bluuebunny/crossref_metadata_2025_split huggingface dataset:
1229 Management of Severe Sleep Related Disordered Breathing in a Pediatric Case of Vagal Nerve Stimulation
Authors: Ryba-White Benjamin , Molero Helena , Irfan Muna 
Published: May 2020

Abstract

Introduction
Vagal nerve stimulator (VNS) can be an effective treatment for refractory epilepsy but can lead to sleep related disordered breathing in the form of obstructive and central sleep apnea. We describe a pediatric case with VNS developing severe obstructive sleep apnea (OSA) and the course of management.

Report of Case
A 10-year-old boy with history of cerebral palsy, medically intractable epilepsy secondary to Lennox-Gastaut syndrome, and noted improvement in seizure frequency with VNS placement, presented to the pediatric sleep clinic with concern for sleep disordered breathing in the setting of snoring and witnessed gasping at night. The polysomnogram (PSG) showed severe OSA with apnea/hypopnea index (AHI) of 21.8 events/hour and associated hypoxemia. He subsequently underwent adenotonsillectomy. Post procedural PSG demonstrated persistent severe OSA. The obstructive events had rhythmicity temporally coincident with the VNS cycling. Subsequent PSG, after turning VNS off, showed a reduction in AHI to 11 events/hour without hypoxemia. Positive airway pressure (PAP) was initiated during this PSG, however titration was rendered unsuccessful due to VNS activation. A final titration PSG was performed with optimal resolution of OSA with VNS turned off. Since the patient did not have nocturnal seizures, it was recommended to turn the VNS off at nighttime with CPAP use. Patient was noted to have less sleep disruptions at night and more alertness during the day.

Conclusion
This case poses a challenging situation where patient’s epilepsy responded to VNS but the device contributed to OSA and affected PAP titration. One strategy to improve OSA in such cases is to change VNS parameters at the risk of changing stimulation settings effective for seizure control. Turning VNS off during sleep provided a simpler solution while ensuring effective treatment. Thus, clinicians should be vigilant about concomitant OSA treatment in the setting of vagal nerve stimulation, which can improve quality of life.
0138 Behaviorally Assessed Sleep Duration and Oxidative Stress
Authors: Decker A , Cribbet M 
Published: May 2020

Abstract

Introduction
Sleep may promote health by acting as an antioxidant, thereby increasing the body’s resistance to oxidative stress. Lipid peroxidation, a marker of oxidative stress, is one of the key early events in the development of atherosclerosis, the pathologic condition that underlies cardiovascular disease (CVD). Short sleep duration is prevalent in the general population and associated with CVD risk. However, the mechanisms linking short sleep to CVD are not well understood.

Methods
To test the hypothesis that short sleep duration would be associated with higher levels of oxidative stress, we conducted secondary data analysis on a diverse sample of participants (N= 81; Mage = 30.1(SD=10.9); 57% Male; 27% African American) from Pittsburgh, Pennsylvania. Participants wore actigraphs on their non-dominant wrists for 7 days to collect rest and activity data, which were used to derive average sleep duration. Participants provided urine samples and self-reported demographic data, along with daily mood, alcohol use, and physical activity. Oxidative stress was quantified as urinary concentrations of 15-F2t-isoprostane (15-F2-IsoP). Levels of 15-F2-IsoP were measured with a competitive enzyme linked-immunosorbent assay (ELISA) in duplicate. Urinary 15-F2-IsoP was adjusted by urinary creatinine to create an index of oxidative stress that took into account a measure of antioxidant defenses.

Results
Sleep duration was inversely correlated with oxidative stress (b= -.24, p=.03). In multiple regression analyses that controlled for age, sex, race, body mass index, alcohol use, physical activity, and depressed mood, sleep duration remained significantly negatively associated with oxidative stress, β = -.16, t = -2.33, p = .02, R2= .20.

Conclusion
These findings are important because they suggest that a relationship exists between short sleep and increased oxidative damage to lipids. Increasing our understanding of this relationship could motivate new CVD prevention strategies that target the inhibition of oxidative stress through sleep interventions.

