Publications by authors named "Emmanuel Mignot"

245 Publications

Living to Dream-Reply.

JAMA Neurol 2021 Mar 1. Epub 2021 Mar 1.

Stanford University, Palo Alto, California.

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http://dx.doi.org/10.1001/jamaneurol.2021.0056DOI Listing
March 2021

Acoustic stimulation time-locked to the beginning of sleep apnea events reduces oxygen desaturations: a pilot-study.

Sleep Med 2021 Feb 11;78:38-42. Epub 2020 Dec 11.

Center for Investigation and Research in Sleep, University Hospital of Lausanne (CHUV), Lausanne, Switzerland.

Study Objectives: We aimed to determine whether bone-conducted acoustic stimulation could prematurely terminate sleep apnea events, thereby decreasing amplitude and duration of subsequent oxygen desaturation. As oxygen desaturation has been linked to cardiovascular consequences, we postulate this could be a viable therapy in some cases.

Methods: Eight patients with severe Obstructive Sleep Apnea (2 women, 45 [20-68] y.o. Apnea-Hypopnea Index: 77.7 ± 22.3/h) underwent polysomnography at the Lausanne University Sleep Center. Short acoustic stimulations were administered by bone conduction every second event of sleep apnea. Sounds were remotely administered using a Dreem® headband worn by patients while undergoing nocturnal polysomnography. Amplitude (%) and duration(s) of oxygen desaturations following terminated apneas were compared to that of non-stimulated previous and subsequent events.

Results: 549 stimulations (68.6 ± 38 sounds per patient) in N1 (16.2%), N2 (69.9%), N3 (4.2%), and REM(9.6%) were conducted. Compared to the previous and subsequent non-stimulated apnea, stimulations reduced event duration by 21.4% (-3.4 ± 7.2 s, p < 0.0001) while oxygen desaturation amplitude and duration were reduced by 30.4% (mean absolute difference ± SD: -1.9 ± 2.8%, p < 0.0001), and 39.6% (-5.7 ± 9.2 s, p < 0.0001) respectively. For these variables, each patient showed a significant improvement following acoustic stimulation. Sound-associated discomfort was rated 1.14 ± 1.53 on an 8 points scale (8 = worst) and only 6.8% of emitted sounds were perceived by the patients, suggesting a well-tolerated intervention.

Conclusions: Bone-conducted sound stimuli decreased apnea events duration as well as duration and amplitude of associated oxygen desaturations. Stimulations were well tolerated and rarely perceived by patients. This therapeutic approach deserves further investigation, with monitoring of effects on sleep quality, daytime function/sleepiness and cardiovascular parameters.
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http://dx.doi.org/10.1016/j.sleep.2020.12.006DOI Listing
February 2021

Cortical Arousal Frequency is Increased in Narcolepsy Type 1.

Sleep 2020 Nov 29. Epub 2020 Nov 29.

Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Denmark.

Study Objectives: Hypocretin deficient narcolepsy (type 1, NT1) presents with multiple sleep abnormalities including sleep onset rapid eye movement (REM) periods (SOREMPs) and sleep fragmentation. We hypothesized that cortical arousals, as scored by an automatic detector, are elevated in NT1 and narcolepsy type 2 (NT2) patients as compared to control subjects.

Methods: We analyzed nocturnal polysomnography (PSG) recordings from 25 NT1 patients, 20 NT2 patients, 18 clinical control subjects (CC, suspected central hypersomnia but with normal cerebrospinal (CSF) fluid hypocretin-1 (hcrt-1) levels and normal results on the multiple sleep latency test), and 37 healthy control (HC) subjects. Arousals were automatically scored using Multimodal Arousal Detector (MAD), a previously validated automatic wakefulness and arousal detector. Multiple linear regressions were used to compare arousal index (ArI) distributions across groups. Comparisons were corrected for age, sex, body-mass index, medication, apnea-hypopnea index, periodic leg movement index, and comorbid rapid eye movement sleep behavior disorder.

Results: NT1 was associated with an average increase in ArI of 4.02 events/hour (p = 0.0246) compared to HC and CC, while no difference was found between NT2 and control groups. Additionally, a low CSF hcrt-1 level was predictive of increased ArI in all the CC, NT2, and NT1 groups.

Conclusions: The results further support the hypothesis that a loss of hypocretin neurons causes fragmented sleep, which can be measured as an increased ArI as scored by the MAD.
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http://dx.doi.org/10.1093/sleep/zsaa255DOI Listing
November 2020

Mass Spectrometric Characterization of Narcolepsy-Associated Pandemic 2009 Influenza Vaccines.

Vaccines (Basel) 2020 Oct 30;8(4). Epub 2020 Oct 30.

Stanford Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Sciences, Stanford University, 3165 Porter Drive, Stanford, CA 94304, USA.

The onset of narcolepsy, an irreversible sleep disorder, has been associated with 2009 influenza pandemic (pH1N1) infections in China, and with ASO3-adjuvanted pH1N1 vaccinations using Pandemrix in Europe. Intriguingly, however, the increased incidence was only observed following vaccination with Pandemrix but not Arepanrix in Canada. In this study, the mutational burden of actual vaccine lots of Pandemrix (n = 6) and Arepanrix (n = 5) sourced from Canada, and Northern Europe were characterized by mass spectrometry. The four most abundant influenza proteins across both vaccines were nucleoprotein NP, hemagglutinin HA, matrix protein M1, with the exception that Pandemrix harbored a significantly increased proportion of neuraminidase NA (7.5%) as compared to Arepanrix (2.6%). Most significantly, 17 motifs in HA, NP, and M1 harbored mutations, which significantly differed in Pandemrix versus Arepanrix. Among these, a 6-fold higher deamidation of HA146 (p.Asn146Asp) in Arepanrix was found relative to Pandemrix, while NP257 (p.Thr257Ala) and NP424 (p.Thr424Ile) were increased in Pandemrix. DQ0602 binding and tetramer analysis with mutated epitopes were conducted in Pandemrix-vaccinated cases versus controls but were unremarkable. Pandemrix harbored lower mutational burden than Arepanrix, indicating higher similarity to wild-type 2009 pH1N1, which could explain differences in narcolepsy susceptibility amongst the vaccines.
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http://dx.doi.org/10.3390/vaccines8040630DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712488PMC
October 2020

