Sleep Diagnoses Research Articles

Effect of positive airway pressure treatment on excessive fragmentary myoclonus in 100 sleep-related breathing disorder patients

Study objectivesExcessive fragmentary myoclonus (EFM) is a frequent finding in patients undergoing video-polysomnography (VPSG). We aimed to evaluate the potential effect of sleep-related breathing disorder's treatment with positive airway pressure (PAP) therapy on EFM. MethodsOne hundred consecutive patients with EFM and sleep-related breathing disorder subsequently treated with PAP at the sleep lab of the Medical University of Innsbruck, Department of Neurology, Austria, were included. Each patient underwent two nights of VPSG: the first night without and the second night with PAP therapy. Fragmentary myoclonus was automatically scored with validated software, and fragmentary myoclonus index (FMI) and minutes of non-rapid eye movement (NREM) sleep with EFM (minNREM+EFM) were calculated. ResultsUnder PAP therapy there was a significant decrease in the minNREM+EFM — 60.5 (9.5–161.8) at baseline vs. 37.5 (6.3–168.8) minutes under PAP, p = 0.025. No significant differences were observed for FMI between the two nights. Sleep variables, sleep diagnoses, comorbidities, and medication did not influence FMI or the minNREM+EFM. ConclusionsThe initiation of PAP treatment led to a significant reduction of minNREM+EFM, but not of FMI. The results suggest that PAP therapy might influence the distribution of FM potentials.

Diagnosis of sleep disorders in child healthcare settings

ObjectivesSleep disorders impact at least 10 % of children, pose risks to overall wellbeing, and are key targets of preventive interventions. The objectives of this study were to describe the prevalence of pediatric sleep disorder diagnoses across sociodemographic characteristics and co-occurring conditions, and to explore potential sociodemographic disparities. MethodsCross-sectional analysis of 12,394,902 children (0–17 years; 50.9 % Medicaid-insured) in the 2017 MarketScan database. Prevalence was assessed utilizing ICD-10 codes, with multivariate logistic regressions examining disparities (insurance coverage; race and ethnicity in Medicaid-insured) for diagnoses in ≥0.10 % of children. ResultsThe prevalence of sleep disorder diagnoses was 2.36 %. The most common diagnoses were obstructive sleep disordered breathing (oSDB, 1.17 %), unspecified sleep disorders (0.64 %), insomnia (0.52 %), and other SDB (0.10 %), with <0.10 % for all other diagnoses. Insomnia and parasomnias diagnoses were much lower than diagnostic estimates. Sleep diagnoses were more prevalent in Medicaid versus commercially insured youth, 2-5-year-olds, and in children with co-occurring medical, neurodevelopmental, or behavioral health conditions. Girls and boys were generally equally likely to be diagnosed with any sleep disorder. In Medicaid-insured children, white children were more likely to have any sleep diagnosis compared to all other racial and ethnic groups. Black/African American children were more likely than white children to have oSDB. ConclusionsCompared to diagnostic estimates, claims data suggest sleep disorders are under-diagnosed, with notable sociodemographic disparities. Findings suggest a need for clinical resources to identify and address sleep disorders and to understand biases potentially driving disparities, given that sleep is a modifiable determinant of child wellbeing.

From trenches to technology: a narrative review of sleep medicine in the military.

Thomas CL, Carr K, Yang F, etal. From trenches to technology: a narrative review of sleep medicine in the military. J Clin Sleep Med. 2024;20(6):973-981.

SLEEP DISORDERS AND RISK OF MID- TO LATE-LIFE SUICIDE ATTEMPT

Abstract Sleep is a compelling target for suicide prevention in later life, as 50% of older adults experience sleep difficulties. The primary objective of the current study was to evaluate whether sleep disorders increase risk of late-life suicide attempt and death by suicide. We examined a nationally based cohort of over 5 million US veterans 50 years and older, who used Department of Veterans Affairs (VA) health services. Veterans with a sleep diagnosis (i.e., sleep apnea, insomnia, hypersomnia, and others) at baseline (October 1, 2011, to September 30, 2013) or earlier (October 1, 2007, to September 30, 2011) were propensity matched 1:1 with patients without these diagnoses based on demographic characteristics and Charlson Comorbidity Index (Total N=1,990,362; 995,181 each group). Secondary analyses examined psychiatric disorders as potential moderators. Fully adjusted models (additionally adjusted for psychiatric disorders) demonstrated that patients with sleep disorders had a 23% increased risk of any suicide attempt (HR 1.23, 95% CI [1.20-1.27]), while risk mitigated for lethality of attempt (HR 0.98, 95% CI [0.92-1.03]). Depression, PTSD, and generalized anxiety disorder were all approximately twice as prevalent among the sleep disorder group. Among veterans with a psychiatric disorder, any sleep disorder diagnosis was associated with a 28% increased risk for suicide attempt, which attenuated among those without a psychiatric diagnosis. In conclusion, sleep disorder diagnoses conferred increased risk for suicide attempt, especially in older adults with psychiatric disorders. These findings suggest that treatment of sleep disorders may mitigate risk of suicide attempts in older vulnerable veterans.