Support
This study was supported by a grant from the National Center for Complementary and Integrative Health (AT006694); a grant from the National Institute of Allergy and Infectious Diseases (R01 AI066367); grants from the National Institutes of Health (UL1 RR024153 and UL1 RT000005); and by the John D. and Catherine T. MacArthur Foundation.
0241 Habitual Daytime Sleepiness and the Timing of Use of Alcohol, Tobacco, and Caffeine
Authors: Tubbs A , Hale L , Branas C , Killgore W D , Wills C C , Grandner M A 
Published: May 2020

Abstract

Introduction
Alcohol, caffeine, and tobacco are frequently used in the community, and the timing of use may impact daytime sleepiness. The present analysis examined relationships between daytime sleepiness and timing of alcohol, tobacco, and caffeine use in a real-world sample.

Methods
Data were from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study of N=1007 adults age 22–60 from the community. Daytime sleepiness was assessed with the Epworth Sleepiness Scale (ESS). Participants were asked if they had ever used caffeine, alcohol and tobacco. If they reported using a substance, they were then asked which times of day they were likely to use the substance: 5AM-8AM, 8AM-11AM, 11AM-2PM, 2PM-5PM, 5PM-8PM, 8PM-11PM, 11PM-2AM, and 2AM-5AM. Logistic regression analyses examined the relationship between ESS score and likelihood of use of substances at each time.

Results
ESS score was associated with increased odds of ever using alcohol (OR: 1.05, 95% CI: 1.01 to 1.09) or tobacco (OR: 1.04, 95% CI: 1.01 to 1.07). ESS score was associated with an increased likelihood of drinking alcohol in the morning (5AM-8AM, OR: 1.13) and night (11PM-5AM, OR: 1.05). Sleepiness was also associated with increased likelihood of tobacco use in the afternoon (11AM-2PM, OR 1.04) and night (11PM-2AM, OR 1.05). Finally, ESS score was associated with increased likelihood of caffeine use during the midday and afternoon (11AM-5PM, OR: 1.04).

Conclusion
Greater sleepiness is associated with use of alcohol in the morning and at night, and with use of tobacco in the afternoon and at night. Finally, increased sleepiness was associated with caffeine use during the latter part of the workday. Some of these use patterns may be a cause of sleepiness (e.g., morning alcohol use or nighttime smoking) and some a consequence (e.g., daytime caffeine and tobacco use). More research on the impact of real-world sleepiness on real-world substance use is warranted.

Support
This work was supported by a grant from Jazz Pharmaceuticals
Dr. Grandner is supported by R01MD011600The SHADES study was funded by R21ES022931
0281 Sleep Deprivation Impairs Cooperative Behavior Selectively: Evidence from Prisoner’s and Chicken Dilemmas
Authors: Ma N , Lin Y , Mo L 
Published: May 2020

Abstract

Introduction
Cooperation, the cornerstone of human interaction, has attracted much attention since it was indispensable in the contemporary world. However, little research has been done on whether sleep deprivation altered human cooperative behavior. In the present work, we investigated cooperation and sleep deprivation directly, aiming to evaluate the influences of acute sleep deprivation on cooperation with two classical social dilemmas, the Prisoner’s dilemma (PD) and the chicken dilemma (CD).

Methods
All participants (N=24) were required to come for the experiments twice, one time for normal sleep condition, the other time for sleep deprivation condition, with a counter-balanced sequence. In the following afternoon, participants completed the psychomotor vigilance task (PVT) and two social dilemmas tasks, as well as the Karolinska Sleepiness Scale (KSS), the Risk Orientation Questionnaire (ROQ) and the Positive and Negative Affect Schedule (PANAS).

Results
Our results demonstrated that sleep deprivation significantly impaired cooperative behaviors in the CD but not in the PD. In addition, this detrimental effect was not related with the alteration in the risk-seeking, objective alertness, subjective sleepiness, and mood.

Conclusion
The current findings revealed that sleep deprivation impairs human cooperative behaviors selectively. However, the underlying mechanism remains to further explore with neuroimaging studies and focus on the mediating role of general trust and sensitivity of loss.

