bluuebunny/crossref_metadata_2025_split huggingface dataset:
0521 Validation of Deep Learning Algorithm Model in Automatic Scoring of the Respiratory Events in Adult Polysomnography
Authors: Park Marn Joon , Choi Ji Ho , Ha Tae Kyoung , Lee Ho Dong , Lee Young Jun 
Published: April 2024

Abstract

Introduction
Background: Polysomnography (PSG) has several benefits as a conventional diagnostic tool for identifying and assessing a range of sleep disorders, with particular emphasis on obstructive sleep apnea (OSA). Nevertheless, the process of scoring raw data requires a significant amount of effort and time. Hence, the objective of this research was to assess the precision of a recently devised automated scoring system for respiratory events, using a deep learning algorithm model, in the context of people afflicted with sleep-disordered breathing (SDB).

Methods
A total of 1,000 case PSG data were enrolled to develop a deep learning algorithm. Of the 1,000 data, 700 were distributed for training, 200 for validation, and 100 (simple snoring, mild, moderate, and severe OSA; n = 25 in each group) for testing. The respiratory events scoring deep learning model is composed of five sequential layers: an initial layer of perceptrons, followed by three consecutive layers of long short-term memory cells, and ultimately, an additional two layers of perceptrons.

Results
The PSG data of 100 patients (mean age 50.59 ± 14.01 years; 55 men and 45 women; body mass index 26.30 ± 3.98) were selected for validation and testing of the deep learning model, which included simple snoring, mild, moderate, and severe OSA groups (n = 25 each). Our deep learning algorithm had a sensitivity (95% confidence interval [CI]) of 98.06 (96.64-99.53), 98.12 (96.68-99.87), 98.23 (96.75-99.72), and 98.51 (96.2-95.91) for identifying apnea or hypopnea events. The specificity (95% CI) of simple snoring, mild OSA, moderate OSA, and severe OSA was 97.79 (92.84-99.95), 96.84 (93.6-99.95), 96.7 (93.32-99.59), and 95.46 (93.71-99.08) compared to a manually graded PSG report. The deep learning model's area under the curve values for predicting OSA in apnea-hypopnea index ≥ 5, 15, and 30 groups were 0.9402, 0.9388, and 0.9442, respectively, showing no significant differences between each group.

Conclusion
The deep learning algorithm used in our work demonstrated a notable accuracy in recognizing apnea/hypopnea episodes and subsequently categorizing the severity of OSA.

Support (if any)
This work was supported by the Technology Development Program (S2718665), which is funded by the Ministry of SMEs and Startups of Korea and Soonchunhyang University Research Fund.
0523 Screening for Prevalence and Pattern of Sleepiness and Obstructive Sleep Apnea Among Nigerian Population
Authors: Olaoye Olatunji , Bolarinwa Oladimeji , Alatishe-Muhammad Bilqis , Fasiku Mojirola , Aibinuomo Oluwatoyosi , Adejoro Ige , John Godwin , Kayode Gladys , Adeyemi Motunrayo , Omelu Louritta , Omokanye Habeeb , Olaoye Olanrewaju 
Published: April 2024

Abstract

Introduction
Significant sleepiness and Obstructive Sleep Apnea (OSA) burden can be effectively managed in Sub-Saharan Africa (SSA) if population-based data are available. To fill this study gaps, this study piloted a screening survey in an African country using the two popular tools (Epworth and Stop Bang), translated and validated among a Yoruba-dominated diverse population of the North-Central region of Nigeria

Methods
This was a household survey among rural, urban and semi-urban populations in North-Central Nigeria. A total of 2388 eligible permanent adult residents were selected through a 4-level multistage sampling process. Epworth Sleepiness Scale (ESS) and Stop Bang Questionnaire (SBQ) were adapted, translated into the Yoruba language, and validated (face, content and construct validity). Both Descriptive and inferential analyses were presented to report the prevalence, pattern and factors associated with sleepiness and OSA. The level of significance was set at a p-value of &lt; 0.05 and a 95% confidence interval. Ethical approval was obtained from the Kwara State Ministry of Health

Results
More than a quarter of the respondents were 50 and older, with almost equal gender representation and Yoruba tribe preponderance (87.5%). The urban (34.4%), semi-urban (33.5%) and rural (32.1%) locations were equally distributed. Using the ESS, 15.7% had average sleepiness while 29.4% reported abnormal sleepiness. With the SBQ, 24.2% had a moderate risk of OSA while 5.6% had a high risk of OSA. The geographical pattern of OSA showed no significant distribution with ESS. Still, SBQ shows a significant (p&lt; 0.0001) moderate (38.8%) and high risk (3.9%) of OSA among urban population than semi-urban (moderate: 25.8% vs high: 0.9%) and rural population (moderate:18.9% vs high: 0.3%). Female gender, increasing age, occupation type, low-income earning and obesity were significantly associated with average and abnormal sleepiness using ESS. While being a widow, a large household and obesity were significantly associated with OSA using SBQ.

