68 proteins significant in the base model, 17 were present in the 1300 panel, and 15 of 17 were replicated. The most significant proteins in the combined model were Hepcidin (LEAP-1), Cathepsin A, Ferritin, and RANTES. Exploration of proteins in RLS versus non-RLS identified Cathepsin Z, Heme oxygenase 2 (HO-2), Interleukin-17A (upregulated in the combined cohort), and Megalin (upregulated in STAGES only) although results were less significant than for proteins associated with PLM index. CONCLUSIONS: These results confirm the association of PLM with low iron status and suggest the involvement of catabolic enzymes in PLM/RLS.

Once-nightly sodium oxybate (FT218) improved symptoms of disrupted nighttime sleep in people with narcolepsy: a plain language summary.

WHAT IS THIS SUMMARY ABOUT?: This is a plain language summary of a published article in the journal CNS Drugs. Narcolepsy is a rare sleep condition. Most people with narcolepsy experience disrupted nighttime sleep and have poor quality of sleep. Sometimes these symptoms are not easily diagnosed as a symptom of narcolepsy. Sodium oxybate is an approved treatment for narcolepsy. The only version of sodium oxybate that was available until 2023 required people to take their sodium oxybate at bedtime and then again in the middle of the night. The US Food and Drug Administration (FDA for short) has approved a once-nightly bedtime dose of sodium oxybate (ON-SXB for short, also known as FT218 or LUMRYZ™) to treat symptoms of narcolepsy in adults. These symptoms are daytime sleepiness and cataplexy, which is an episode of sudden muscle weakness. The once-nightly bedtime dose of ON-SXB removes the need for a middle-of-the-night dose of sodium oxybate. The REST-ON clinical study compared ON-SXB to a placebo (a substance that contains no medicine) to determine if it was better at treating symptoms of disrupted nighttime sleep associated with narcolepsy. This summary looks at whether; ON-SXB was better than placebo at treating symptoms of disrupted nighttime sleep. WHAT WERE THE RESULTS?: Compared to people who took placebo, people who took ON-SXB had fewer number of changes from deeper to lighter sleep stages and woke up less during the night. They also reported that they slept better at night and felt more refreshed when waking up in the morning. People with narcolepsy sometimes take alerting agents to help with sleepiness during the day, but alerting agents can cause difficulty sleeping at night. This study showed that people who took ON-SXB had better nighttime sleep even if they were taking alerting agents during the day. The most common side effects of ON-SXB included dizziness, nausea (feeling sick to your stomach), vomiting, headache, and bedwetting. WHAT DO THE RESULTS MEAN?: A once-nightly bedtime dose of ON-SXB is a narcolepsy treatment option for people without the need for a middle-of-the-night dose of sodium oxybate.

Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study.

BACKGROUND: Consumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited studies have comprehensively validated the performance of widely used CSTs. Our study therefore investigated popular CSTs based on various biosignals and algorithms by assessing the agreement with polysomnography. OBJECTIVE: This study aimed to validate the accuracy of various types of CSTs through a comparison with in-lab polysomnography. Additionally, by including widely used CSTs and conducting a multicenter study with a large sample size, this study seeks to provide comprehensive insights into the performance and applicability of these CSTs for sleep monitoring in a hospital environment. METHODS: The study analyzed 11 commercially available CSTs, including 5 wearables (Google Pixel Watch, Galaxy Watch 5, Fitbit Sense 2, Apple Watch 8, and Oura Ring 3), 3 nearables (Withings Sleep Tracking Mat, Google Nest Hub 2, and Amazon Halo Rise), and 3 airables (SleepRoutine, SleepScore, and Pillow). The 11 CSTs were divided into 2 groups, ensuring maximum inclusion while avoiding interference between the CSTs within each group. Each group (comprising 8 CSTs) was also compared via polysomnography. RESULTS: The study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea. Across the 2 centers, we collected a total of 3890 hours of sleep sessions based on 11 CSTs, along with 543 hours of polysomnography recordings. Each CST sleep recording covered an average of 353 hours. We analyzed a total of 349,114 epochs from the 11 CSTs compared with polysomnography, where epoch-by-epoch agreement in sleep stage classification showed substantial performance variation. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. Various sleep trackers exhibited diverse performances across sleep stages, with SleepRoutine excelling in the wake and rapid eye movement stages, and wearables like Google Pixel Watch and Fitbit Sense 2 showing superiority in the deep stage. There was a distinct trend in sleep measure estimation according to the type of device. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. Subgroup analyses of sleep trackers revealed variations in macro F1 scores based on factors, such as BMI, sleep efficiency, and apnea-hypopnea index, while the differences between male and female subgroups were minimal. CONCLUSIONS: Our study showed that among the 11 CSTs examined, specific CSTs showed substantial agreement with polysomnography, indicating their potential application in sleep monitoring, while other CSTs were partially consistent with polysomnography. This study offers insights into the strengths of CSTs within the 3 different classes for individuals interested in wellness who wish to understand and proactively manage their own sleep.

