bluuebunny/crossref_metadata_2025 huggingface dataset:
0495 Association of Cannabis Use and Symptoms Among Patients with Obstructive Sleep Apnea
Authors: Inaya Andira , Baldassarri Stephen , Sands Scott , Yaggi Henry , Wellman Andrew , Redeker Nancy , Zinchuk Andrey
Published: April 2024

Introduction

The use of recreational substances such cannabis is linked to sleep quality. Long-term cannabis use has been linked to increased sleep latency, less slow wave sleep, and worse sleep efficiency. However, little is known about the relationship between cannabis use and subjective OSA symptoms. We examined the association of cannabis use and symptoms among patients newly diagnosed with and yet to be treated for OSA.

Methods

This was an interim analysis of participants enrolled in the NICEPAP Study (n=182/267, NCT05067088) a prospective observational study assessing physiological predictors of CPAP effectiveness. Participants self-reported cannabis use over a six-month period prior to OSA diagnosis. Outcomes included symptoms of pain affecting sleep, parasomnias, nightmares, sleep latency, sleep duration, insomnia symptoms (Insomnia Severity Index), sleepiness (Epworth Sleepiness Scale), sleep quality PROMIS (Patient-Reported Outcomes Measurement Information System) and OSA-related quality of life FOSQ (Functional Outcomes of Sleep Questionnaire). Groups were compared using Chi-squared, and the Mann-Whitney U tests as appropriate.

Results

We studied 182 enrolled participants, 47% were male with a mean age of 51.3 years; of this total sample, 32 patients (17%) reported cannabis use. Users of cannabis reported more regular alcohol use and tobacco use (72% vs 46% and 44% vs. 29% respectively, p< 0.01). No differences were observed in other demographics, sleep latency, self-report sleep duration or OSA severity. Cannabis users reported higher but not statistically significant proportion of pain during sleep (38% vs 25%, p=0.17). No differences from non-users were noted for other presenting symptoms including nightmares, parasomnias, insomnia, sleepiness, sleep quality or OSA-related quality of life.

Conclusion

One in six individuals in this cohort of individuals recently diagnosed with OSA, reported use of cannabis. Cannabis users also noted higher consumption of alcohol and tobacco. There was a trend for higher prevalence of pain affecting sleep. Notably, no other statistical differences were noted upon analysis of daytime and nighttime symptoms. Further research is needed to better understand the long-term effects of cannabis on subjective symptoms of sleep in OSA patients.

Support (if any)

This study was supported by the Parker B. Francis Foundation, National Heart, Lung, and Blood Institute NIH/NHLBI
0497 Obstructive Sleep Apnea Prevalence in a Young Mixed Trauma Population with Post-traumatic Stress Disorder Symptoms
Authors: Jordan Amy , Cherian Darshana , Pan Maddie , Bruce Gemma , Schenker Maya , Zeng Liz , Felmingham Kim
Published: April 2024

Introduction

Disturbed sleep appears to play a role in post-traumatic stress disorder (PTSD) development and maintenance. The prevalence of obstructive sleep apnea (OSA, as defined as an AHI>5 events/hr) has been reported to be up to 75% in individuals with PTSD (Zhang 2017). Most prior studies investigating OSA prevalence in PTSD have been conducted in military populations, who commonly have other risk factors for OSA such as being male, middle-aged and having high rates of substance use disorders. The aim of this study was to determine whether OSA is more common in individuals with PTSD symptoms exposed to non-military traumas as compared to individuals without PTSD symptoms.

Methods

125 participants interested in completing a sleep study completed a screening questionnaire measuring previous Criterion A trauma exposure (Life events checklist) and PTSD symptoms (PTSD symptom checklist for DSM-5, PCL-5), along with alcohol use and demographic information. A sub-set were recruited for a standard overnight polysomnography either in the sleep laboratory (Grael, Compumedics), or at home (Siesta, Compumedics). Sleep studies were scored blinded to questionnaire data by an independent sleep technician. Three symptom severity groups were compared, individuals with Likely PTSD (PCL-5 >33 and Criterion A trauma exposure) Sub-Syndromal PTSD (PCL-5 15-33 and Criterion A trauma exposure) or No-PTSD (PCL-5 < 15 and no intrusive symptoms).

Results

45 participants (31 female; aged 18-48 years) completed the polysomnography. 11 had likely PTSD, 10 Sub-Syndromal PTSD and 23 had No-PTSD. The 3 groups were comparable in age (25±7 years), sex (68% women) and BMI (24.2±4.5 kg/m2). In total, 6 participants had AHI >5 events/hr; 3 in the Likely PTSD group, 2 in the Sub-Syndromal group and 1 in the No-OSA group. The mean AHI did not differ between groups (Likely PTSD = 5.1±7.7, Sub-Syndromal PTSD = 4.0±6.7, No-PTSD = 2.2±4.4 events/hr, p=.64).

Conclusion

Although the a priori sample size (n=26 in each group) has not yet been reached, these preliminary data indicate that the prevalence of OSA in a young mixed-trauma PTSD population is unlikely to be as high as has been observed previously. PTSD may only increase the risk for OSA in older/heavier individuals.