Prediction of Patient Demographics using 3D Craniofacial Scans and Multi-view CNNs.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:1950-1953

3D data is becoming increasingly popular and accessible for computer vision tasks. A popular format for 3D data is the mesh format, which can depict a 3D surface accurately and cost-effectively by connecting points in the (x, y, z) plane, known as vertices, into triangles that can be combined to approximate geometrical surfaces. However, mesh objects are not suitable for standard deep learning techniques due to their non-euclidean structure. We present an algorithm which predicts the sex, age, and body mass index of a subject based on a 3D scan of their face and neck. This algorithm relies on an automatic pre-processing technique, which renders and captures the 3D scan from eight different angles around the x-axis in the form of 2D images and depth maps. Subsequently, the generated data is used to train three convolutional neural networks, each with a ResNet18 architecture, to learn a mapping between the set of 16 images per subject (eight 2D images and eight depth maps from different angles) and their demographics. For age and body mass index, we achieved a mean absolute error of 7.77 years and 4.04 kg/m on the respective test sets, while Pearson correlation coefficients of 0.76 and 0.80 were obtained, respectively. The prediction of sex yielded an accuracy of 93%. The developed framework serves as a proof of concept for prediction of more clinically relevant variables based on 3D craniofacial scans stored in mesh objects.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176333DOI Listing
July 2020

Predicting Age with Deep Neural Networks from Polysomnograms.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:146-149

The aim of this study was to design a new deep learning framework for end-to-end processing of polysomnograms. This framework can be trained to analyze whole-night polysomnograms without the limitations of and bias towards clinical scoring guidelines. We validated the framework by predicting the age of subjects. We designed a hierarchical attention network architecture, which can be pre-trained to predict labels based on 5-minute epochs of data and fine-tuned to predict based on whole-night polysomnography recordings. The model was trained on 511 recordings from the Cleveland Family study and tested on 146 test subjects aged between 6 to 88 years. The proposed network achieved a mean absolute error of 7.36 years and a correlation to true age of 0.857. Sleep can be analyzed using our end-to-end deep learning framework, which we expect can generalize to learning other subject-specific labels such as sleep disorders. The difference in the predicted and chronological age is further proposed as an estimate of biological age.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176254DOI Listing
July 2020

Deep transfer learning for improving single-EEG arousal detection.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:99-103

Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two deep transfer learning strategies for overcoming the channel mismatch problem for cases where two datasets do not contain exactly the same setup leading to degraded performance in single-EEG models. Specifically, we train a baseline model on multivariate polysomnography data and subsequently replace the first two layers to prepare the architecture for single-channel electroencephalography data. Using a fine-tuning strategy, our model yields similar performance to the baseline model (F1=0.682 and F1=0.694, respectively), and was significantly better than a comparable single-channel model. Our results are promising for researchers working with small databases who wish to use deep learning models pre-trained on larger databases.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176723DOI Listing
July 2020

PSG Validation of minute-to-minute scoring for sleep and wake periods in a consumer wearable device.

PLoS One 2020 17;15(9):e0238464. Epub 2020 Sep 17.

Stanford University Center for Sleep Sciences and Medicine, Palo Alto, California, United States of America.

Background: Actigraphs are wrist-worn devices that record tri-axial accelerometry data used clinically and in research studies. The expense of research-grade actigraphs, however, limit their widespread adoption, especially in clinical settings. Tri-axial accelerometer-based consumer wearable devices have gained worldwide popularity and hold potential for a cost-effective alternative. The lack of independent validation of minute-to-minute accelerometer data with polysomnographic data or even research-grade actigraphs, as well as access to raw data has hindered the utility and acceptance of consumer-grade actigraphs.

Methods: Sleep clinic patients wore a consumer-grade wearable (Huami Arc) on their non-dominant wrist while undergoing an overnight polysomnography (PSG) study. The sample was split into two, 20 in a training group and 21 in a testing group. In addition to the Arc, the testing group also wore a research-grade actigraph (Philips Actiwatch Spectrum). Sleep was scored for each 60-s epoch on both devices using the Cole-Kripke algorithm.

Results: Based on analysis of our training group, Arc and PSG data were aligned best when a threshold of 10 units was used to examine the Arc data. Using this threshold value in our testing group, the Arc has an accuracy of 90.3%±4.3%, sleep sensitivity (or wake specificity) of 95.5%±3.5%, and sleep specificity (wake sensitivity) of 55.6%±22.7%. Compared to PSG, Actiwatch has an accuracy of 88.7%±4.5%, sleep sensitivity of 92.6%±5.2%, and sleep specificity of 60.5%±20.2%, comparable to that observed in the Arc.

Conclusions: An optimized sleep/wake threshold value was identified for a consumer-grade wearable Arc trained by PSG data. By applying this sleep/wake threshold value for Arc generated accelerometer data, when compared to PSG, sleep and wake estimates were adequate and comparable to those generated by a clinical-grade actigraph. As with other actigraphs, sleep specificity plateaus due to limitations in distinguishing wake without movement from sleep. Further studies are needed to evaluate the Arc's ability to differentiate between sleep and wake using other sources of data available from the Arc, such as high resolution accelerometry and photoplethysmography.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0238464PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7498244PMC
October 2020

Automatic sleep stage classification with deep residual networks in a mixed-cohort setting.