Sleep disorders in patients with chronic tinnitus

Tinnitus has a lifetime prevalence of 25% in Germany. A common comorbidity in chronic cases are sleep disorders. The aims of this study were to detect sleep disorders and to identify possible associations with tinnitus parameters.Fifty patients with chronic tinnitus were recruited. The patients underwent audiometry, polysomnography, and completed standardised questionnaires on tinnitus and sleep behaviour.Data were available in 30 men and 9 women (age 50.2 ± 11 y, BMI 28.8 ± 4.4 kg/m²). The median duration of tinnitus was 36 (9; 120) months with a severity score of 2.00 (1.00; 3.00). The mean Tinnitus Questionnaire (TF) score was 43.6 ± 17.1, the Epworth Sleepiness Scale (ESS) score was 8.41 ± 4.27, the Pittsburgh Sleep Quality Index (PSQI) score was 9.21 ± 4.32, and the Screening Scale for Chronic Stress (SSCS) score was 58.13 ± 9.58.Sleep diagnoses included 18 cases of insomnia, 4 cases of RLS, and 11 cases of OSA. Patients with sleep comorbidities showed higher tinnitus severity, PSQI scores, and body weight compared to those without sleep disorders.Worse sleep quality was associated with higher tinnitus severity (p=0.038) and more disruptive tinnitus (p=0.03). Patients with subjectively highly disruptive tinnitus reported higher chronic stress scores. Tinnitus duration was correlated with OSA-severity (p=0.026).More than two-thirds of tinnitus patients showed sleep disorders as comorbidity. A sleep screening appears useful in cases of increased tinnitus severity. Whether CPAP therapy is helpful in reducing tinnitus symptoms could not be conclusively determined but deserves further attention.

Health Disparities in Pediatric Sleep.

This article reviews disparities in pediatric sleep health and sleep disorders from early childhood through adolescence (birth to age 18years). Sleep health is a multidimensional construct including sleep duration, consolidation, and other domains, whereas sleep disorders reflect both behaviorally (eg, insomnia) and medically based (eg, sleep disordered breathing) sleep diagnoses. Using a socioecological framework, we review multilevel (ie, child, family, school, health-care system, neighborhood, and sociocultural) factors linked to sleep health disparities. Mechanistic research and studies using an intersectional lens to understand overlapping marginalized identities are needed to inform multilevel interventions to promote sleep health equity in pediatrics.

0694 Sleep Disorders and Serious Mental Illnesses: Comorbidity, Treatment, &amp; Demographic Factors in VA Health Record Data

Abstract Introduction People diagnosed with serious mental illnesses (SMI), including schizophrenia-spectrum disorders, bipolar disorders, and major depression with psychosis, report high rates of clinically significant sleep disturbance. Yet, examination of how demographic- and health-related factors impact diagnosis and treatment in this group is scarce. We aimed to fill this gap by examining medical records of Veterans receiving treatment in VA VISN 4 healthcare facilities. Methods We used VA health record data to examine comorbidity between SMI and sleep diagnoses and receipt of sleep treatment from 2011-2019 (total N=573,974). A combination of descriptive and statistical techniques were used to investigate prevalence of sleep disorders, receipt of sleep treatment, and associations with demographic- and health-related factors. Results In 2019, 21.8% of Veterans with SMI were diagnosed with sleep disorder(s) in the medical record. White Veterans with SMI were more likely to have a sleep disorder than Veterans identifying as racial minorities. Veterans with SMI and sleep disorders were also younger, had higher BMIs, and had more medical comorbidities. Veterans with SMI were more likely to receive sleep treatment than Veterans without SMI, primarily driven by higher rates of sleep medication. Importantly, in contrast to findings regarding sleep disorder comorbidity, Veterans identifying as racial minorities received sleep treatment at a higher rate than White Veterans, and Veterans with higher BMIs received sleep treatment at a lower rate. Demographic factors including sex, age, and rurality were also associated in varying ways with rates of sleep treatment. Conclusion Our findings suggest that Veterans with SMI are being treated for sleep disorders at higher rates than Veterans without SMI. However, diagnosis and treatment are differentially impacted by demographic and health-related factors. Work is needed to better understand drivers of demographic differences and to examine any potential inequities in diagnosis or treatment of sleep disorders in Veterans with SMI. Support (if any) This work was funded by the Veterans Health Foundation (Co-PIs: Bonfils &amp; Longenecker). Additional support was provided by pilot project funds (PI: Bramoweth) from the Department of Veterans Affairs VISN 4 Mental Illness Research, Education and Clinical Center and the Mississippi Center for Clinical &amp; Translational Research (U54GM115428).