Support (if any)

University of Melbourne.
0499 Relationship Between RDW and Disease Burden in Obstructive Sleep Apnea
Authors: Abraham Stanley , Basist Madeleine , Duenas Sean , Dong Vincent , Rusk Sam , Nygate Yoav , Quintero Luis , Hahn Stella , Greenberg Harly , Talwar Arunabh
Published: April 2024

Introduction

Obstructive sleep apnea (OSA) has significant physiologic consequences. The Apnea-Hypopnea Index (AHI) is used to define severity, but AHI only reflects the rate of sleep-disordered breathing events and does not correlate well with clinical outcomes that arise from hypoxia. Percentage of cumulative time with oxygen saturation below 90% (T90) and hypoxic burden (HBI) more accurately reflect nocturnal hypoxia and may be more suitable correlates to morbidity associated with sleep apnea. Rising Red Cell Distribution Width (RDW) reflects dysregulation in erythrocyte homeostasis seen with metabolic derangements like oxidative stress and inflammation which has been shown to correlate with mortality in cardiovascular disease. Previous work demonstrates a linear relationship between RDW and AHI. We postulate there is a positive correlation between RDW, T90 and HBI.

Methods

A retrospective analysis of 1797 participants at Northwell with Home Sleep Apnea Testing or in-lab Polysomnography data and complete blood counts (CBC) within 6 months of the sleep study was performed. Subjects with chronic hypoxemia or hematologic conditions that may affect CBC were excluded. Ordinary least squares (OLS) regression was performed using RDW as the dependent variable and AHI, T90, HBI and mean saturation of peripheral oxygen (SpO2) as independent variables.

Results

OLS analysis revealed a statistically significant positive correlation between RDW and T90 (coef=.807 (CI 0.426,1.188), p-value=0.000034), HBI (coef=0.000033 (0.000019,0.000054), p-value=0.002)) and AHI (coef= 0.0062 (0.0028, 0.0097), p-value= 0.0004). A negative correlation was noted between mean SpO2 and RDW (coef=-0.047 (-0.078,-0.162), p-value=0.003).

Conclusion

There is a positive correlation with RDW and sleep apnea markers of disease burden, including T90, HBI, and mean SpO2. A 100% change in T90 (0 – 100%) in a patient will result in an increase in RDW by 0.8 and a 100% change in HBI (0 – 500 %min/hour) will result in an increase in RDW by 1.5. AHI was noted to poorly correlate with RDW. An AHI of 100 resulted in a 0.62 increase in RDW versus an AHI of 0. For the range of mean SpO2 observed (range 80 – 100%) the expected change in RDW is 0.94. This relationship requires further exploration in future studies.

Support (if any)
0501 Sleep Medicine Referrals in a High-Risk Obstetrics Population
Authors: Boyd Victoria , Shewnarain Kimberly Persaud , Oo Thae Aient Aient , Naik Sreelatha , Paglia Michael , Mackeen A Dhanya , Young Amanda , Grey Celia
Published: April 2024

Introduction

Obstructive sleep apnea (OSA) has traditionally been underdiagnosed and undertreated in pregnancy. Untreated OSA has been associated with adverse peripartum outcomes, such as hypertensive disorders of pregnancy, diabetes, cesarean delivery and NICU admissions. In August 2023, a taskforce from the Society of Anesthesia and Sleep Medicine and the Society for Obstetric Anesthesia and Perinatology published recommendations that pregnant patients with obesity, gestational or pre-existing diabetes, or hypertensive disorders of pregnancy be screened for OSA. Currently, our Maternal-Fetal Medicine (MFM) group only screens for OSA in the obese pregnant population on a conditional basis. It is unknown how many of these patients are screened for OSA and/or referred to sleep medicine for follow-up.

Methods

This is a cross-sectional retrospective study to characterize the 19,439 pregnancies from January 1, 2012-December 31, 2022 at a rural health system with five labor and delivery units that would meet criteria for screening for OSA and a sleep medicine referral. Patients were included if they were over 18, pregnant and had at least a hypertensive disorder of pregnancy, diabetes (pre-existing or gestational) or were obese. The Cochrane-Armitage trend test was used to evaluate the rates over the time-period.

Results

In the initial data review, we found that between 2012 and 2022, the practice grew considerably; there was an increase in the proportion of women with gestational diabetes (16.9-31.2%, p< 0.01), obesity (75.0-75.2%, p< 0.01) and a hypertensive disorder of pregnancy (22.9-25.8%, p< 0.01). Despite the increase in sleep medicine referrals during pregnancy (0.4-1.1%, p< 0.01) over the years, the overall referral rate to sleep medicine regardless of the selected peripartum co-morbidity remained low at 0.5-0.9%.

Conclusion

Less than 1% of patients at high-risk for OSA in pregnancy are being screened or tested for OSA. Based on the guidelines published in August 2023, these results establish a wide practice gap in our center. It is likely that this knowledge is generalizable to centers around the country. We conclude that there is a large opportunity to improve our maternal outcomes by connecting these at-risk patients with sleep medicine referrals to be screened and treated for OSA.

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0502 Impact of Excessive Daytime Sleepiness on Day-to-day Experience of Patients Suffering from Obstructive Sleep Apnea
Authors: Khan Anees , Waheed Sana , Rasul Ammar , Usama Muhammad , Usman Abira , woo Emily , Waheed Muazzam , Cheema Mustafa
Published: April 2024

Introduction

Today, excessive daytime sleepiness (EDS) is considered as one of the most frequently occurring clinical manifestations in patients with obstructive sleep apnea (OSA). According to the National Sleep Foundation 2000 Omnibus Sleep in America Poll: `a sizable proportion of adults (43%) report that they are so sleepy during the day that it interferes with their daily activities a few days per month or more; and, one out of five (20%) experience this level of daytime sleepiness at least a few days per week or more'.