Sleep 2021 Jan;44(1)

Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.

Study Objectives: Sleep stage scoring is performed manually by sleep experts and is prone to subjective interpretation of scoring rules with low intra- and interscorer reliability. Many automatic systems rely on few small-scale databases for developing models, and generalizability to new datasets is thus unknown. We investigated a novel deep neural network to assess the generalizability of several large-scale cohorts.

Methods: A deep neural network model was developed using 15,684 polysomnography studies from five different cohorts. We applied four different scenarios: (1) impact of varying timescales in the model; (2) performance of a single cohort on other cohorts of smaller, greater, or equal size relative to the performance of other cohorts on a single cohort; (3) varying the fraction of mixed-cohort training data compared with using single-origin data; and (4) comparing models trained on combinations of data from 2, 3, and 4 cohorts.

Results: Overall classification accuracy improved with increasing fractions of training data (0.25%: 0.782 ± 0.097, 95% CI [0.777-0.787]; 100%: 0.869 ± 0.064, 95% CI [0.864-0.872]), and with increasing number of data sources (2: 0.788 ± 0.102, 95% CI [0.787-0.790]; 3: 0.808 ± 0.092, 95% CI [0.807-0.810]; 4: 0.821 ± 0.085, 95% CI [0.819-0.823]). Different cohorts show varying levels of generalization to other cohorts.

Conclusions: Automatic sleep stage scoring systems based on deep learning algorithms should consider as much data as possible from as many sources available to ensure proper generalization. Public datasets for benchmarking should be made available for future research.
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http://dx.doi.org/10.1093/sleep/zsaa161DOI Listing
January 2021

Association of Rapid Eye Movement Sleep With Mortality in Middle-aged and Older Adults.

JAMA Neurol 2020 Jul 6. Epub 2020 Jul 6.

University of California San Francisco, San Francisco.

Importance: Rapid eye movement (REM) sleep has been linked with health outcomes, but little is known about the relationship between REM sleep and mortality.

Objective: To investigate whether REM sleep is associated with greater risk of mortality in 2 independent cohorts and to explore whether another sleep stage could be driving the findings.

Design, Setting, And Participants: This multicenter population-based cross-sectional study used data from the Outcomes of Sleep Disorders in Older Men (MrOS) Sleep Study and Wisconsin Sleep Cohort (WSC). MrOS participants were recruited from December 2003 to March 2005, and WSC began in 1988. MrOS and WSC participants who had REM sleep and mortality data were included. Analysis began May 2018 and ended December 2019.

Main Outcomes And Measures: All-cause and cause-specific mortality confirmed with death certificates.

Results: The MrOS cohort included 2675 individuals (2675 men [100%]; mean [SD] age, 76.3 [5.5] years) and was followed up for a median (interquartile range) of 12.1 (7.8-13.2) years. The WSC cohort included 1386 individuals (753 men [54.3%]; mean [SD] age, 51.5 [8.5] years) and was followed up for a median (interquartile range) of 20.8 (17.9-22.4) years. MrOS participants had a 13% higher mortality rate for every 5% reduction in REM sleep (percentage REM sleep SD = 6.6%) after adjusting for multiple demographic, sleep, and health covariates (age-adjusted hazard ratio, 1.12; fully adjusted hazard ratio, 1.13; 95% CI, 1.08-1.19). Results were similar for cardiovascular and other causes of death. Possible threshold effects were seen on the Kaplan-Meier curves, particularly for cancer; individuals with less than 15% REM sleep had a higher mortality rate compared with individuals with 15% or more for each mortality outcome with odds ratios ranging from 1.20 to 1.35. Findings were replicated in the WSC cohort despite younger age, inclusion of women, and longer follow-up (hazard ratio, 1.13; 95% CI, 1.08-1.19). A random forest model identified REM sleep as the most important sleep stage associated with survival.

Conclusions And Relevance: Decreased percentage REM sleep was associated with greater risk of all-cause, cardiovascular, and other noncancer-related mortality in 2 independent cohorts.
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http://dx.doi.org/10.1001/jamaneurol.2020.2108DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7550971PMC
July 2020

Narcolepsy risk and COVID-19.

J Clin Sleep Med 2020 10;16(10):1831-1833

Cincinnati Children's Hospital, Cincinnati, Ohio.

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http://dx.doi.org/10.5664/jcsm.8668DOI Listing
October 2020

COMMENTARY on Lammers et al, Diagnosis of central disorders of hypersomnolence: Challenges in defining central disorders of hypersomnolence.

Sleep Med Rev 2020 08 6;52:101327. Epub 2020 May 6.

Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA.

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http://dx.doi.org/10.1016/j.smrv.2020.101327DOI Listing
August 2020

Proteomic biomarkers of sleep apnea.

Sleep 2020 Nov;43(11)

Department of Medicine and Clinical Research Core, Weill Cornell Medicine-Qatar, Qatar Foundation-Education City, Doha, Qatar.

Study Objectives: Obstructive sleep apnea (OSA) is characterized by recurrent partial to complete upper airway obstructions during sleep, leading to repetitive arousals and oxygen desaturations. Although many OSA biomarkers have been reported individually, only a small subset have been validated through both cross-sectional and intervention studies. We sought to profile serum protein biomarkers in OSA in unbiased high throughput assay.

Methods: A highly multiplexed aptamer array (SomaScan) was used to profile 1300 proteins in serum samples from 713 individuals in the Stanford Sleep Cohort, a patient-based registry. Outcome measures derived from overnight polysomnography included Obstructive Apnea Hypopnea Index (OAHI), Central Apnea Index (CAI), 2% Oxygen Desaturation index, mean and minimum oxygen saturation indices during sleep. Additionally, a separate intervention-based cohort of 16 individuals was used to assess proteomic profiles pre- and post-intervention with positive airway pressure.