0787 Sleep Problems and Sleep Disorders in Children with Rubinstein-Taybi Syndrome

Abstract Introduction Rubinstein-Taybi syndrome (RSTS) is a genetic condition which characterized by distinctive facial features, broad and angulated thumbs and halluces, short stature, and moderate-to-severe intellectual disability. Sleep problems have been reported in children with RSTS. However, most studies were case reports in adult population. There is limited information on details about sleep problems in children with RSTS. The purpose of this study was to: (1.) Describe the demographics, clinical presentation of children with RSTS; (2.) Describe polysomnographic findings, sleep diagnoses and treatments of children with RSTS. Methods A retrospective study was performed in patients with RSTS (ages 0-20 years) who underwent polysomnography at Cincinnati Children’s Hospital Medical Center from January 1, 2000 to October 31, 2022. Demographics, clinical presentation, polysomnographic findings, diagnoses and treatments were reviewed. Descriptive statistics were utilized. Results Thirteen children with RSTS were included, 69% were male (N=9). The median age was 6.3 years (range 13 days to 12.6 years). The most common presenting symptoms included snoring (N=9, 69.2%), pauses of breathing (N=6, 46.2%) and nighttime awakenings (N=5, 38.5%). The age of onset of sleep symptoms ranged from birth to 5 years. From polysomnographic findings, the most common diagnosis was obstructive sleep apnea (OSA) (N=11, 84.6%) with obstructive apnea-hypopnea index ranging from 2.8 to 72.7/h, (mild, N=3; moderate, N=3; severe; N=5). All children with mild OSA (N=3) were treated with medications, while children with moderate-to-severe OSA were managed with PAP therapies (N=4), supplemental oxygen (N=3), and surgeries (N=3). Other diagnoses included central sleep apnea (N=2) and periodic limb movement disorder (N=2), which were treated with supplemental oxygen and iron supplements, respectively. Conclusion The most common presenting sleep symptoms in children with RSTS were snoring, pauses of breathing and nighttime awakening. The age of onset of symptoms was variable, but usually occurred during infancy through childhood. OSA was the most common diagnosis in these populations. Physicians should recognize the early signs and symptoms of sleep problems in children with RSTS to allow early diagnosis and appropriate treatment. Support (if any) Cincinnati Children's Hospital Research Fund

Sleep disorders in veterans with serious mental illnesses: prevalence in Veterans Affairs health record data.

This study aimed to estimate the 12-month prevalence of diagnosed sleep disorders among veterans with and without serious mental illnesses (SMI) in Veterans Affairs health record data in 2019. We also examined diagnosed sleep disorders across a 9-year period and explored associations with demographic and health factors. This study used health record data from VISN 4 of the Veterans Health Administration from 2011 to 2019. SMI diagnoses included schizophrenia and bipolar spectrum diagnoses as well as major depression with psychosis. Sleep diagnoses included insomnias, hypersomnias, sleep-related breathing disorders, circadian rhythm sleep-wake disorders, and sleep-related movement disorders. Demographic and health-related factors were also collected from the record. In 2019, 21.8% of veterans with SMI were diagnosed with a sleep disorder. This is a significantly higher proportion than for veterans without SMI, 15.1% of whom were diagnosed with a sleep disorder. Sleep disorder rates were highest in veterans with a chart diagnosis of major depression with psychosis. From 2011 to 2019, the overall prevalence of sleep disorders in veterans with SMI more than doubled (10.2%-21.8%), suggesting improvements in the detection and diagnosis of sleep concerns for this group. Our findings suggest that identification and diagnosis of sleep disorders for veterans with SMI has improved over the past decade, though diagnoses still likely underrepresent actual prevalence of clinically relevant sleep concerns. Sleep concerns may be at particularly high risk of going untreated in veterans with schizophrenia-spectrum disorders. Bonfils KA, Longenecker JM, Soreca I, etal. Sleep disorders in veterans with serious mental illnesses: prevalence in Veterans Affairs health record data. J Clin Sleep Med. 2023;19(9):1651-1660.