Methods

Inclusion criteria The articles identified, were screened by title and abstract, and selected for full text review if they met the following inclusion criteria. 1)OSA and EDS should be primary or secondary outcomes and not incidental findings. 2)Studies showing clinical manifestations of EDS in patients with OSA such as problems with vigilance, cognitive function, memory, concentration, and mood; deterioration in school and/or job performance and productivity, social relationships, and driving skills Exclusion criteria Those articles and or studies were excluded from our analysis that included: 1)Patients who did not undergo the polysomnography.2)Patients who had pure or mainly central sleep apneas, co-morbidities, terminal illness 2)Patients with other causes of fatigue or excessive daytime sleepiness 3)Patients from pediatrics

Results

Our search strategy yielded 770 citations. Irrelevant papers were excluded by title and abstract review, leaving 72 manuscripts that were identified from the comprehensive database search. We read the titles of these articles specifically identify papers that looked at EDS and its impact on day to day life of patients suffering from. This eventually yielded the selection of 5 number of articles from their respective databases.

Conclusion

All of the included studies point towards a growing evidence that there is a strong correlation between EDS and OSA and how EDS affects the course of disease. Some negative effects of OSA are reduced productivity, cognitive dysfunction, frequent awakenings, disrupted sleep, irritability, judgment error and increased accident rates. OSA related EDS is related to neurocognitive function. Therefore, patients often have difficulty in concentrating and paying attention for long periods of time. Fine-motor coordination also gets affected.

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0504 Unraveling the Relationships Between Chronic Illness, Healthcare Access, and Obstructive Sleep Apnea in Nigeria
Authors: Olaoye Olatunji , Bolarinwa Oladimeji , Alatishe-Muhammad Bilqis , Saka Kafayat , Yakubu Bisola , Sotome anuoluwa , Adejumo Oluwatobiloba , Suberu Anavami , Oluwatoyin Aishat , Babs-Lawal Muhibat , Munirdeen Abdulrahman , Malomo Victor , Ademola Suliyat , Adewale Maryam , Babayanju Hikmat , Olaoye Olanrewaju
Published: April 2024

Introduction

Diagnosing Obstructive Sleep Apnea (OSA) in the wider population in Sub-Saharan Africa is challenging, while co-morbidity with other chronic illnesses and Cardiovascular disease is still unestablished. For the few individuals with established sleep disorders and chronic illness, poor health-seeking behaviour has contributed to this gap. This population-based survey assessed associations between chronic illness, healthcare access and OSA in Nigeria

Methods

A cross-sectional study was conducted among 2388 respondents in North Central Nigeria using the STOP-BANG Questionnaire (ESQ) tool and Epworth Sleepiness Scale (ESS). A multistage sampling technique was used. Data collected was analyzed using SPSS IBM V.23. Descriptive and inferential statistics to test associations between categorical variables were analyzed. The level of statistical significance was set at p< 0.05 at 95% confidence level Ethical approval (ERC/MOH/2023/11/164) was obtained from the Ethical Review Committee of the Kwara State Ministry of Health.

Results

Of 2388 respondents, 683 (28.6%) were less than 50 years old, and 18.2% reported chronic illness, with hypertension being the most reported (59.1%). About 92% of those with chronic illness were on medications, and only 40.7% accessed hospital follow-up. About a third (33.3%) reported that the chronic illness affected their daily work and activities. Following medical measurement, 8.5% were underweight, 21.2% overweight, and 11.4% obese. About 16.6% had elevated diastolic blood pressure, while 21.9% had elevated systolic blood pressure. Of the respondents who reported sleep problems, SBQ classified 50% of them as having moderate to high risk of OSA (p< 0.001). There were significantly higher risks of sleepiness and OSA among the respondents who reported having chronic illness in both ESS (p=0001) and SBQ (p< 0.0001) scales. Similarly, high blood pressure and obesity were significantly associated with a high risk of sleepiness and OSA in SBQ (p=0.001) and ESS(p=0.004) scales respectively. The SBQ scale also revealed a significantly lower health facility utilization rate among respondents with a high risk of sleepiness (p< 0.001)

Conclusion

There is a higher risk of sleepiness and OSA among the Nigerian population with chronic illness, many of whom have low healthcare access. This poses the danger of undiagnosed and iceberg phenomena in diagnosing OSA in low-resource settings.

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0506 Practice Patterns of Sleep Disorders Diagnoses in a Statewide Health System: An Electronic Health Record Analysis
Authors: Vyata Vishruth , Deshpande Pushkar , So Jennifer , Glick Danielle , Verceles Avelino , Fang Beverly , Rolla Krishna Jayanth , Johnston Christine , Diaz-Abad Montserrat , Wickwire Emerson
Published: April 2024

Introduction

A substantial proportion of medical care is delivered through hospital networks and health systems. Yet relatively little is known about how sleep disorders are managed from a health systems perspective. Such insight is vital to improve population sleep health. The purpose of this study was to characterize sleep disorder practice patterns within a statewide health system.

Methods

The University of Maryland Medical System (UMMS) is comprised of 13 largely autonomous hospitals that function in a decentralized manner. Four UMMS hospitals feature sleep centers. In this study, patients diagnosed with sleep disorders (January 1 – December 5, 2023) were identified using the UMMS electronic health record (EPIC). Demographic information was obtained from the EHR. Sleep disorders were identified based on ICD-10 diagnostic codes assigned within the EHR. Descriptive statistics were used to characterize sleep disorder diagnoses at the system and hospital levels.