Results: OAHI was associated with 65 proteins, predominantly pathways of complement, coagulation, cytokine signaling, and hemostasis which were upregulated. CAI was associated with two proteins including Roundabout homolog 3 (ROBO3), a protein involved in bilateral synchronization of the pre-Bötzinger complex and cystatin F. Analysis of pre- and post intervention samples revealed IGFBP-3 protein to be increased while LEAP1 (Hepicidin) to be decreased with intervention. An OAHI machine learning classifier (OAHI >=15 vs OAHI<15) trained on SomaScan protein measures alone performed robustly, achieving 76% accuracy in a validation dataset.

Conclusions: Multiplex protein assays offer diagnostic potential and provide new insights into the biological basis of sleep disordered breathing.
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http://dx.doi.org/10.1093/sleep/zsaa086DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7686561PMC
November 2020

Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness.

Clin Neurophysiol 2020 06 2;131(6):1187-1203. Epub 2020 Apr 2.

Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.

Objective: Significant interscorer variability is found in manual scoring of arousals in polysomnographic recordings (PSGs). We propose a fully automatic method, the Multimodal Arousal Detector (MAD), for detecting arousals.

Methods: A deep neural network was trained on 2,889 PSGs to detect cortical arousals and wakefulness in 1-second intervals. Furthermore, the relationship between MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was analyzed in 1447 MSLT instances in 873 subjects.

Results: In a dataset of 1,026 PSGs, the MAD achieved an F1 score of 0.76 for arousal detection, while wakefulness was predicted with an accuracy of 0.95. In 60 PSGs scored by nine expert technicians, the MAD performed comparable to four and significantly outperformed five expert technicians for arousal detection. After controlling for known covariates, a doubling of the arousal index was associated with an average decrease in MSL of 40 seconds (p = 0.0075).

Conclusions: The MAD performed better or comparable to human expert scorers. The MAD-predicted arousals were shown to be significant predictors of MSL.

Significance: This study validates a fully automatic method for scoring arousals in PSGs.
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http://dx.doi.org/10.1016/j.clinph.2020.02.027DOI Listing
June 2020

Treatment of narcolepsy with natalizumab.

Sleep 2020 Jul;43(7)

Center for Sleep Sciences and Medicine, Stanford University School of Medicine, Palo Alto, CA.

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http://dx.doi.org/10.1093/sleep/zsaa050DOI Listing
July 2020

Primary DQ effect in the association between HLA and neurological syndromes with anti-GAD65 antibodies.

J Neurol 2020 Jul 9;267(7):1906-1911. Epub 2020 Mar 9.

French Reference Center on Paraneoplastic Neurological Syndromes and Autoimmune Encephalitis, Hospices Civils de Lyon, Hôpital Neurologique, 59 Boulevard Pinel, 69677, Bron Cedex, France.

The primary cause of neurological syndromes with antibodies against glutamic acid decarboxylase 65 (GAD65-Ab) is unknown, but genetic predisposition may exist as it is suggested by the co-occurrence in patients and their relatives of other organ-specific autoimmune diseases, notably type 1 diabetes mellitus (T1DM), and by the reports of a few familial cases. We analyzed the human leukocyte antigen (HLA) in 32 unrelated patients and compared them to an ethnically matched sample of 137 healthy controls. Four-digit resolution HLA alleles were imputed from available Genome Wide Association data, and full HLA next-generation sequencing-based typing was also performed. HLA DQA1*05:01-DQB1*02:01-DRB1*03:01 was the most frequent class II haplotype in patients (13/32, 41%). DQB1*02:01 was the only allele found to be significantly more common in patients than in controls (20/137, 15%, corrected p = 0.03, OR 3.96, 95% CI [1.54-10.09]). There was also a trend towards more frequent DQA1*05:01 among patients compared to controls (22/137, 16%; corrected p = 0.05, OR 3.54, 95% CI [1.40-8.91]) and towards a protective effect of DQB1*03:01 (2/32, 6% in patients vs. 42/137, 31% in control group; corrected p = 0.05, OR 0.15, 95% CI [0.02-0.65]). There was no significant demographic or clinical difference between DQ2 and non-DQ2 carriers (p > 0.05). Taken together, these findings suggest a primary DQ effect on GAD65-Ab neurological diseases, partially shared with other systemic organ-specific autoimmune diseases such as T1DM. However, it is likely that other non-HLA loci are involved in the genetic predisposition of GAD65-Ab neurological syndromes.
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http://dx.doi.org/10.1007/s00415-020-09782-8DOI Listing
July 2020

Design of a deep learning model for automatic scoring of periodic and non-periodic leg movements during sleep validated against multiple human experts.

Sleep Med 2020 05 23;69:109-119. Epub 2020 Jan 23.

Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Glostrup, Denmark. Electronic address:

Objective: Currently, manual scoring is the gold standard of leg movement scoring (LMs) and periodic LMs (PLMS) in overnight polysomnography (PSG) studies, which is subject to inter-scorer variability. The objective of this study is to design and validate an end-to-end deep learning system for the automatic scoring of LMs and PLMS in sleep.

Methods: The deep learning system was developed, validated and tested, with respect to manual annotations by expert technicians on 800 overnight PSGs using a leg electromyography channel. The study includes data from three cohorts, namely, the Wisconsin Sleep Cohort (WSC), Stanford Sleep Cohort (SSC) and MrOS Sleep Study. The performance of the system was further compared against individual expert technicians and existing PLM detectors.