Mental disorder prevalence in chronic pain patients using opioid versus non-opioid analgesics: A registry-linkage study.

Chronic pain and mental disorders are leading causes of disability worldwide. Individuals with chronic pain are more likely to experience mental disorders compared to individuals without chronic pain, but large-scale estimates are lacking. We aimed to calculate overall prevalence of mental health diagnoses from primary and secondary care among individuals treated for chronic pain in 2019 and to compare prevalence among chronic pain patients receiving opioid versus non-opioid analgesics, according to age and gender. It is a population-based cohort study. Linked data from nationwide health registers on dispensed drugs and diagnoses from primary (ICPC-2) and secondary (ICD-10) health care. Chronic pain patients were identified as all patients over 18 years of age filling at least one prescription of an analgesic reimbursed for non-malignant chronic pain in both 2018 and 2019 (N = 139,434, 69.3% women). Prevalence of any mental health diagnosis was 35.6% (95% confidence interval: 35.4%-35.9%) when sleep diagnoses were included and 29.0% (28.8%-29.3%) when excluded. The most prevalent diagnostic categories were sleep disorders (14% [13.8%-14.2%]), depressive and related disorders (10.1% [9.9%-10.2%]) and phobia and other anxiety disorders (5.7% [5.5%-5.8%]). Prevalence of most diagnostic categories was higher in the group using opioids compared to non-opioids. The group with the highest overall prevalence was young women (18-44 years) using opioids (50.1% [47.2%-53.0%]). Mental health diagnoses are common in chronic pain patients receiving analgesics, particularly among young individuals and opioid users. The combination of opioid use and high psychiatric comorbidity suggests that prescribers should attend to mental health in addition to somatic pain. This large-scale study with nation-wide registry data supports previous findings of high psychiatric burden in chronic pain patients. Opioid users had significantly higher prevalence of mental health diagnoses, regardless of age and gender compared to users of non-opioid analgesics. Opioid users with chronic pain therefore stand out as a particularly vulnerable group and should be followed up closely by their physician to ensure they receive sufficient care for both their mental and somatic symptoms.

0734 Gender Differences in US Military Personnel with Insomnia, Obstructive Sleep Apnea &amp; Comorbid Insomnia and Obstructive Sleep Apnea

Abstract Introduction Women constitute approximately 17% of the military. Yet, relatively little is known regarding sleep disorders and comorbid diagnoses effecting this gender. The aim of this study was to determine if there were gender-related differences in symptoms of sleep disorders and sleep-related impairment, comorbid sleep, behavioral medicine, and traumatic brain injury (TBI) diagnoses and polysomnographic (PSG) variables in military personnel with insomnia, OSA, and comorbid insomnia and OSA (COMISA). Methods Participants were 372 military personnel (46.2% females, 53.8% males) with an average age of 37.7±7.46 years and median BMI of 28.4 (5.50) kg/m2. Based on clinical evaluation and video-PSG, participants were diagnosed with insomnia (n = 118, 71.2% female), OSA (n = 118, 28.8% female) and COMISA (n = 136, 39.7% female). Insomnia severity, excessive daytime sleepiness, sleep quality, nightmare disorder, sleep impairment, fatigue, posttraumatic stress disorder (PTSD), anxiety, depression symptoms, and traumatic brain injury (TBI) were evaluated with validated questionnaires. Descriptive statistics, parametric and non-parametric pairwise comparisons and effect-size analyses were used to assess differences between sexes in terms of variables of interest. Results Female service members had significantly greater symptoms of nightmare disorder, PTSD, anxiety, and depression than their male counterparts. There were no significant differences between males and females with insomnia or OSA in sleep-related symptoms, impairment, or PSG based apnea-hypopnea index (AHI). Male service members with COMISA had a significantly greater AHI; females with COMISA had significantly greater symptoms of nightmare disorder, PTSD and anxiety. Conclusion In the largest study to date to clinically evaluate sleep disorders in military personnel, COMISA was the most frequent diagnosis. Surprisingly, there were minimal differences in self-reported symptoms of sleep disorders and PSG characteristics. However, male servicemembers with COMISA have more severe sleep disordered breathing while female servicemembers with COMISA have greater mental health morbidity. Military service may result in distinct sleep disorder phenotype(s). Support (if any) This work was supported by the Defense Health Agency, Defense Medical Research and Development Program, Clinical Research Intramural Initiative for Military Women’s Health (DM170708; Mysliwiec) and the US Air Force (USAF) Air Force Materiel Command (AFMC), Wright Patterson Air Force Base, Ohio (FA8650-18-2-6953; Peterson).