Results

Of UMMS patients seen during the study period, 5.8% of individuals (up to N=65,095) were diagnosed with sleep disorders. The two most common sleep disorders were obstructive sleep apnea (OSA; n=36,130 patients) and insomnia (n=20,280). Others included restless legs syndrome (n=3744), hypersomnia (n=1959), parasomnias (n=816), circadian rhythm disorders (n=532), narcolepsy (n=397), central sleep apnea (n=342), and periodic limb movement disorder (n=175). The highest numbers of sleep disorder diagnoses occurred at hospitals with sleep centers. Multiple points of care had zero diagnoses for common sleep disorders. In terms of sociodemographic characteristics, the ratio of OSA vs insomnia differed between Black and White adults.

Conclusion

Sleep disorders are commonly diagnosed in a statewide health system, with substantial variation between hospitals and points of care. Potential disparities were observed in the ratio of OSA vs insomnia diagnoses between Black vs White adults, suggesting possible underdiagnosis of insomnia among Black adults and an important topic for future research. Health systems leaders should consider system and hospital-level approaches including increasing provider awareness and leveraging telehealth and technology to improve outcomes for patients, providers, and health systems.

Support (if any)

University of Maryland, Baltimore
0507 A Novel Machine Learning Model to Predict Obstructive Sleep Apnea Using Craniofacial Photography with Questionnaire
Authors: Kim Yunsoo , Park June-Young , Jang Yeji , Kim Min Hye , Ryu Wi-Sun , Kim Jee Hyun , Shin Hye-Rim , Kim Tae-Joon
Published: April 2024

Introduction

Obstructive sleep apnea (OSA) is a complex and heterogeneous sleep-related breathing disorder, associated with systemic consequences such as hypertension, stroke, and cardiovascular diseases. Although initial screening tools such as STOP-BANG or the Berlin questionnaires have been developed, it is still challenging to capture the wide spectrum of the condition alone. Besides the questionnaires including clinical presentations, craniofacial abnormalities are also recognized as an important risk factor for detecting OSA. Herein we aim to develop an efficient approach to predict the risk of having moderate to severe OSA using machine learning techniques by incorporating anatomical information from 2D photographs.

Methods

This retrospective analysis included 348 patients, who completed answering the STOP-BANG questionnaires and took facial images at Dankook University Medical Center in South Korea between 2012 and 2022. A 1:1 Random under-sampling (RUS) method was applied to solve the imbalance problem between moderate to severe OSA cases (Apnea-Hypopnea Index ≥15 events/hour) and control. Balanced data were randomly divided into two data sets (training and validation: 80%; testing: 20%). We performed leave-one-out cross-validation (LOOCV) to seek the optimal parameters to improve the model performance and generalization using four machine learning models (logistic regression, random forest, support vector machine, XGBoost) in the training set. Thereafter, we adopted the model with the highest area under the receiver operating characteristic (AUROC) in the testing set. Finally, we evaluated the importance of each input feature in assessing OSA risk by calculating the Shapley additive explanations (SHAP) values.

Results

The logistic regression model achieved the best AUROC of 94.1% for predicting moderate to severe risk OSA. The value of craniofacial images built from deep learning algorithms was found to be a predominant contribution in the risk screening models of OSA with the aforementioned levels of severity.

Conclusion

The results of the study suggest that the proposed model improved the diagnosis of patients with moderate to severe OSA based on the combination of sleep-related questionnaires and 2D facial images by identifying the risk factors more efficiently.

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0509 Association Between Access to Sleep Care and Acute Care Utilization: A Preliminary Analysis
Authors: Deshpande Pushkar , Vyata Vishruth , Glick Danielle , Verceles Avelino , Fang Beverly , Rolla Krishna Jayanth , Johnston Christine , Diaz-Abad Montserrat , Wickwire Emerson , So Jennifer
Published: April 2024

Introduction

Obstructive Sleep Apnea (OSA) has been linked to increased cardiopulmonary morbidity and mortality, as well as acute care healthcare resource utilization (HCRU; emergency department [ED] visits and hospitalizations). The purpose of this study was to test the hypothesis that access to OSA care was associated with decreased acute care HCRU in the state of Maryland.

Methods

The University of Maryland Medical System (UMMS) is comprised of 12 autonomous hospitals that function in a decentralized manner. Patients (N=31,477) were identified within the UMMS electronic health record system based on ICD-10 code for OSA (G47.33). Access to care was defined based on the number of AASM-accredited sleep centers in the five main regions of Maryland. Acute care HCRU was defined based on rates of ED admissions and inpatient hospitalizations from January 1st, 2023 to December 5th 2023 in each of the 12 hospitals and main regions of Maryland.

Results

The final sample included 31,447 patients diagnosed with OSA. At the individual hospital level, the highest rate of ED admissions was observed within a suburban hospital (7.79%). The highest rate for inpatient hospitalizations was observed within an urban hospital (6.47%). At a regional level, the Central Maryland region (seven hospital sites) showed the highest average rate of ED admission (3.78%) and inpatient hospitalizations (2.98%). Notably the Central Maryland region also had the largest number of AASM-accredited sleep centers (n=14) compared to other Maryland regions, suggesting a positive association between access to OSA care and acute care HCRU. From a socioeconomic stand point, Central Maryland average median household income was only the third highest ($96,128).