Support
This research was supported by the National Natural Science Foundation of China (31500906), Guangdong Natural Science Funding - General Program (2019A1515012182).
0371 Educational Disparities in U.S. Elementary School Children are Related to Sleep Duration and Bedtimes
Authors: Woods A D , Morgan P L , Jiao J L , Buxton O M 
Published: May 2020

Abstract

Introduction
Sleep is vital for healthy development in children. Suboptimal sleep health may play an under-appreciated role in educational achievement gaps among vulnerable student populations. Students of color, students from economically disadvantaged homes, and students with disabilities are especially likely to experience poor sleep. Sleep deficiency could interfere with school functioning, including academic achievement.

Methods
Longitudinal data from the nationally-representative Early Childhood Longitudinal Study (ECLS-K: 2011) included ~12,000 students. We tested the hypothesis that parent-reported child sleep duration (typical hours per night) was associated with academic achievement trajectories (3rd-5th grade). We further tested the extent to which this relation is linked to parent-reported bedtimes in kindergarten. Preregistered analyses (osf.io) used structural equation path modeling, stratified by racial/ethnic group (White, Black, Hispanic, Asian), disability status, and socioeconomic status (SES) tertiles. Students were assessed using psychometrically-validated standardized academic achievement tests.

Results
Children with later kindergarten bedtimes had shorter sleep duration across 3rd-5th grade. Children with shorter sleep duration also had poorer achievement in 3rd grade. The path by which sleep associates with achievement differed by vulnerable subgroups. Among children from average- or high-SES families, earlier bedtimes were related to higher reading achievement growth across 3rd-5th grade, but not among children from low-SES families. For children with disabilities, longer sleep duration was significantly and positively associated with growth in reading achievement across 3rd-5th grade, but this relation among children with disabilities was not evident within racial or ethnic groups or SES strata.

Conclusion
Sleep duration, a modifiable behavioral factor, may be a promising target of intervention in families for promoting healthy childhood sleep health behaviors. Results provide evidence that age-appropriate bedtimes and adequate sleep duration could be promoted among vulnerable populations including students with disabilities. For instance, although most screening instruments do not currently evaluate sleep or sleep disorders, assessing and treating disability or behavioral difficulties could include such evaluations for clinical and parent consideration.

Support
Penn State Center for Educational Disparities Research
0385 Risk Assessment of Sleep Disorder Comorbidity Across Active Duty Army Installations from Military Medical Databases
Authors: Brager A J , Hosamane N , Capaldi V , Simonelli G 
Published: May 2020

Abstract

Introduction
The impact of sleep disorders on active duty Soldiers’ medical readiness is clinically significant. Sleep disorders present high comorbidity with disease states directly impacting medical readiness, ranging from musculoskeletal injury (MSK-I), obesity, and drug dependence. The current study performed a risk assessment of sleep disorder comorbidity with MSK-I, obesity, and drug dependence across active duty United States Army installations.

Methods
Health incidences (percent active duty per installation) were queried from the Office of the Surgeon General Health of the Force (HoF) report, specifically for Fiscal Year (FY) 2017 (n = 471,000; 85.5% male, &gt; 70% between 18 -34). Nonparametric ranked tests identified active duty Army installations at low risk (green; &lt; 25% percentile relative to mean rank), moderate risk (amber; 25% - 50%), and high risk (red; &gt; 75% percentile). Linear regressions determined extent of comorbidity of sleep disorders with MSK-I, obesity, and drug dependence (tobacco use and substance abuse).

Results
Mean rank comparisons for sleep disorders vs. injury index (p=0.499), obesity (p=0.306), tobacco use (p=0.378), and substance abuse (p=0.591) did not differ for each installation. Further, there was a high degree of co-morbidity for mean percentage of diagnosed sleep disorder with injury index (p&lt;0.001; r2 = 0.517), obesity (p&lt;0.001; r2 = 0.963), tobacco use (p&lt;0.001; r2 = 0.928), and substance abuse (p&lt;0.001; r2 = 0.968).

Conclusion
In general, large infantry and artillery training units located in the Southeastern United States were “in the red” for not meeting medical readiness standards. A few exceptions include Virginia-Maryland triangle, a heavily populated area. These data demonstrate strong geographical influences on health risk comorbidity in active duty Soldiers comparable to civilian sectors.