Results: The system achieved an F1 score of 0.83, 0.71, and 0.77 for the WSC, SSC, and an ancillary study (Osteoporotic Fractures in Men Study, MrOS) cohorts, respectively. In a total of 60 PSGs from the WSC and the SSC scored by nine expert technicians, the system performed better than two and comparable to seven of the individual scorers with respect to a majority-voting consensus of the remaining scorers. In 60 PSGs from the WSC scored accurately for PLMS, the system outperformed four previous PLM detectors, which were all evaluated on the same data, with an F1 score of 0.85.

Conclusions: The proposed system performs better or comparable to individual expert technicians while outperforming previous automatic detectors. Thereby, the study validates fully automatic methods for scoring LMs in sleep.
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http://dx.doi.org/10.1016/j.sleep.2019.12.032DOI Listing
May 2020

DNMT1 mutations leading to neurodegeneration paradoxically reflect on mitochondrial metabolism.

Hum Mol Genet 2020 Jul;29(11):1864-1881

IRCCS Istituto delle Scienze Neurologiche di Bologna, UOC Clinica Neurologica, Bologna 40139, Italy.

ADCA-DN and HSN-IE are rare neurodegenerative syndromes caused by dominant mutations in the replication foci targeting sequence (RFTS) of the DNA methyltransferase 1 (DNMT1) gene. Both phenotypes resemble mitochondrial disorders, and mitochondrial dysfunction was first observed in ADCA-DN. To explore mitochondrial involvement, we studied the effects of DNMT1 mutations in fibroblasts from four ADCA-DN and two HSN-IE patients. We documented impaired activity of purified DNMT1 mutant proteins, which in fibroblasts results in increased DNMT1 amount. We demonstrated that DNMT1 is not localized within mitochondria, but it is associated with the mitochondrial outer membrane. Concordantly, mitochondrial DNA failed to show meaningful CpG methylation. Strikingly, we found activated mitobiogenesis and OXPHOS with significant increase of H2O2, sharply contrasting with a reduced ATP content. Metabolomics profiling of mutant cells highlighted purine, arginine/urea cycle and glutamate metabolisms as the most consistently altered pathways, similar to primary mitochondrial diseases. The most severe mutations showed activation of energy shortage AMPK-dependent sensing, leading to mTORC1 inhibition. We propose that DNMT1 RFTS mutations deregulate metabolism lowering ATP levels, as a result of increased purine catabolism and urea cycle pathways. This is associated with a paradoxical mitochondrial hyper-function and increased oxidative stress, possibly resulting in neurodegeneration in non-dividing cells.
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http://dx.doi.org/10.1093/hmg/ddaa014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7372549PMC
July 2020

Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:556-561

Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation of arousals and leg movements. Both of these are commonly seen during normal sleep, while an excessive amount of either is linked to disrupted sleep patterns, excessive daytime sleepiness impacting quality of life, and various sleep disorders. Our model was trained on 1,485 subjects and tested on 1,000 separate recordings of sleep. We tested two different experimental setups and found optimal arousal detection was attained by including a recurrent neural network module in our default model with a dynamic default event window (F1 = 0.75), while optimal leg movement detection was attained using a static event window (F1 = 0.65). Our work show promise while still allowing for improvements. Specifically, future research will explore the proposed model as a general-purpose sleep analysis model.
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http://dx.doi.org/10.1109/EMBC.2019.8856570DOI Listing
July 2019

[Narcolepsy: From the discovery of a wake promoting peptide to autoimmune T cell biology and molecular mimicry with flu epitopes].

Biol Aujourdhui 2019 12;213(3-4):87-108. Epub 2019 Dec 12.

Stanford Center for Sleep Sciences and Medicine, Department of Psychiatry and Behavioral Medicine, Stanford University, 3615 Porter Drive, Palo Alto, CA, USA.

Narcolepsy-cataplexy was first described in the late 19th century in Germany and France. Prevalence was established to be 0.05 % and a canine model was discovered in the 1970s. In 1983, a Japanese study found that all patients carried HLA-DR2, suggesting autoimmunity as the cause of the disease. Studies in the canine model established that dopaminergic stimulation underlies anti-narcoleptic action of psychostimulants, while antidepressants were found to suppress cataplexy through adrenergic reuptake inhibition. No HLA association was found in canines. A linkage study initiated in 1988 revealed in hypocretin (orexin) receptor two mutations as the cause of canine narcolepsy in 1999. In 1992, studies on African Americans showed that DQ0602 was a better marker than DR2 across all ethnic groups. In 2000, hypocretin-1/orexin A levels were measured in the cerebrospinal fluid (CSF) and found to be undetectable in most patients, establishing hypocretin deficiency as the cause of narcolepsy. Decreased CSF hypocretin-1 was then found to be secondary to the loss of the 70,000 neurons producing hypocretin in the hypothalamus, suggesting immune destruction of these cells as the cause of the disease. Additional genetic studies, notably genome wide associations (GWAS), found multiple genetic predisposing factors for narcolepsy. These were almost all involved in other autoimmune diseases, although a strong and unique association with T cell receptor (TCR) alpha and beta loci were observed. Nonetheless, all attempts to demonstrate presence of autoantibodies against hypocretin cells in narcolepsy failed, and the presumed autoimmune cause remained unproven. In 2009, association with strep throat infections were found, and narcolepsy onsets were found to occur more frequently in spring and summer, suggesting upper away infections as triggers. Following reports that narcolepsy cases were triggered by vaccinations and infections against influenza A 2009 pH1N1, a new pandemic strain that erupted in 2009, molecular mimicry with influenza A virus was suggested in 2010. This hypothesis was later confirmed by peptide screening showing higher activity of CD4 T cell reactivity to a specific post-translationally amidated segment of hypocretin (HCRT-) and cross-reactivity of specific TCRs with a pH1N1-specific segment of hemagglutinin that shares homology with HCRT-. Strikingly, the most frequent TCR recognizing these antigens was found to carry sequences containing TRAJ24 or TRVB4-2, segments modulated by narcolepsy-associated genetic polymorphisms. Cross-reactive CD4 T cells with these cross-reactive TCRs likely subsequently recruit CD8 T cells that are then involved in hypocretin cell destruction. Additional flu mimics are also likely to be discovered since narcolepsy existed prior to 2009. The work that has been conducted over the years on narcolepsy offers a unique perspective on the conduct of research on the etiopathogeny of a specific disease.
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http://dx.doi.org/10.1051/jbio/2019026DOI Listing
May 2020

Robust, ECG-based detection of Sleep-disordered breathing in large population-based cohorts.