Conclusion

Contrary to our hypothesis, access to OSA care was associated with increased acute care HCRU among individuals with OSA in a statewide health system. The most likely explanation for this finding is a higher number of OSA diagnoses around sleep centers which are regionally clustered and possible regional differences in patient population. Future research should seek to improve OSA management and reduce acute care HCRU.

Support (if any)

None
0510 ETCO2 Screening for Obesity Hypoventilation Syndrome: A Patient Safety and Quality Improvement Study
Authors: Cocozza Victoria , Clay Collin , Scheuller H Samuel , Brock Matthew , Tchopev Zahari
Published: April 2024

Introduction

Obesity hypoventilation syndrome (OHS) is a disorder of daytime hypercapnia which worsens during sleep. OHS may be underdiagnosed yet has an insidious progression to significant cardiopulmonary and neurologic morbidities and increased mortality. End-tidal carbon dioxide (ETCO2) monitoring is not routinely obtained in adult patients undergoing in-lab diagnostic polysomnography (PSG), possibly contributing to underdiagnosis. Patients with OHS may be missed without initial screening and consideration for this diagnosis. This quality improvement project sought to estimate the incidental rates of OHS in obese patients and describe their clinical characteristics.

Methods

Patients presented to our academic sleep center from September – November 2023 for clinically indicated diagnostic PSG. Those with a body-mass index (BMI) greater than 30 kg/m2 underwent ETCO2 monitoring during their PSG. Those with a documented history of opioid use, primary pulmonary or neurologic disease were excluded. Basic demographics and sleep screening questionnaires were obtained.

Results

Initial data revealed 43 eligible patients. 21% (n=9) of patients met the International Classification of Sleep Disorders (ICSD) Third Edition Text Revision diagnostic criteria for OHS. There was no statistical difference between the OHS group and the non-OHS group in the following categories: Male (77.8% v 76.5%), Age (33.8 v. 33.6), BMI (33.2 v. 33.8), AHI (23.6 v. 22.9), and ESS (12.8 v. 12.3). Of note, ISI scores were significantly different (15.9 v. 16.9, p=0.04).

Conclusion

Implementing an OHS screening protocol is a quick, feasible way to improve diagnosis of OHS. The incidental discovery of OHS was found to be clinically meaningful. Given the relatively low BMI, this may be an underestimation of OHS prevalence in obese patients. To the authors knowledge, no published improvement projects exist for improving diagnosis of OHS with routine PSG in an outpatient setting.

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0511 High Frequency Radar for Non-Contact Detection of Respiratory Signals During Sleep
Authors: Goswami Umesh , Kablan John , Condon John
Published: April 2024

Introduction

Polysomnography (PSG) requires on-body sensors for respiratory effort measurement, that can adversely affect sleep quality and accuracy of detection of sleep related breathing disorders (SRBD). We developed a non-contact 60 Hz pulsed wave radar system with integrated advanced signal processing to collect respiratory effort, heart rate and gross body movements data using phase differencing to a sub-mm resolution. In this study we tested the performance of this system in detecting SRBD events against standard PSG in human participants.

Methods

During participants’ clinically indicated PSG, we collected concurrent data from the radar system placed under the bed on a tripod mount. Algorithms were developed to quantify breath amplitude reductions collected by radar and compared to the time-synced signals derived from respiratory impedance plethysmography belts. A Convolutional Neural Network (CNN) was trained on radar in-phase/quadrature (I/Q) data for each 30 second epoch to output probability of apnea and hypopnea events using standard event definitions. Receiver Operating Characteristics (ROC) plot and confusion matrix for the trained network were generated to provide accuracy of event predictions.

Results

A total of 60 participants completed the study. The participants had a mean age of 65 years (Inter Quartile Range 13.5 years), and 56% subjects were male. The mean AHI for the entire cohort was 12.9 (IQR 14.75) events/hour. ROC plots showed an Area Under the Curve (AUC) of 0.98 for events detection by radar compared to human scored PSGs. The confusion matrix was generated at an operating point of 95% sensitivity and 80% specificity.

Conclusion

The non-contact high frequency radar system showed excellent performance in respiratory event detection in patients with sleep-related breathing disorders in comparison to the gold standard PSG system. Use of this technology is expected to reduce the number of body contact sensors used during conventional sleep studies and thus improve patient comfort and accuracy of results.

Support (if any)

Research reported in this abstract was supported by National Institute of Aging of the NIH under award number: R44AG060779
0513 Assessment of Sleep Fragmentation in a Large US Sample by Home Under-Mattress Devices
Authors: Kushida Clete , Cotton-Clay Andrew , Baron Susan , Fava Laura , Wray Andrew , Easwar Venkat , Kinsolving Arthur , Leng Yue , Zitser Jennifer , Kahn Philippe
Published: April 2024

Introduction

Sleep fragmentation by arousals and sleep-disordered breathing (SDB) is known to increase with age and differ by gender, although the magnitude of these effects is largely unknown. Population studies allow large collection of continuous nightly data to explore these effects.

Methods

Sleep fragmentation index (SFI, arousals/hour) and SDB were analyzed by a commercially available home monitoring device (Sleeptracker-AI Monitor, Fullpower Technologies Inc., California, USA) that passively monitors sleep using piezo-electric sensors. Validated sleep/respiratory parameters were derived from device data. De-identified data were analyzed, following review and exemption of the study (#57681) from Stanford University IRB. SFI was assessed using an estimated arousal index, estimating individual arousal events from passive sensor data, with validation against PSG. Data 8/16/2023-12/15/2023 were reviewed. Individuals with birth year and gender provided and age 20-79 years in 2023 were included in the analytic dataset.