Support
Military Operational Medicine Research Program
0432 A Deep Learning Approach for Automated Sleep-Wake Scoring in Pre-Clinical Animal Models
Authors: Svetnik V , Wang T , Xu Y , Hansen B J , Fox S V 
Published: May 2020

Abstract

Introduction
Experimental investigation of sleep-wake dynamics in animals is an important part of pharmaceutical development. It typically involves recording of electroencephalogram, electromyogram, locomotor activity, and electrooculogram. Visual identification, or scoring, of the sleep-wake states from these recordings is time-consuming. We sought to develop software for automated sleep-wake scoring capable of processing large databases of multi-channel signal recordings in a range of animal species.

Methods
We used a large historical database of signal recordings and scores in non-human primates, dogs, mice, and rats, to develop a deep Convolutional Neural Network (CNN) classification algorithm for automatically scoring sleep-wake states. We compared the performance of the CNN algorithm with that of a widely used Machine Learning algorithm, Random Forest (RF).

Results
In non-human primates and dogs, CNN accuracy in sleep-wake scoring of data was significantly higher than RF accuracy: 0.75 versus 0.66 for non-human primates and 0.73 versus 0.64 for dogs. In rodents, the difference between CNN and RF was smaller: 0.83 versus 0.81 for mice and 0.78 versus 0.77 for rats. The variability of CNN accuracy was lower than that of RF for non-human primates, dogs and mice, but similar for rats.

Conclusion
We recommend use of CNN for sleep-wake scoring in non-human primates and dogs, and RF for sleep-wake scoring in rodents.

Support
Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA
0458 Smoking and Caffeine Consumption Differ Between Insomnia Phenotypes Based on Objective Sleep Duration
Authors: Vgontzas A N , Fernandez-Mendoza J , Puzino K , Calhoun S L , Krishnamurthy V B , Basta M , Bixler E O 
Published: May 2020

Abstract

Introduction
The insomnia with short sleep phenotype (ISS), in contrast to the normal sleep phenotype (INS), is characterized by physiological hyperarousal including activation of the stress system and cardiometabolic morbidity. The aim of this study was to assess whether these two insomnia phenotypes differ in terms of the use of two common stimulants (i.e., caffeine and nicotine).

Methods
Data from the Penn State Adult Cohort (N=1741) was used in this study (52.2% women, 48.8±13.6 years). A 1-night, 8-hour, polysomnography (PSG) was used to classify subjects into normal (≥6h) and short (&lt;6h) sleep duration groups. Self-reported sleep difficulty was defined based on three levels of severity as normal sleep (n=1022), poor sleep (n=520) and insomnia (n=199). Self-reported heavy caffeine use was defined as ≥3 cups daily and heavy smoking as ≥20 cigarettes daily. Multinomial logistic regression analyses were conducted adjusting for covariates such as age, gender, and race.

Results
Compared to normal sleepers, ISS (OR=0.55, 95% CI=0.31-0.97, p=0.04), but not INS (OR=0.92, 95% CI=0.52-1.64, p=0.77), was associated with significantly less heavy caffeine use. In contrast, INS (OR=2.20, 95% CI=1.10-4.40, p=0.03), but not ISS (OR=0.95, 95% CI=0.41-2.17, p=0.90), was associated with significantly more heavy smoking.

Conclusion
These results indicate that the use of common stimulants (i.e., smoking cigarettes and drinking caffeine) is higher in the INS phenotype than the ISS phenotype. Individuals with the ISS phenotype may be using less caffeine and tobacco to avoid further stimulation of the already hyperaroused physiologic system, which may result in worsening of their insomnia. In the INS phenotype, changes in health behaviors should be an important part of a multidimensional approach to treatment.

Support
American Heart Association (14SDG19830018), National Institutes of Health (R01HL51931, R01HL40916)
0594 Can a Deep Convolutional Neural Network Extract Diagnostic Information on Obstructive Sleep Apnea from Images?
Authors: Tsuiki S , Nagaoka T , Fukuda T , Sakamoto Y , Almeida F R , Nakayama H , Inoue Y , Enno H 
Published: May 2020

Abstract

Introduction
Lateral cephalometric radiography is a simple way to provide craniofacial soft/hard tissue profiles specific for patients with obstructive sleep apnea (OSA) and may thus offer diagnostic information on the disease. We hypothesized that a machine learning technology, a deep convolutional neural network (DCNN), could make it possible to detect OSA based solely on lateral cephalometric radiographs without the need for either large amounts of subjective/laboratory data or skilled analyses.