Predicting benzodiazepine prescriptions: A proof-of-concept machine learning approach.

Benzodiazepines are the most commonly prescribed psychotropic medications, but they may place users at risk of serious adverse effects. Developing a method to predict benzodiazepine prescriptions could assist in prevention efforts. The present study applies machine learning methods to de-identified electronic health record data, in order to develop algorithms for predicting benzodiazepine prescription receipt (yes/no) and number of benzodiazepine prescriptions (0, 1, 2+) at a given encounter. Support-vector machine (SVM) and random forest (RF) approaches were applied to outpatient psychiatry, family medicine, and geriatric medicine data from a large academic medical center. The training sample comprised encounters taking place between January 2020 and December 2021 (N = 204,723 encounters); the testing sample comprised data from encounters taking place between January and March 2022 (N = 28,631 encounters). The following empirically-supported features were evaluated: anxiety and sleep disorders (primary anxiety diagnosis, any anxiety diagnosis, primary sleep diagnosis, any sleep diagnosis), demographic characteristics (age, gender, race), medications (opioid prescription, number of opioid prescriptions, antidepressant prescription, antipsychotic prescription), other clinical variables (mood disorder, psychotic disorder, neurocognitive disorder, prescriber specialty), and insurance status (any insurance, type of insurance). We took a step-wise approach to developing a prediction model, wherein Model 1 included only anxiety and sleep diagnoses, and each subsequent model included an additional group of features. For predicting benzodiazepine prescription receipt (yes/no), all models showed good to excellent overall accuracy and area under the receiver operating characteristic curve (AUC) for both SVM (Accuracy = 0.868-0.883; AUC = 0.864-0.924) and RF (Accuracy = 0.860-0.887; AUC = 0.877-0.953). Overall accuracy was also high for predicting number of benzodiazepine prescriptions (0, 1, 2+) for both SVM (Accuracy = 0.861-0.877) and RF (Accuracy = 0.846-0.878). Results suggest SVM and RF algorithms can accurately classify individuals who receive a benzodiazepine prescription and can separate patients by the number of benzodiazepine prescriptions received at a given encounter. If replicated, these predictive models could inform system-level interventions to reduce the public health burden of benzodiazepines.

Prevalence and predictors for postpartum sleep disorders: a nationwide analysis

Objective To describe the prevalence and predictors of postpartum sleep disorders. Design A retrospective cohort study. Setting Postpartum. Population Commercially insured women delivering in California (USA) between 2011 and 2014. Methods Using the Optum Clinformatics Datamart Database. Main outcome measures Prevalence of a postpartum sleep disorder diagnosis with and without a depression diagnosis up to 12 months following hospital discharge for inpatient delivery. We also identified predictors of a postpartum sleep disorder diagnosis using multivariable logistic regression. Results We identified 3535 (1.9%) women with a postpartum sleep disorder diagnosis. The prevalence of sleep disorder diagnoses was insomnia (1.3%), sleep apnea (0.25%), and other sleep disorder (0.25%). The odds of a postpartum sleep disorder were highest among women with a history of drug abuse (adjusted odds ratio (aOR): 2.70, 95% confidence interval (CI): 1.79–4.09); a stillbirth delivery (aOR: 2.15, 95% CI: 1.53–3.01); and chronic hypertension (aOR: 1.82; 95% CI: 1.57–2.11). A comorbid diagnosis of a postpartum sleep disorder and depression occurred in 1182 women (0.6%). These women accounted for 33.4% of all women with a postpartum sleep disorder. The strongest predictors of a comorbid diagnosis were a history of drug abuse (aOR: 4.13; 95% CI: 2.37–7.21) and a stillbirth delivery (aOR: 2.93; 95% CI: 1.74–4.92). Conclusions Postpartum sleep disorders are underdiagnosed conditions, with only 2% of postpartum women in this cohort receiving a sleep diagnosis using International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes. Insomnia was the most common disorder and one-third of women diagnosed with a postpartum sleep disorder had a co-morbid diagnosis of depression. Future studies are needed to improve the screening and diagnostic accuracy of postpartum sleep disorders.