An approach for determining the reliability of manual and digital scoring of sleep stages.

STUDY OBJECTIVES: Inter-scorer variability in sleep staging is largely due to equivocal epochs that contain features of more than one stage. We propose an approach that recognizes the existence of equivocal epochs and evaluates scorers accordingly. METHODS: Epoch-by-epoch staging was performed on 70 polysomnograms by six qualified technologists and by a digital system (Michele Sleep Scoring [MSS]). Probability that epochs assigned the same stage by only two of the six technologists (minority score) resulted from random occurrence of two errors was calculated and found to be <5%, thereby indicating that the stage assigned is an acceptable variant for the epoch. Acceptable stages were identified in each epoch as stages assigned by at least two technologists. Percent agreement between each technologist and the other five technologists, acting as judges, was determined. Agreement was considered to exist if the stage assigned by the tested scorer was one of the acceptable stages for the epoch. Stage assigned by MSS was likewise considered in agreement if included in the acceptable stages made by the technologists. RESULTS: Agreement of technologists tested against five qualified judges increased from 80.8% (range 70.5%-86.4% among technologists) when using the majority rule, to 96.1 (89.8%-98.5%) by the proposed approach. Agreement between unedited MSS and same judges was 90.0% and increased to 92.1% after brief editing. CONCLUSIONS: Accounting for equivocal epochs provides a more accurate estimate of a scorer's (human or digital) competence in scoring sleep stages and reduces inter-scorer disagreements. The proposed approach can be implemented in sleep-scoring training and accreditation programs.

Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation.

BACKGROUND: The growing public interest and awareness regarding the significance of sleep is driving the demand for sleep monitoring at home. In addition to various commercially available wearable and nearable devices, sound-based sleep staging via deep learning is emerging as a decent alternative for their convenience and potential accuracy. However, sound-based sleep staging has only been studied using in-laboratory sound data. In real-world sleep environments (homes), there is abundant background noise, in contrast to quiet, controlled environments such as laboratories. The use of sound-based sleep staging at homes has not been investigated while it is essential for practical use on a daily basis. Challenges are the lack of and the expected huge expense of acquiring a sufficient size of home data annotated with sleep stages to train a large-scale neural network. OBJECTIVE: This study aims to develop and validate a deep learning method to perform sound-based sleep staging using audio recordings achieved from various uncontrolled home environments. METHODS: To overcome the limitation of lacking home data with known sleep stages, we adopted advanced training techniques and combined home data with hospital data. The training of the model consisted of 3 components: (1) the original supervised learning using 812 pairs of hospital polysomnography (PSG) and audio recordings, and the 2 newly adopted components; (2) transfer learning from hospital to home sounds by adding 829 smartphone audio recordings at home; and (3) consistency training using augmented hospital sound data. Augmented data were created by adding 8255 home noise data to hospital audio recordings. Besides, an independent test set was built by collecting 45 pairs of overnight PSG and smartphone audio recording at homes to examine the performance of the trained model. RESULTS: The accuracy of the model was 76.2% (63.4% for wake, 64.9% for rapid-eye movement [REM], and 83.6% for non-REM) for our test set. The macro F1-score and mean per-class sensitivity were 0.714 and 0.706, respectively. The performance was robust across demographic groups such as age, gender, BMI, or sleep apnea severity (accuracy 73.4%-79.4%). In the ablation study, we evaluated the contribution of each component. While the supervised learning alone achieved accuracy of 69.2% on home sound data, adding consistency training to the supervised learning helped increase the accuracy to a larger degree (+4.3%) than adding transfer learning (+0.1%). The best performance was shown when both transfer learning and consistency training were adopted (+7.0%). CONCLUSIONS: This study shows that sound-based sleep staging is feasible for home use. By adopting 2 advanced techniques (transfer learning and consistency training) the deep learning model robustly predicts sleep stages using sounds recorded at various uncontrolled home environments, without using any special equipment but smartphones only.