Methods
In this diagnostic study, a DCNN was developed (n=1,258) and tested (n=131) using data from 1,389 lateral cephalometric radiographs obtained from individuals diagnosed with severe OSA (n=867; apnea hypopnea index &gt;30/hour) or non-OSA (n=522; apnea hypopnea index &lt; 5) at a single center for sleep disorders from March, 2006 to February, 2017. Three kinds of data sets were prepared by changing the area of interest using a single image; original image without any modification (Full Image), image containing a facial profile, upper airway, craniofacial soft/hard tissues, and image containing part of the occipital region (upper left corner of the image; Head Only). A radiologist and an orthodontist also performed a manual cephalometric analysis of the Full Image for comparison. Observers were blinded to the patient groupings. Data analysis was performed from April, 2018 to August, 2019. When the predictive score obtained from the DCNN analysis exceeded the threshold (0.50), the patient was judged to have OSA. The primary outcome was diagnostic accuracy in terms of area under the receiver-operating characteristic curve.

Results
The sensitivity/specificity was 0.87/0.82 for Full Image, 0.88/0.75 for Main Region, 0.71/0.63 for Head Only, and 0.54/0.80 for the manual analysis. The area under the curve was the highest for Main Region (0.92): 0.89 for Full Image, 0.70 for Head Only, and 0.75 for the manual analysis.

Conclusion
A DCNN identified individuals with OSA with high accuracy. This is a useful approach that does not require any laborious analyses in a primary care setting or in remote areas where an initial specialized OSA diagnosis is not feasible.

Support
This study was supported in part by the Japan Society for the Promotion of Science (grant numbers 17K11793, 19K10236).
0607 Clinical Phenotypes of OSA in Diverse Hispanics/Latinos: Results from the Hispanic Community Health Study/Study of Latinos
Authors: Gonzalez K T , Tarraf W , Wallace D M , Stickel A , Schneiderman N , Redline S , Patel S R , Gallo L C , Mossavar-Rahmani Y , Daviglus M , Zee P C , Talavera G A , Sotres-Alvarez D , Gonzalez H M , Ramos A R 
Published: May 2020

Abstract

Introduction
Recent work on US non-Latino Whites and Europeans from clinical samples used obstructive sleep apnea (OSA) symptoms to generate OSA phenotypes for individuals with moderate-severe OSA and proposed between 3-5 clusters. Validating these clusters in a diverse Hispanic/Latino community-based population with different biopsychosocial characteristics is crucial for early OSA identification and more personalized treatment.

Methods
This work is based on baseline data from The Hispanic Community Health Study/Study of Latinos (HCHS/SOL). HCHS/SOL is a prospective cohort study designed using a multisite (Bronx, NY, Chicago, IL, Miami, FL, San Diego, CA) multistage probability sample. The subpopulation of interest included adults 18-74 years (unweighted n=1,623) meeting criteria for moderate-severe OSA symptoms (≥15 Apnea-Hypopnea index (AHI) events per hour). We performed latent class analysis (LCA) using 15 common OSA symptoms to identify phenotype clusters.

Results
Average age was 52.4 ± 13.9 years and 34.1% were female. Mean AHI was 33.8 ± 22.5 events per hour. Fit statistics and clinical significance suggested that a three-class solution provided best fit to the data. The symptom profiles were consistent with (1) a Minimally Symptomatic group (46.8%), (2) a Disturbed Sleep group (38.1%), and (3) a Daytime Sleepiness group (15.1%). Validation analyses using alternative hierarchical and partitioning algorithms also suggested support for a three-class solution.

Conclusion
Sleep apnea phenotypes among diverse Hispanics/Latinos were consistent with recent findings from the Sleep Apnea Global Interdisciplinary Consortium. However, we found notable differences in the prevalence of these clusters relative to Whites. This suggests that other biopsychosocial factors may be contributing to OSA phenotypes among Hispanics/Latinos. Identification of OSA phenotypes in Hispanics/Latinos could inform better sleep interventions and therapeutics and help better align public health resources.

Support
5R01AG048642-05; R21AG056952; R21HL140437.