Sleep 2020 May;43(5)

Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.

Study Objectives: Up to 5% of adults in Western countries have undiagnosed sleep-disordered breathing (SDB). Studies have shown that electrocardiogram (ECG)-based algorithms can identify SDB and may provide alternative screening. Most studies, however, have limited generalizability as they have been conducted using the apnea-ECG database, a small sample database that lacks complex SDB cases.

Methods: Here, we developed a fully automatic, data-driven algorithm that classifies apnea and hypopnea events based on the ECG using almost 10 000 polysomnographic sleep recordings from two large population-based samples, the Sleep Heart Health Study (SHHS) and the Multi-Ethnic Study of Atherosclerosis (MESA), which contain subjects with a broad range of sleep and cardiovascular diseases (CVDs) to ensure heterogeneity.

Results: Performances on average were sensitivity(Se)=68.7%, precision (Pr)=69.1%, score (F1)=66.6% per subject, and accuracy of correctly classifying apnea-hypopnea index (AHI) severity score was Acc=84.9%. Target AHI and predicted AHI were highly correlated (R2 = 0.828) across subjects, indicating validity in predicting SDB severity. Our algorithm proved to be statistically robust between databases, between different periodic leg movement index (PLMI) severity groups, and for subjects with previous CVD incidents. Further, our algorithm achieved the state-of-the-art performance of Se=87.8%, Sp=91.1%, Acc=89.9% using independent comparisons and Se=90.7%, Sp=95.7%, Acc=93.8% using a transfer learning comparison on the apnea-ECG database.

Conclusions: Our robust and automatic algorithm constitutes a minimally intrusive and inexpensive screening system for the detection of SDB events using the ECG to alleviate the current problems and costs associated with diagnosing SDB cases and to provide a system capable of identifying undiagnosed SDB cases.
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http://dx.doi.org/10.1093/sleep/zsz276DOI Listing
May 2020

Validation of Multiple Sleep Latency Test for the diagnosis of pediatric narcolepsy type 1.

Neurology 2019 09 12;93(11):e1034-e1044. Epub 2019 Aug 12.

From the Department of Biomedical and Neuromotor Sciences (DIBINEM) (F.P., S.V., E.A., G.P.), University of Bologna; IRCCS Istituto delle Scienze Neurologiche di Bologna (F.P., S.V., E.A., G.P.), Italy; National Reference Centre for Orphan Diseases, Narcolepsy, Rare Hypersomnias, Sleep Disorders Center, Department of Neurology (L.B., Y.D.), Gui de Chauliac Hospital, Montpellier; Inserm, U1061 (L.B., I.J., Y.D.), Montpellier; University of Montpellier (L.B., I.J., Y.D.), France; and Stanford University Center for Sleep Sciences, Department of Psychiatry and Behavioral Sciences (E.M.), Stanford University School of Medicine, Palo Alto, CA.

Objective: To validate polysomnographic markers (sleep latency and sleep-onset REM periods [SOREMPs] at the Multiple Sleep Latency Test [MSLT] and nocturnal polysomnography [PSG]) for pediatric narcolepsy type 1 (NT1) against CSF hypocretin-1 (hcrt-1) deficiency and presence of cataplexy, as no criteria are currently validated in children.

Methods: Clinical, neurophysiologic, and, when available, biological data (HLA-DQB1*06:02 positivity, CSF hcrt-1 levels) of 357 consecutive children below 18 years of age evaluated for suspected narcolepsy were collected. Best MSLT cutoffs were obtained by receiver operating characteristic (ROC) curve analysis by contrasting among patients with available CSF hcrt-1 assay (n = 228) with vs without CSF hcrt-1 deficiency, and further validated in patients without available CSF hcrt-1 against cataplexy (n = 129).

Results: Patients with CSF hcrt-1 deficiency were best recognized using a mean MSLT sleep latency ≤8.2 minutes (area under the ROC curve of 0.985), or by at least 2 SOREMPs at the MSLT (area under the ROC curve of 0.975), or the combined PSG + MSLT (area under the ROC curve of 0.977). Although specificity and sensitivity of referenc MSLT sleep latency ≤8 minutes and ≥2 SOREMPs (nocturnal SOREMP included) was 100% and 94.87%, the combination of MSLT sleep latency and SOREMP counts did not improve diagnostic accuracy. Age or sex also did not significantly influence these results in our pediatric population.

Conclusions: At least 2 SOREMPs or a mean sleep latency ≤8.2 minutes at the MSLT are valid and reliable markers for pediatric NT1 diagnosis, a result contrasting with adult NT1 criteria.

Classification Of Evidence: This study provides Class III evidence that for children with suspected narcolepsy, polysomnographic and MSLT markers accurately identify those with narcolepsy type 1.
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http://dx.doi.org/10.1212/WNL.0000000000008094DOI Listing
September 2019

Cardiovascular autonomic dysfunction, altered sleep architecture, and muscle overactivity during nocturnal sleep in pediatric patients with narcolepsy type 1.

Sleep 2019 12;42(12)

Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.