Evaluation of consensus sleep stage scoring of dysregulated sleep in Parkinson's disease.

OBJECTIVE: Sleep dysregulation in Parkinson's disease (PD) has been hypothesized to occur, in part, from dysfunction in the basal ganglia-cortical circuit. Assessment of this relationship requires accurate sleep stage determination, a known challenge in this clinical population. Our objective was to optimize the consensus on the sleep staging process and reduce interrater variability in a cohort of advanced PD subjects. METHODS: Fifteen PD subjects were enrolled from three sites in a clinical trial that involved recordings from subthalamic nucleus (STN) deep brain stimulation (DBS) leads (NCT04620551). Video polysomnography (vPSG) data for a total of 45 nights were analyzed. Four experienced scorers independently scored data on initial review. Epochs with less than 75% consensus were flagged for secondary review. In secondary review of discordant epochs, two of the original scorers re-assessed epochs, from which the final consensus stage was derived. RESULTS: Sleep stage classification agreement averaged 83.10% across all sleep stages on initial scoring (IS), and on secondary consensus scoring (CS) review, agreement reached 96.58%. Greatest disagreement was noted in determination of awake epochs (33.6% of discordant epochs) and non-rapid-eye-movement stage 2 (N2) epochs (31.8% of discordant epochs). Scoring discrepancy was resolved with direct measurement of cortical frequency and amplitudes, physiologic context of the epoch, and video review. CONCLUSION: Our method of multi-level initial and then secondary consensus review scoring resulted in consensus scoring agreement superior to conventional standards. This work features a custom-engineered vPSG software and review platform for integration of consensus sleep stage scoring in a multi-site clinical trial.

Sodium oxybate in treatment-resistant rapid-eye-movement sleep behavior disorder.

STUDY OBJECTIVES: Symptomatic therapies for rapid-eye-movement (REM) sleep behavior disorder (RBD) are limited. Sodium oxybate (SXB), a gamma-aminobutyric acid (GABA)-B agonist, could be effective but has not been evaluated against placebo. METHODS: This double-blind, parallel-group, randomized, placebo-controlled trial in 24 participants was conducted at the Stanford Sleep Center. Patients were adults with definite iRBD or Parkinson's disease and probable RBD (PD-RBD), and persistence of ≥ 2 weekly episodes despite standard therapy. Patients were randomized 1:1 to receive SXB during a 4-week titration followed by a 4-week stable dosing period. Primary outcome was number of monthly RBD episodes according to a diary filled by patients and partners. Secondary outcomes were severity, number of severe RBD episodes, and objective RBD activity on video polysomnography. RESULTS: Twelve iRBD and 12 PD-RBD participated (mean 65.8 years), and 22 (n = 10 SXB, 12 placebo) completed the study. Although no significant between-group difference was found, SXB showed reduction of monthly RBD episodes by 23.1 (95% CI -36.0, -10.2; p = 0.001) versus 10.5 with placebo (95% CI, -22.6, 1.6; p = 0.087). Improvement from baseline was similarly observed for RBD overall severity burden (each episode weighted for severity), number of severe episodes, and objective RBD activity per video-polysomnography. Two participants receiving SXB withdrew due to anxiety and dizziness. The majority of adverse events are otherwise resolved with dose adjustment. CONCLUSION: SXB could reduce RBD symptoms; however, response was inconsistent and a large placebo effect was observed across patient-reported outcomes. Larger studies using objective endpoints are needed. CLINICAL TRIAL: Treatment of REM Sleep Behavior Disorder (RBD) With Sodium Oxybate https://clinicaltrials.gov/ct2/show/NCT04006925 ClinicalTrials.gov identifier: NCT04006925.