Sleep Quality in An Acute Inpatient Rehabilitation Setting: The Importance of Prior Sleep Disorders

<h3>Research Objectives</h3> To assess whether demographic and clinical factors might impact sleep quality during inpatient stay in a rehabilitation setting. <h3>Design</h3> In this prospective study, patients were assessed for their sleep quality at admission and discharge. <h3>Setting</h3> Patients admitted in the acute inpatient rehabilitation unit of Casa Colina Hospital and Centers for Healthcare (Pomona, California) were included in the study. <h3>Participants</h3> Thirty five patients participated to this study (age=61±16 years old; BMI=30±7kg/m2; 18 females, 18 Caucasians). <h3>Interventions</h3> None. <h3>Main Outcome Measures</h3> The Pittsburg Sleep Quality Index (PSQI) was administered as primary outcome. Demographic and clinical data were collected as covariates (i.e., age, BMI, gender, race, cause of hospitalization, pre-hospital setting and prior diagnosis of sleep disorders). The level of anxiety and depression were also assessed using Beck Anxiety Inventory (BAI) and Patient Health Questionnaire-9 (PHQ-9). <h3>Results</h3> The average length of hospitalization was 18±8 days. Cause of hospitalization included stroke (20%), spinal cord injury (28%), orthopedic (28%) and other diagnosis (23%). In this sample, 22% had a sleep disorder diagnosis (mostly sleep apnea) and 60% came from an acute care unit. ANOVAs with repeated measures were performed on the outcome measure (PSQI) controlling for BMI and age. Among the demographic and clinical variables considered, the presence of sleep disorders had an effect on changes in PSQI total scores. The PSQI decreased significantly in patients with prior diagnosis of sleep disorders. The pre-hospital setting trended toward statistical significance only for the group without sleep disorders. The initial level of anxiety or depression did not have an effect on the PSQI scores. However, changes in BAI and PHQ-9 total scores were related to changes in PSQI. Finally, our dataset did not reveal an effect of race or gender. <h3>Conclusions</h3> Prior sleep disorders diagnoses factors has an impact on sleep quality during hospitalization. These results identify high-risk patient characteristics that impact sleep hygiene which will be taken into consideration in developing an intervention for improving inpatient sleep hygiene. <h3>Author(s) Disclosures</h3> The authors have nothing to declare.

A comprehensive evaluation of insomnia, obstructive sleep apnea and comorbid insomnia and obstructive sleep apnea in US military personnel.

The aim of this study was to characterize the sleep disorders of insomnia, obstructive sleep apnea (OSA), and comorbid insomnia and OSA (COMISA) in active duty military personnel. Prospective observational study of 309 military personnel with a mean age of 37.17 years (SD = 7.27). Participants served in four branches of the U.S. military (47.9% Air Force, 38.8% Army, 11.3% Navy, and 1.9% Marines). Sleep diagnoses were rendered after video-polysomnography and a clinical evaluation. Validated self-report measures assessed insomnia severity, excessive daytime sleepiness, sleep quality, disruptive nocturnal behaviors, nightmare disorder, shift work disorder (SWD), sleep impairment, fatigue, posttraumatic stress disorder (PTSD) symptoms, anxiety, depression, and traumatic brain injury (TBI). General linear models and Pearson chi-square tests were used for between-group differences in data analyses. Insomnia was diagnosed in 32.7%, OSA in 30.4% and COMISA in 36.9%. Compared to military personnel with OSA alone, those with insomnia only and COMISA had significantly greater insomnia severity, disruptive nocturnal behaviors, sleep-related impairment, rates of nightmare disorder, and poorer sleep quality (all Ps < .05). They also reported greater symptoms of fatigue, PTSD, anxiety, and depression (all Ps < .05). There were no significant differences among the three sleep disorder diagnostic groups on sleepiness, SWD, or TBI. Military personnel with insomnia only and COMISA overall report worsened symptoms of sleep disorders, sleep-related impairment, fatigue, and psychiatric disorders than those with OSA. Results highlight the importance of a comprehensive assessment for sleep-related impairment, sleep, and comorbid disorders in military personnel with clinically significant sleep disturbances.