Results

117,846 individuals (64,049 men, 48.2±12.6 years; 53,797 women, 48.3±12.6 years) with 10,090,778 recorded nights met the inclusion criteria. For AHI< 5, the SFI mean±SE for women and men were 13.6±0.16 and 12.6±0.13 at 20-24 years, respectively, and increased with age to 15.0±0.26 and 16.0±0.24 for 75-79 years. For AHI< 5, men showed a significantly greater SFI during REM sleep than women across all ages above 24 years, but not during NREM sleep. For AHI< 5, SFI significantly increased with age during NREM sleep (women 11.9±0.15 to 14.6±0.17, men 10.8±0.12 to 15.1±0.26), but for REM sleep increased up to 25-34 years (women 18.8±0.12, men 19.6±0.09), but then steadily decreased with age to 75-79 years (women 16.1±0.27, men 18.1±0.24), and a similar trend was found for AHI≥5. For AHI≥5 compared to AHI< 5, SFI was significantly higher by 6-13 arousals/hour across all ages.

Conclusion

Use of a noninvasive in-home monitoring device enabling continuous nightly collection/analysis of a large sample of sleep/respiratory data revealed differences in sleep fragmentation across age and by gender. Greater sleep fragmentation was observed for men across age during REM sleep, and fragmentation during REM sleep decreased with age after 34 years, which is unexpected and warrants further study given that overall quantities of NREM and REM sleep decrease with age.

Support (if any)

Fullpower
0514 Can the PPG Cardiac Component Envelope Reflect the Respiratory Drive?
Authors: Hsu Chih-Wei , Wu Hau Tieng
Published: April 2024

Introduction

Using a home-based fingertip monitor can decrease the time and cost of screening obstructive sleep apnea (OSA). During OSA events, respiration is partially or totally blocked, which stimulates the compensatory respiratory drive. We discovered a significant cardiac component envelope dynamics induced by the apnea events, particularly a strong surge following most apnea events. We conjecture that it reflects the respiratory drive.

Methods

We recruited 76 participants with suspected OSA. Each participant attends overnight PSG analysis with the TipTraQ device, which is installed in the fingertip with red, infrared and green photoplethysmography (PPG). Sleep apnea events were labeled by the same sleep experts. The envelope of the cardiac component (EC) and the instantaneous heart rate (IHR) were extracted from each PPG channel. The surge in the EC and IHR near the end of each OSA event was extracted by the spectrum analysis. The lag seconds of the surge after each event, latency between the EC surge and the IHR surge, and maximal shifted correlation coefficient were evaluated.

Results

During periods of frequent OSA, the EC surge and the IHR surge are well-aligned. The correlation coefficient is over 0.8 with the EC surge shift of about 4 seconds forward. The lag seconds are distributed from 10 to 40 for both channels’ surges, central at 25. The age does not affect distributions.

Conclusion

The study results show that after OSA events, the EC surges often happen after IHR surges. This suggests that the sympathetic stimulation induced by OSA happens earlier than hemodynamic changes reflecting respiratory efforts, especially when participants enter the frequent OSA periods state, which further suggests that EC encodes respiratory drive. This observation could help better determine whether a patient is suffering a severe apnea from the mobile fingertip devices like TipTraQ.

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0517 Integrating Body Sensor into a Wearable Platform to Enhance the Identification of Central and Mixed Apneas
Authors: Gu Wenbo , Wu Peter , Liu Arthur , Liu Wen-Te , Kuan Yi-Chun , Lee Hsin-Chien , Leung Lydia , Wu I-Chen , Chiang Ambrose
Published: April 2024

Introduction

Accurate identification of apnea types is crucial for effective diagnosis and management of sleep-disordered breathing. The Belun Sleep System (BLS-100, a.k.a., Belun Ring) is an FDA-cleared home sleep apnea testing system (K222579) comprising an adjustable ring-shaped wearable, a cradle, and two deep learning-powered algorithms. The Belun Cor, a novel subxiphoid sensor equipped with accelerometry, can detect respiratory effort, respiratory rate, body position, and facilitates the detection of central events. This preliminary analysis aims to assess the performance of the integrated BLS-100 in detecting apnea events containing central components.

Methods

This interim analysis evaluated the performance of BLS-100 in a clinical cohort of hospitalized patients admitted for acute ischemic stroke. Eligible patients underwent in-lab polysomnography (PSG) alongside concurrent BLS-100 testing. PSG scoring adhered to the latest AASM scoring manual, with scoring technicians blinded to the BLS-100 results. The BLS-100 derived total sleep time (bTST), sleep stages (bSTAGE), apnea-hypopnea index (bAHI), and combined central and mixed apnea index (bCMAI).

Results

As of 12/17/2023, 25 consecutive Taiwanese patients were enrolled. Four patients were excluded due to short bTST< 120 mins. The analysis was conducted on 21 patients. M:F 19:2; age 59.7; PSG TST 270 ± 61.9 mins; PSG AHI 27.0 (1.4-81.9) with 3 normal, 3 mild, 7 moderate, and 8 severe OSA cases. The mean PSG central apnea index (PSG-CAI) was 4.8 (0.0-34.0), with 5 patients having PSG-CAI≥5. The mean PSG central and mixed apnea index (PSG-CMAI) was 8.4 (0.0-47.3). Pearson correlation coefficients between PSG-CAI and bCMAI, as well as PSG-CMAI and bCMAI, were 0.939 (P< 0.001) and 0.982 (P< 0.001), respectively. Using bCMAI≥5 to predict PSG-CAI≥5, the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Cohen’s Kappa were 0.81, 1.00, 0.75, 0.56, 1.00, and 0.59, respectively. Similarly, using bCMAI≥5 to predict PSG-CMAI≥5, the corresponding values were 0.86, 0.88, 0.85, 0.78, 0.92, and 0.70, respectively.