Study Objectives: Arterial blood pressure (ABP) decreases during sleep compared with wakefulness and this change is blunted in mouse models of and adult patients with narcolepsy type 1 (NT1). We tested whether: (1) pediatric patients with NT1 have similar cardiovascular autonomic abnormalities during nocturnal sleep; and (2) these abnormalities can be linked to hypocretin-1 cerebrospinal fluid concentration (CSF HCRT-1), sleep architecture, or muscle activity.

Methods: Laboratory polysomnographic studies were performed in 27 consecutive drug-naïve NT1 children or adolescents and in 19 matched controls. Nocturnal sleep architecture and submentalis (SM), tibialis anterior (TA), and hand extensor (HE) electromyographic (EMG) activity were analyzed. Cardiovascular autonomic function was assessed through the analysis of pulse transit time (PTT) and heart period (HP).

Results: PTT showed reduced lengthening during total sleep and REM sleep compared with nocturnal wakefulness in NT1 patients than in controls, whereas HP did not. NT1 patients had altered sleep architecture, higher SM EMG during REM sleep, and higher TA and HE EMG during N1-N3 and REM sleep when compared with controls. PTT alterations found in NT1 patients were more severe in subjects with lower CSF HRCT-1, but did not cluster or correlate with sleep architecture alterations or muscle overactivity during sleep.

Conclusion: Our results suggest that pediatric NT1 patients close to disease onset have impaired capability to modulate ABP as a function of nocturnal wake-sleep transitions, possibly as a direct consequence of hypocretin neuron loss. The relevance of this finding for cardiovascular risk later in life remains to be determined.
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http://dx.doi.org/10.1093/sleep/zsz169DOI Listing
December 2019

Factors associated with fatigue in patients with insomnia.

J Psychiatr Res 2019 10 28;117:24-30. Epub 2019 Jun 28.

Department of Psychiatry and Behavioral Medicine, Stanford University Center for Sleep Sciences and Medicine, Stanford University, CA, USA.

Although fatigue is common in insomnia, the clinical associates of fatigue in patients with insomnia are largely unknown. We aimed to investigate the clinical associates of fatigue in patients with insomnia. Patients visiting the Stanford Sleep Medicine Center completed the Insomnia Severity Index (ISI), Insomnia Symptom Questionnaire (ISQ), the Fatigue Severity Scale (FSS), the Epworth Sleepiness Scale (ESS), and the Patient Health Questionnaire (PHQ-9). Among 6367 patients, 2024 were diagnosed with insomnia (age 43.06 ± 15.19 years; 1110 women and 914 men) according to the ISI and the ISQ. Insomnia patients with severe fatigue (n = 1306) showed higher insomnia symptoms, daytime sleepiness, depression and longer habitual sleep duration than those without severe fatigue (n = 718). Higher insomnia symptoms, daytime sleepiness and depressive symptoms, and longer habitual sleep duration, independently predicted higher fatigue scores. Among insomnia patients with daytime sleepiness (ESS≥10), only habitual sleep duration and depression predicted fatigue scores. The interaction between insomnia severity and daytime sleepiness significantly predicted the severity of fatigue. Depression was a significant mediator between insomnia and fatigue. For 598 insomnia patients undergoing overnight polysomnography (PSG), no significant correlations were found between fatigue and any PSG parameters. The current study suggests that managing insomnia or depression may reduce the fatigue of insomnia patients, whereas arbitrary efforts to prolong sleep duration may worsen their fatigue.
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http://dx.doi.org/10.1016/j.jpsychires.2019.06.021DOI Listing
October 2019

Cross-disorder analysis of schizophrenia and 19 immune-mediated diseases identifies shared genetic risk.

Hum Mol Genet 2019 10;28(20):3498-3513

Lancaster Medical School and Data Science Institute, Lancaster University, Lancaster, UK.

Many immune diseases occur at different rates among people with schizophrenia compared to the general population. Here, we evaluated whether this phenomenon might be explained by shared genetic risk factors. We used data from large genome-wide association studies to compare the genetic architecture of schizophrenia to 19 immune diseases. First, we evaluated the association with schizophrenia of 581 variants previously reported to be associated with immune diseases at genome-wide significance. We identified five variants with potentially pleiotropic effects. While colocalization analyses were inconclusive, functional characterization of these variants provided the strongest evidence for a model in which genetic variation at rs1734907 modulates risk of schizophrenia and Crohn's disease via altered methylation and expression of EPHB4-a gene whose protein product guides the migration of neuronal axons in the brain and the migration of lymphocytes towards infected cells in the immune system. Next, we investigated genome-wide sharing of common variants between schizophrenia and immune diseases using cross-trait LD score regression. Of the 11 immune diseases with available genome-wide summary statistics, we observed genetic correlation between six immune diseases and schizophrenia: inflammatory bowel disease (rg = 0.12 ± 0.03, P = 2.49 × 10-4), Crohn's disease (rg = 0.097 ± 0.06, P = 3.27 × 10-3), ulcerative colitis (rg = 0.11 ± 0.04, P = 4.05 × 10-3), primary biliary cirrhosis (rg = 0.13 ± 0.05, P = 3.98 × 10-3), psoriasis (rg = 0.18 ± 0.07, P = 7.78 × 10-3) and systemic lupus erythematosus (rg = 0.13 ± 0.05, P = 3.76 × 10-3). With the exception of ulcerative colitis, the degree and direction of these genetic correlations were consistent with the expected phenotypic correlation based on epidemiological data. Our findings suggest shared genetic risk factors contribute to the epidemiological association of certain immune diseases and schizophrenia.
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http://dx.doi.org/10.1093/hmg/ddz145DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891073PMC
October 2019

Meeting report narcolepsy and pandemic influenza vaccination: What we know and what we need to know before the next pandemic? A report from the 2nd IABS meeting.