Rates of diagnoses of sleep disorders in children with chronic medical conditions.

This investigation examines sleep disorder (SD) diagnoses in a large population of children and adolescents with chronic medical conditions (CMCs). Little is known about SD diagnoses in this population. The large population used in this study allowed examination of SD rates by CMC type and demographics. Data were from the Coordinated Health Care for Complex Kids (CHECK) project designed for Medicaid-funded children and adolescents with at least 1 CMC from a large metropolitan area. The study population (n = 16,609) was limited to children and adolescents, 0 to 18 years of age. SD and CMC diagnoses were obtained from Medicaid claims data. Fourteen percent of the population (mean age of 9.1 years [standard deviation = 5.2]; 35.8% African American; 56.4% male; 77 with more than 1 CMC) received a sleep disorder diagnosis. The most frequent diagnosis was sleep-disordered breathing (11.2%), followed by nocturnal enuresis (1.2%) and insomnia (1%). SDs were diagnosed more frequently in those with multiple CMCs than in those with 1 CMC (19.7% vs 5.8%; P < .001). Insomnia rates in Hispanic/Latinx (1.2%) and African American (0.8%) children and adolescents were significantly lower (both P < .001) than in Caucasians (3.5%). Odds of receiving a sleep diagnosis varied among CMCs. Our analysis of Medicaid claims data of a large urban cohort offers detailed information about the rates of sleep diagnoses and suggests underdiagnosis of SDs in this vulnerable, high-risk, primarily ethnic minority population. Underrecognition of sleep disorders has short- and long-term health and economic consequences. Study results may help clinicians implement appropriate SD screening and management for children and adolescents with CMCs. Adavadkar PA, Pappalardo AA, Glassgow AE, etal. Rates of diagnoses of sleep disorders in children with chronic medical conditions. J Clin Sleep Med. 2022;18(8):2001-2007.

0487 Exploration of Sleep Problems and Medication Use for Youth Residing in Juvenile Justice Facilities

Abstract Introduction Insufficient and disordered sleep are common among developing adolescents and can result in poor health and behavioral consequences. Previous studies have examined sleep and adolescent criminal behavior; however, little is known about adolescent sleep difficulties or disorders while residing in juvenile detention and treatment facilities. The current study explores psychiatric and sleep disorder diagnoses and medication use of youth under the care of the Department of Juvenile Services (DJS). Methods Participants were recruited from 11 detention and treatment facilities across Maryland. Youth (N = 67) were 13-19 years old (M = 16.8, SD = 1.2) and 84% male and 16% female. Racial Backgrounds: 55% Black, 18% White, 14% Multiracial, and 12% Other. A Healthcare staff member from each facility completed an online medical questionnaire regarding each youth’s sleep history, medical diagnoses, and current medications. Results The most common youth diagnoses were Insomnia (N = 26), ADHD (N = 26), and Anxiety (N = 12) with 72% of youth having more than one psychiatric/sleep disorder diagnosis. The most frequently used medications were melatonin (N = 23), trazodone (N = 12), and quetiapine (N=7) and 58% were on more than one sleep/psychotropic medication. Healthcare staff ordered 25 behavioral sleep studies (e.g., DJS behavioral sleep studies consists of night resident staff tracking if youth is asleep/awake while making rounds), resulting in diagnoses of insomnia (N=9) and parasomnia (N=1). Youth with sleep studies were prescribed the following medications: trazodone (N = 5), melatonin (N = 3), other (e.g., methylphenidate, clonidine, N = 11). Conclusion These preliminary findings suggest that youth are experiencing poor sleep quality while residing in juvenile justice facilities; over a third of the youth are struggling with insomnia and are prescribed Melatonin. Further data analyses will provide a better understanding of the youths’ sleep problems and the effects on their overall health and well-being. Support (If Any) This research is made possible by the American Academy of Sleep Medicine (AASM) Foundation Award #22-CS-19 and Department of Juvenile Services’ supportive collaboration.