Conclusion

Early findings indicate that the BLS-100 with Belun Cor shows promising performance in identifying apnea events that include central components. An elevated bCMAI serves as a valuable indicator for clinicians, signaling the presence of central or mixed apneas.

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Case Western Reserve University-Taipei Medical University Pilot Award
0518 Longitudinal Testing with a Home Sleep Apnea Test Shows Large Variability in Nightly Sleep Apnea Severity
Authors: Rohrscheib Chelsie
Published: April 2024

Introduction

Sleep apnea (SA) is traditionally diagnosed by a single-night sleep study, however research suggest that nightly variability in SA severity is common. The purpose of this study was to utilize a novel longitudinal home sleep apnea test (HSAT) to evaluate the extent of variability and identify the ideal number of tests for diagnostic accuracy.

Methods

This study retrospectively evaluated 149 adults across the U.S. that used a novel type-3 HSAT, capable of unlimited longitudinal testing. 1,184 tests were evaluated (Mean 7.9 tests/subject; SD 6.9). Subjects who reported using SA treatments were removed from analysis. Apnea/Hypopnea Index (AHI) was assessed to determine the presence and severity of SA. A subgroup of previously diagnosed individuals were assessed for accuracy of their original sleep study.

Results

On repeat testing, 69.7% of subjects had AHI scores that varied in severity. 51% had AHIs within the normal (< 5) and abnormal range (>5). The average AHI point difference between a subject's best and worst test was 12.4 (SD 12.1). Of the subjects that were positive for SA, 52% had false negatives on their first test. An average of 3.2 (SD 1.6) test were completed before obtaining a positive AHI, and 3.6 (SD 2.2) tests before arriving at the highest recorded severity. 26.1% of previously diagnosed subjects had HSAT-results that were more severe than their original diagnosis. Of the subjects that reported having mild SA, 11% had HSAT scores in the severe range. Of the subjects that reported having moderate SA, 38% HSAT scores in the severe range. Factors that were found to correlate with AHI variation were variation in time spent in supine position and time spent in REM.

Conclusion

Longitudinal testing with an HSAT confirmed high night-to-night variability of AHI in the majority of subjects. Subjects with mild to moderate SA were at high risk for misdiagnosis or misclassification if diagnosis is based on single or double night sleep studies. This study emphasized the need to standardize longitudinal testing and suggests that >3 tests is ideal for diagnostic accuracy.

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0519 Predictors of False Negative HSATs in a Veteran Population
Authors: Hussain Shahnoor , Long Ariel , Mattison Carol , Stafford Erick , Ali Ibrahim Sultan
Published: April 2024

Introduction

Obstructive sleep apnea (OSA) is a common chronic condition associated with significant morbidity and mortality. Polysomnography (PSG) is the gold standard for diagnosis but is not always readily available. American Academy of Sleep Medicine (AASM) 2017 guidelines recommend in-lab PSG if a patient with high pretest probability of OSA has a negative home sleep apnea test (HSAT) defined as AHI < 5 events/hour. At our institution, the threshold for obtaining an in-lab PSG has been broadened to include pAHI < 10 events/hour. We investigated the validity of this threshold and analyzed co-morbidities and other predictors of false negative HSATs.

Methods

We reviewed 306 consecutive WatchPAT®️ (Itmar Medical, Ltd.) HSATs between January 2021 and December 2022 with a peripheral arterial tonometry–derived apnea-hypopnea index (pAHI) < 10. False negative HSAT was defined as pAHI < 10 with in-lab PSG AHI > 5 and true negative HSAT was defined as pAHI < 10 with in-lab PSG AHI < 5. The in-lab PSG results were categorized as mild, moderate, or severe OSA. Demographic data and co-morbidity information were gathered for these patients. Chi-squared test was used for statistical analysis.

Results

Demographic data were well balanced between the two groups including male gender, age, and BMI. 131 patients met criteria for in-lab PSG. Of these, 64 (49%) were false negative and 67 (51%) were true negative. Out of the false negative HSATs, 65% were mild whereas 35% were moderate or severe OSA. These false negative HSATs were significantly associated with diabetes (p=0.01), hypertension (p=0.001), and patients with hypertension taking two or more anti-hypertensive medications (p=0.001).

Conclusion

False negative HSATs were common in the evaluation of OSA in our veteran population with a notable percentage of moderate to severe OSA on in-lab PSG even when the threshold was raised to pAHI < 10. False negative HSATs were significantly associated with diabetes, hypertension, and patients on two or more anti-hypertensive medications. AASM guidelines recommend in-lab PSG for negative HSATs. Our study proposes that we should consider increasing our threshold to repeat a gold-standard in-lab PSG in patients with a pAHI < 10, especially those with hypertension or diabetes.