The Link between Obstructive Sleep Apnea and Neurocognitive Impairment: An Official American Thoracic Society Workshop Report.

There is emerging evidence that obstructive sleep apnea (OSA) is a risk factor for preclinical Alzheimer's disease (AD). An American Thoracic Society workshop was convened that included clinicians, basic scientists, and epidemiologists with expertise in OSA, cognition, and dementia, with the overall objectives of summarizing the state of knowledge in the field, identifying important research gaps, and identifying potential directions for future research. Although currently available cognitive screening tests may allow for identification of cognitive impairment in patients with OSA, they should be interpreted with caution. Neuroimaging in OSA can provide surrogate measures of disease chronicity, but it has methodological limitations. Most data on the impact of OSA treatment on cognition are for continuous positive airway pressure (CPAP), with limited data for other treatments. The cognitive domains improving with CPAP show considerable heterogeneity across studies. OSA can negatively influence risk, manifestations, and possibly progression of AD and other forms of dementia. Sleep-dependent memory tasks need greater incorporation into OSA testing, with better delineation of sleep fragmentation versus intermittent hypoxia effects. Plasma biomarkers may prove to be sensitive, feasible, and scalable biomarkers for use in clinical trials. There is strong biological plausibility, but insufficient data, to prove bidirectional causality of the associations between OSA and aging pathology. Engaging, recruiting, and retaining diverse populations in health care and research may help to decrease racial and ethnic disparities in OSA and AD. Key recommendations from the workshop include research aimed at underlying mechanisms; longer-term longitudinal studies with objective assessment of OSA, sensitive cognitive markers, and sleep-dependent cognitive tasks; and pragmatic study designs for interventional studies that control for other factors that may impact cognitive outcomes and use novel biomarkers.

Proteomic Biomarkers of the Apnea Hypopnea Index and Obstructive Sleep Apnea: Insights into the Pathophysiology of Presence, Severity, and Treatment Response.

Obstructive sleep apnea (OSA), a disease associated with excessive sleepiness and increased cardiovascular risk, affects an estimated 1 billion people worldwide. The present study examined proteomic biomarkers indicative of presence, severity, and treatment response in OSA. Participants (n = 1391) of the Stanford Technology Analytics and Genomics in Sleep study had blood collected and completed an overnight polysomnography for scoring the apnea−hypopnea index (AHI). A highly multiplexed aptamer-based array (SomaScan) was used to quantify 5000 proteins in all plasma samples. Two separate intervention-based cohorts with sleep apnea (n = 41) provided samples pre- and post-continuous/positive airway pressure (CPAP/PAP). Multivariate analyses identified 84 proteins (47 positively, 37 negatively) associated with AHI after correction for multiple testing. Of the top 15 features from a machine learning classifier for AHI ≥ 15 vs. AHI < 15 (Area Under the Curve (AUC) = 0.74), 8 were significant markers of both AHI and OSA from multivariate analyses. Exploration of pre- and post-intervention analysis identified 5 of the 84 proteins to be significantly decreased following CPAP/PAP treatment, with pathways involving endothelial function, blood coagulation, and inflammatory response. The present study identified PAI-1, tPA, and sE-Selectin as key biomarkers and suggests that endothelial dysfunction and increased coagulopathy are important consequences of OSA, which may explain the association with cardiovascular disease and stroke.

Is the sodium in sodium oxybate a risk for heart health? A plain language summary of publication.

WHAT IS THIS SUMMARY ABOUT?: Sodium oxybate is a medicine for narcolepsy symptoms. It contains a high level of sodium. Should people taking sodium oxybate and their doctors worry about the sodium increasing their risk of heart or cardiovascular problems? This is a summary of an article that reviewed 20 years of published data to answer that question. WHAT WERE THE RESULTS?: We found that sodium oxybate was not linked to cardiovascular risks, such as heart attacks or strokes. WHAT DO THE RESULTS MEAN?: This suggests that the sodium in sodium oxybate may not add cardiovascular risk for people with narcolepsy. People currently taking sodium oxybate should talk to their doctor to ask if they need to be concerned about the sodium in their medicine. People who take sodium oxybate are unlikely to need to change their sodium oxybate medicine because of the sodium.