Biologicals 2019 Jul 23;60:1-7. Epub 2019 May 23.

IABS, Rue de la Vallée 3, 1204, Genève, Switzerland. Electronic address:

A group of scientific and public health experts and key stakeholders convened to discuss the state of knowledge on the relationship between adjuvanted monovalent inactivated 2009 influenza A H1N1 vaccines used during the 2009 influenza pandemic and narcolepsy. There was consensus that an increased risk of narcolepsy was consistently observed after Pandemrix (AS03-adjuvanted) vaccine, but similar associations following Arepanrix (AS03-adjuvanted) or Focetria (MF59-adjuvanted) vaccines were not observed. Whether the differences are due to vaccine composition or other factors such as the timing of large-scale vaccination programs relative to H1N1pdm09 wild-type virus circulation in different geographic regions is not clear. The limitations of retrospective observational methodologies could also be contributing to some of the differences across studies. More basic and epidemiologic research is needed to further elucidate the association between adjuvanted influenza vaccine and narcolepsy and its mechanism and to inform planning and preparation for vaccination programs in advance of the next influenza pandemic.
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http://dx.doi.org/10.1016/j.biologicals.2019.05.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6668612PMC
July 2019

Response to "H1N1 hemagglutinin-specific HLA-DQ6-restricted CD4+ T cells can be readily detected in narcolepsy type 1 patients and healthy controls".

J Neuroimmunol 2019 08 1;333:476959. Epub 2019 May 1.

Stanford Center For Sleep Sciences and Medicine, Dept of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, California, USA.

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http://dx.doi.org/10.1016/j.jneuroim.2019.04.019DOI Listing
August 2019

Physical activity, sleep and cardiovascular health data for 50,000 individuals from the MyHeart Counts Study.

Sci Data 2019 04 11;6(1):24. Epub 2019 Apr 11.

Department of Medicine, Stanford University, Stanford, California, USA.

Studies have established the importance of physical activity and fitness for long-term cardiovascular health, yet limited data exist on the association between objective, real-world large-scale physical activity patterns, fitness, sleep, and cardiovascular health primarily due to difficulties in collecting such datasets. We present data from the MyHeart Counts Cardiovascular Health Study, wherein participants contributed data via an iPhone application built using Apple's ResearchKit framework and consented to make this data available freely for further research applications. In this smartphone-based study of cardiovascular health, participants recorded daily physical activity, completed health questionnaires, and performed a 6-minute walk fitness test. Data from English-speaking participants aged 18 years or older with a US-registered iPhone who agreed to share their data broadly and who enrolled between the study's launch and the time of the data freeze for this data release (March 10 2015-October 28 2015) are now available for further research. It is anticipated that releasing this large-scale collection of real-world physical activity, fitness, sleep, and cardiovascular health data will enable the research community to work collaboratively towards improving our understanding of the relationship between cardiovascular indicators, lifestyle, and overall health, as well as inform mobile health research best practices.
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http://dx.doi.org/10.1038/s41597-019-0016-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6472350PMC
April 2019

The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight.

Science 2019 04;364(6436)

Northwestern University, Evanston, IL, USA.

To understand the health impact of long-duration spaceflight, one identical twin astronaut was monitored before, during, and after a 1-year mission onboard the International Space Station; his twin served as a genetically matched ground control. Longitudinal assessments identified spaceflight-specific changes, including decreased body mass, telomere elongation, genome instability, carotid artery distension and increased intima-media thickness, altered ocular structure, transcriptional and metabolic changes, DNA methylation changes in immune and oxidative stress-related pathways, gastrointestinal microbiota alterations, and some cognitive decline postflight. Although average telomere length, global gene expression, and microbiome changes returned to near preflight levels within 6 months after return to Earth, increased numbers of short telomeres were observed and expression of some genes was still disrupted. These multiomic, molecular, physiological, and behavioral datasets provide a valuable roadmap of the putative health risks for future human spaceflight.
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http://dx.doi.org/10.1126/science.aau8650DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7580864PMC
April 2019

Knowledge Gaps in the Perioperative Management of Adults With Narcolepsy: A Call for Further Research.

Anesth Analg 2019 07;129(1):204-211

Division of Pulmonary, Critical Care and Sleep Medicine, Metro Health Medical Center, Case Western Reserve University, Cleveland, Ohio.

There is increasing awareness that sleep disorders may be associated with increased perioperative risk. The Society of Anesthesia and Sleep Medicine created the Narcolepsy Perioperative Task Force: (1) to investigate the current state of knowledge of the perioperative risk for patients with narcolepsy, (2) to determine the viability of developing perioperative guidelines for the management of patients with narcolepsy, and (3) to delineate future research goals and clinically relevant outcomes. The Narcolepsy Perioperative Task Force established that there is evidence for increased perioperative risk in patients with narcolepsy; however, this evidence is sparse and based on case reviews, case series, and retrospective reviews. Mechanistically, there are a number of potential mechanisms by which patients with narcolepsy could be at increased risk for perioperative complications. These include aggravation of the disease itself, dysautonomia, narcolepsy-related medications, anesthesia interactions, and withdrawal of narcolepsy-related medications. At this time, there is inadequate research to develop an expert consensus or guidelines for the perioperative management of patients with narcolepsy. The paucity of available literature highlights the critical need to determine if patients with narcolepsy are at an increased perioperative risk and to establish appropriate research protocols and clearly delineated patient-centered outcomes. There is a real need for collaborative research among sleep medicine specialists, surgeons, anesthesiologists, and perioperative providers. This future research will become the foundation for the development of guidelines, or at a minimum, a better understanding how to optimize the perioperative care of patients with narcolepsy.
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http://dx.doi.org/10.1213/ANE.0000000000004088DOI Listing
July 2019