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0520 Relationship Between ESS and AHI in PSG vs. HSAT: Possible Modulation by Sex
Authors: Lynch Mc Lyne , Dubrovsky Boris , Weingarten Jeremy , Cunningham John
Published: April 2024

Introduction

Excessive daytime sleepiness is a major symptom of OSA. However, the relationship between Epworth Sleepiness Scale (ESS) and apnea-hypopnea index (AHI) is tenuous and may depend on whether the AHI is derived from an in-lab or at-home study. We analyzed ESS as a function of demographic variables and AHI in a group of patients evaluated with type 3 home sleep apnea test (HSAT) or in-the lab PSG.

Methods

A group of 83 HSAT patients (40 women) were matched with 83 PSG patients by race and sex. Additionally, age was matched within a decade (20-29, 30-39, etc.), and BMI was matched within ±2.5 units. ESS was regressed onto age, BMI, sex, AHI, and AHI-by-sex, AHI-by-HSAT/PSG and AHI-by-sex-by-HSAT/PSG interactions in a backward regression model.

Results

The mean AHI=18.0±20.0, 119 patients had AHI≥5.0, OSA severity was equally represented on HSAT and PSG studies (chi-square=0.51, p=0.92). ESS was negatively related to AHI (B=-0.58, SE=0.25, p=0.022); however, all three interaction terms were significant: AHI-by-sex (B=0.30, SE=0.14, p=0.030), AHI-by-HSAT/PSG (B=0.37, SE=0.14, p=0.009) and AHI-by-sex-by-HSAT/PSG (B=0.20, SE=0.08, p=0.013). Analyzing men and women separately, women evidenced a negative relationship between ESS and AHI (B=-0.29, SE=0.13, p=0.023) and a significant AHI-by-HSAT/PSG interaction (B=0.18, SE=0.07, p=0.009). Among men, no explanatory variables or interaction terms were significantly related to ESS.

Conclusion

As suggested by this limited sample, the relationship between AHI and self-reported daytime sleepiness may depend on the type of test used to evaluate the respiratory status and may also be modulated by the patient’s sex. Future studies are required to elucidate the interaction between sex, AHI and the type of apnea test in the effects of these variables on subjective daytime sleepiness.

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none
0522 Screening and Diagnosis of Obstructive Sleep Apnea in Middle Eastern/North African (MENA) Women: A Pilot Study
Authors: Khalil Zeina Al , Attarian Hrayr , Sankari Abdulghani , Herreshoff Emily , Diemer Matthew , Duval Kim , Ismail Hadi , Sankari Safa , Sukhon Ahmad , Chervin Ron , Dunietz Galit Levi
Published: April 2024

Introduction

The risk of obstructive sleep apnea (OSA) increases with age for both sexes. Beyond chronological age, reproductive aging in women between ages of 40-60 may also raise OSA risk. Associations between reproductive aging and OSA have been infrequently explored among women from underrepresented ethnic groups. Therefore, we initiated a pilot cohort study in women from Middle Eastern/North African (MENA) backgrounds (n=77) from Southeast Michigan. To examine the frequency of OSA, we screened women with the STOP-BMI scoring scheme and then compared their risk profiles against Home Sleep Apnea Test (HSATs).

Methods

This pilot study included 77 MENA women from Michigan between the ages 40-60y. In addition to demographic, lifestyle and anthropometric data, women were also screened for OSA with the STOP-BMI questionnaire (Score >3 indicating high risk and < 3 indicating low risk). A subset of these women completed HSATs with WatchPAT®️ devices (pAHI 3% >5/hour defined OSA).

Results

In this sample, the mean age was 48y and a third of women were postmenopausal. Mean BMI was 27.6 and 25% of the women had a BMI>30. Twenty women underwent HSATs, and 15 (75%) showed OSA. The mean BMI of women with and without OSA was 30.6 and 26.8 kg/m2, respectively. Of the 15 women with OSA, eight scored low-risk on the STOP-BMI. Moreover, among women without OSA, 40% had a high-risk score on the STOP-BMI. Data suggested that the STOP-BMI has moderate sensitivity (47%) and specificity (60%).

Conclusion

We found a high frequency of OSA among midlife women from MENA ancestry and despite a low frequency of obesity. Moreover, the STOP-BMI screening tool for OSA was neither sensitive nor specific among these women. Our data suggest that OSA risk may be independent of BMI and that the current screening tool may not capture the symptoms and risk factors for OSA among midlife women.

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0526 Deep Convolutional Neural Network for Groaning and Snoring Sounds Classification
Authors: Yu Min , Gao Xuemei
Published: April 2024

Introduction

Catathrenia is a rare sleep-related breathing disorder characterized by recurrent monotonous groaning during sleep. The acoustic characteristics of groaning and snoring sounds have been investigated. This study aims to propose a deep convolutional neural network (CNN) for automatic binary classification.

Methods

This study consisted of 3728 episodes of groaning sounds and 4577 episodes of snoring sounds obtained from synchronized audio of full-night polysomnography. Four features extracted from log-scaled mel-spectrograms were used as input. The background gaussian noise and the time-shifting were used to augment the dataset. The dataset was randomly split into training (70%), validation (15%), and testing datasets (15%). A deep learning convolutional neural network architecture was trained and evaluated.

Results

The proposed CNN model achieves an accuracy of 95.0% on the binary classification of groaning and snoring sounds. The model attains a sensitivity/recall of 96.4%, a specificity of 93.4%, and an F1 score of 94.84%.

Conclusion

The proposed CNN architecture has performed well in the automatic binary classification of groaning and snoring sounds, which could reduce difficulties in acoustic analyses of groaning episode detection. The model needs to be verified with more audio data before it can be put into clinical use better.

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