A randomized controlled trial of cognitive behavioral therapy for insomnia and PAP for obstructive sleep apnea and comorbid insomnia: effects on nocturnal sleep and daytime performance.

STUDY OBJECTIVES: This study examines the impact of cognitive behavioral therapy for insomnia (CBT-I) and positive airway pressure (PAP) therapy for comorbid insomnia and sleep apnea on nocturnal sleep and daytime functioning. METHODS: A partial factorial design was used to examine treatment pathways with CBT-I and PAP and the relative benefits of each treatment. One hundred eighteen individuals with comorbid insomnia and sleep apnea were randomized to receive CBT-I followed by PAP, self-monitoring followed by CBT-I concurrent with PAP, or self-monitoring followed by PAP only. Participants were assessed at baseline, PAP titration, and 30 and 90 days after PAP initiation. Outcome measures included sleep diary- and actigraphy-measured sleep, Flinders Fatigue Scale, Epworth Sleepiness Scale, Functional Outcome of Sleep Questionnaire, and cognitive emotional measures. RESULTS: A main effect of time was found on diary-measured sleep parameters (decreased sleep onset latency and wake after sleep onset; increased total sleep time and sleep efficiency) and actigraphy-measured sleep parameters (decreased wake after sleep onset; increased sleep efficiency) and daytime functioning (reduced Epworth Sleepiness Scale, Flinders Fatigue Scale; increased Functional Outcome of Sleep Questionnaire) across all arms (all P < .05). Significant interactions and planned contrast comparisons revealed that CBT-I was superior to PAP and self-monitoring on reducing diary-measured sleep onset latency and wake after sleep onset and increasing sleep efficiency, as well as improving Functional Outcome of Sleep Questionnaire and Flinders Fatigue Scale compared to self-monitoring. CONCLUSIONS: Improvements in sleep and daytime functioning were found with PAP alone or concomitant with CBT-I. However, more rapid effects were observed on self-reported sleep and daytime performance when receiving CBT-I regardless of when it was initiated. Therefore, concomitant treatment appears to be a favorable approach to accelerate treatment outcomes. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov; Name: Multidisciplinary Approach to the Treatment of Insomnia and Comorbid Sleep Apnea (MATRICS); URL: https://clinicaltrials.gov/ct2/show/NCT01785303; Identifier: NCT01785303. CITATION: Tu AY, Crawford MR, Dawson SC, et al. A randomized controlled trial of cognitive behavioral therapy for insomnia and PAP for obstructive sleep apnea and comorbid insomnia: effects on nocturnal sleep and daytime performance. J Clin Sleep Med. 2022;18(3):789-800.

Once-nightly sodium oxybate (FT218) demonstrated improvement of symptoms in a phase 3 randomized clinical trial in patients with narcolepsy.

STUDY OBJECTIVES: To assess the efficacy and safety of FT218, a novel once-nightly formulation of sodium oxybate (ON-SXB), in patients with narcolepsy in the phase 3 REST-ON trial. METHODS: Narcolepsy patients aged ≥16 years were randomized 1:1 to uptitration of ON-SXB (4.5, 6, 7.5, and 9 g) or placebo. Three coprimary endpoints were change from baseline in mean sleep latency on the Maintenance of Wakefulness Test, Clinical Global Impression-Improvement rating, and weekly cataplexy attacks at 9, 7.5, and 6 g. Secondary endpoints included change from baseline on the Epworth Sleepiness Scale. Safety included adverse drug reactions and clinical laboratory assessments. RESULTS: In total, 222 patients were randomized; 212 received ≥1 dose of ON-SXB (n = 107) or placebo (n = 105). For the three coprimary endpoints and Epworth Sleepiness Scale, all three doses of ON-SXB demonstrated clinically meaningful, statistically significant improvement versus placebo (all p < 0.001). For ON-SXB 9 g versus placebo, increase in mean sleep latency was 10.8 versus 4.7 min (Least squares mean difference, LSMD [95% CI], 6.13 [3.52 to 8.75]), 72.0% versus 31.6% were rated much/very much improved on Clinical Global Impression-Improvement (OR [95% CI], 5.56 [2.76 to 11.23]), change in mean weekly number of cataplexy attacks was -11.5 versus -4.9 (LSMD [95% CI], -6.65 [-9.32 to -3.98]), and change in Epworth Sleepiness Scale was -6.5 and -2.7 (LSMD [95% CI], -6.52 [-5.47 to -2.26]). Common adverse reactions included nausea, vomiting, headache, dizziness, and enuresis. CONCLUSIONS: ON-SXB significantly improved narcolepsy symptoms; its safety profile was consistent with SXB. ON-SXB conferred efficacy with a clearly beneficial single nighttime dose. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov: NCT02720744, https://clinicaltrials.gov/ct2/show/NCT02720744.