0325 A Comprehensive Evaluation of Sleep Disorders in Male and Female U.S. Military Personnel

Abstract Introduction Sleep disorders are increasingly recognized in military personnel. However, no study has comprehensively evaluated male and female service members with clinically significant sleep disturbances. While, insomnia and obstructive sleep apnea (OSA) are the two most recognized sleep disorders, some studies have suggested that comorbid insomnia and OSA, also known as COMISA, potentially is the most frequent sleep disorder. Further little is known regarding the co-occurrence of nightmares, shift work disorder, depression, anxiety, and posttraumatic stress disorder (PTSD) in this population. Methods Participants were 309 active duty service members (females n = 113, male n = 196) in all branches of the military who underwent a clinically indicated sleep evaluation in a military sleep disorders center. All underwent an attended in-lab polysomnogram, were diagnosed with insomnia, OSA, or COMISA and completed self-report measures. Participants completed the Nightmare Disorder Index and Shift Work Disorder Index, and non-sleep questionnaires using the PCL-5 for post-traumatic stress disorder (PTSD), the PHQ-9 for depression, the GAD-7 for anxiety, and History of Head Injuries for traumatic brain injury (TBI). Results COMISA was diagnosed in 36.8% of the sample, insomnia in 32.7%, and OSA in 30.4%. Males were significantly more likely to have COMISA or OSA and females were more likely to have insomnia. Polysomnographic variables were consistent with the respective sleep diagnoses. Forty service members (12.9%) met criteria for nightmare disorder; those with OSA were significantly less likely to have nightmares. Shift work disorder was present in 49 (15.9%) and did not differ between sleep diagnoses. PTSD was present in 57 (18%) and those with COMISA were significantly more likely to have PTSD. A history of head injuries was reported by 38.2% and there was no difference in rates between the sleep disorder groups. Conclusion The most frequent sleep disorder profile in service members with sleep disturbances was COMISA, which was associated with significantly higher rates of PTSD and anxiety. Conversely, OSA alone was not associated with higher rates of any comorbid disorders. Nightmare disorder and shift work are relatively prevalent in military personnel with sleep disorders. Support (If Any) This work was supported by the Defense Health Agency, Defense Medical Research and Development Program, Clinical Research Intramural Initiative for Military Women’s Health (DM170708; Mysliwiec), and the Air Force Research Laboratory, Wright Patterson Air Force Base, Ohio (FA8650-18-2-6953; Peterson). The views expressed herein are solely those of the authors and do not represent an endorsement by or the official policy or position of the U.S. Air Force, the U.S. Army, the Defense Health Agency, the Department of Defense, the Department of Veterans Affairs, or the U.S. Government.

An Attention-Guided Spatiotemporal Graph Convolutional Network for Sleep Stage Classification.

Sleep staging has been widely used as an approach in sleep diagnoses at sleep clinics. Graph neural network (GNN)-based methods have been extensively applied for automatic sleep stage classifications with significant results. However, the existing GNN-based methods rely on a static adjacency matrix to capture the features of the different electroencephalogram (EEG) channels, which cannot grasp the information of each electrode. Meanwhile, these methods ignore the importance of spatiotemporal relations in classifying sleep stages. In this work, we propose a combination of a dynamic and static spatiotemporal graph convolutional network (ST-GCN) with inter-temporal attention blocks to overcome two shortcomings. The proposed method consists of a GCN with a CNN that takes into account the intra-frame dependency of each electrode in the brain region to extract spatial and temporal features separately. In addition, the attention block was used to capture the long-range dependencies between the different electrodes in the brain region, which helps the model to classify the dynamics of each sleep stage more accurately. In our experiments, we used the sleep-EDF and the subgroup III of the ISRUC-SLEEP dataset to compare with the most current methods. The results show that our method performs better in accuracy from 4.6% to 5.3%, in Kappa from 0.06 to 0.07, and in macro-F score from 4.9% to 5.7%. The proposed method has the potential to be an effective tool for improving sleep disorders.

Characterizing sleep disorders in an autism-specific collection of electronic health records

Objective/backgroundSleep problems are common in people on the autism spectrum. This study reviews one detailed approach to querying the electronic health record (EHR) in a large tertiary care center. Patients/methodsWe developed methods for identifying people on the autism spectrum and defined their sleep problems using the key words, “sleep” or “melatonin”, or International Classification of Diseases (ICD) codes. We examined treatment responses of these individuals to melatonin supplementation. ResultsSleep problems were documented in 86% of patients with ages ranging from 6 to 30 years old. Our specific keyword search yielded more patients with sleep diagnoses than ICD codes alone. About two-thirds of patients who received melatonin supplementation reported benefit from its use. ConclusionsOur study provides a framework for using deidentified medical records to characterize sleep, a common co-occurring condition, in people on the autism spectrum. Using specific keywords could be helpful in future work that queries the EHR.