Basal Ganglia Local Field Potentials as a Potential Biomarker for Sleep Disturbance in Parkinson's Disease.

Sleep disturbances, specifically decreases in total sleep time and sleep efficiency as well as increased sleep onset latency and wakefulness after sleep onset, are highly prevalent in patients with Parkinson's disease (PD). Impairment of sleep significantly and adversely impacts several comorbidities in this patient population, including cognition, mood, and quality of life. Sleep disturbances and other non-motor symptoms of PD have come to the fore as the effectiveness of advanced therapies such as deep brain stimulation (DBS) optimally manage the motor symptoms. Although some studies have suggested that DBS provides benefit for sleep disturbances in PD, the mechanisms by which this might occur, as well as the optimal stimulation parameters for treating sleep dysfunction, remain unknown. In patients treated with DBS, electrophysiologic recording from the stimulating electrode, in the form of local field potentials (LFPs), has led to the identification of several findings associated with both motor and non-motor symptoms including sleep. For example, beta frequency (13-30 Hz) oscillations are associated with worsened bradykinesia while awake and decrease during non-rapid eye movement sleep. LFP investigation of sleep has largely focused on the subthalamic nucleus (STN), though corresponding oscillatory activity has been found in the globus pallidus internus (GPi) and thalamus as well. LFPs are increasingly being recognized as a potential biomarker for sleep states in PD, which may allow for closed-loop optimization of DBS parameters to treat sleep disturbances in this population. In this review, we discuss the relationship between LFP oscillations in STN and the sleep architecture of PD patients, current trends in utilizing DBS to treat sleep disturbance, and future directions for research. In particular, we highlight the capability of novel technologies to capture and record LFP data in vivo, while patients continue therapeutic stimulation for motor symptoms. These technological advances may soon allow for real-time adaptive stimulation to treat sleep disturbances.

Estimation of Apnea-Hypopnea Index Using Deep Learning On 3-D Craniofacial Scans.

Obstructive sleep apnea (OSA) is characterized by decreased breathing events that occur through the night, with severity reported as the apnea-hypopnea index (AHI), which is associated with certain craniofacial features. In this study, we used data from 1366 patients collected as part of Stanford Technology Analytics and Genomics in Sleep (STAGES) across 11 US and Canadian sleep clinics and analyzed 3D craniofacial scans with the goal of predicting AHI, as measured using gold standard nocturnal polysomnography (PSG). First, the algorithm detects pre-specified landmarks on mesh objects and aligns scans in 3D space. Subsequently, 2D images and depth maps are generated by rendering and rotating scans by 45-degree increments. Resulting images were stacked as channels and used as input to multi-view convolutional neural networks, which were trained and validated in a supervised manner to predict AHI values derived from PSGs. The proposed model achieved a mean absolute error of 11.38 events/hour, a Pearson correlation coefficient of 0.4, and accuracy for predicting OSA of 67% using 10-fold cross-validation. The model improved further by adding patient demographics and variables from questionnaires. We also show that the model performed at the level of three sleep medicine specialists, who used clinical experience to predict AHI based on 3D scan displays. Finally, we created topographic displays of the most important facial features used by the model to predict AHI, showing importance of the neck and chin area. The proposed algorithm has potential to serve as an inexpensive and efficient screening tool for individuals with suspected OSA.