Conclusion
Both the ESS and SBQ tools revealed a moderately high prevalence of sleepiness and OSA, with the urban Nigerian population reporting a higher prevalence. However, the two tools reported slightly varied factors associated with sleepiness and OSA

Support (if any)
0524 BASET Scoring: A Novel Simple Biometric Score for Screening and Grading Obstructive Sleep Apnea
Authors: saleh Abdelbaset , Ahmed Magda , Said Eman El , Awadalla Nabil , Attia Amira 
Published: April 2024

Abstract

Introduction
Polysomnography (PSG) is the gold-standard diagnostic tool for Obstructive Sleep Apnea (OSA). There is no diagnostic validated simple tool with clear cutoff point for predicting and roll out patient with OSA in primary care clinics significantly alters clinical outcomes. Our study aimed to assess the validity of BASET scoring as a new potential tool for screening and grading the severity of OSA patients.

Methods
After institution review board approval and formal patient consent, 144 subjects for suspected OSA and their relatives were enrolled. All subjects were subjected to a full night PSG study after history taking, sleep questionnaires, and physical examination, including BASET score components: B= Body Mass Index (BMI), A= Abdominal circumference (AC), S = Snoring, E= Epworth Sleepiness Scale, and T= Tongue teeth imprint. ROC analysis that used to assess the optimal cutoff point of the BASET score and to compare its accuracy for predicting OSA with Berlin and STOP-Bang scores.

Results
This study included 63 OSAS patients, 33 (52.38%) males and 30 (47.62%) females, and 81 controls; 22 (27.16%) males and 50 (72.84%) females. The Cronbach's alpha for the 5 BASET score components was 0.846, indicating the internal consistency reliability of the scale. Moreover, BASET score has a moderately strong positive significant correlation (r = 0.778, p&lt; 0.001) with AHI. By ROC analysis, the accuracy of the three measures was generally high, with BASET score predicting OSA most accurately (AUC=0.984, 95%CI: 0.956–0.999), followed by STOP-Bang (AUC=0.939, 95%CI: (0.887–0.972) and Berlin (AUC=0.901, 95%CI: 0.841–0.945). The AUC of BASET score was significantly higher compared to the Berlin score (difference= 0.0825, 95%CI: 0.039–0.125) and STOP-Bang score (difference= 0.0447, 95%CI: 0.011–0.078). On the other hand, there was no difference between the AUC of Berlin and STOP-Bang scores (difference=0.0378, 95%CI: 0.006 - 0.081 4). BASET score was significantly (p&lt; 0.001) associated with OSA grades,

Conclusion
BASET score is a convenient, reliable, and valid tool for diagnosing OSA. BASET score is more accurate for predicting OSA than Berlin and STOP-Bang scores, while there is no difference between Berlin and STOP-Bang scores. BASET score indicates OSA grades.

Support (if any)
No financial support
0525 Can Intra Oral and Facial Photos Predict Obstructive Sleep Apnea in the General and Clinical Population ?
Authors: Vidigal Tatiana , Haddad Fernanda , Guimarães Thaís , Silva Luciana , Andersen Monica , Schwab Richard , Cistulli Peter , Pack Alan , Tufik Sergio , Bittencourt Lia 
Published: April 2024

Abstract

Introduction
Obstructive sleep apnea (OSA) is associated with upper airway soft tissue and craniofacial skeletal alterations, but if these abnormalities, using quantitative facial and intraoral photographic analysis, differ between in the general population and clinical samples were not tested yet. This study aimed to evaluate and compare measurements of standardized craniofacial and intraoral photographs between clinical and general population samples, between groups of individuals with an apnea-hypopnea index (AHI) ≥15 and AHI&lt; 15, and the interaction between them, as well as the relationship with the presence and severity of OSA.

Methods
We used data from 929 participants from Sleep Apnea Global Interdisciplinary Consortium (SAGIC): 309 patients from a clinical setting and 620 volunteers from a general population sample.

Results
Moderate and severe OSA (AHI≥15) were observed in 30.3% of the total sample and there were some interactions between facial/intraoral measures with OSA and both samples. Mandibular volume (p&lt; 0.01), sternomental distance (p=0.04), and lateral face height (p=0.04) were higher in the AHI≥15 group in the clinical sample compared to the AHI≥15 group in the general population and AHI&lt; 15 group in the clinical sample. Multivariate regression suggests photography-based variables capture independent associations with OSA in both samples. When adjusted for sex and age, greater mandible width (p&lt; 0.01) differed both in the clinical and in the general population samples, reflecting AHI severity and the likelihood of OSA; the measure of smaller tongue curvature (p&lt; 0.01) reflected the severity and probability of OSA in the clinical sample; and the higher posterior mandibular height (p=0.04) showed a relationship with higher AHI and higher risk of moderate to severe OSA in the general population sample. However, when adjusted for sex, age, and BMI, only smaller tongue curvature (p&lt; 0.01) was associated with moderate/severe OSA.

Conclusion
Quantitative standardized facial photographs indicated that the measures of greater tongue and mandible were associated with increased OSA risk in the clinical sample and higher facial height measurement was associated in the general population sample.

Support (if any)
Acknowledgments: AFIP, CNPq, CAPES, and SAGIC group.
0529 Feasibility of the REST Tracker Platform to Monitor Obese Pregnant Woman for the Onset of OSA Throughout Pregnancy
Authors: Simmons Jerald , Thomas Robert , Ivins Renee , Kirshon Brian , Adam Karolina , Saade George , Sadeghian Sam , Paige Ashley , Mann Chris 
Published: April 2024

Abstract

Introduction
OSA during pregnancy is associated with increased risk for gestational-diabetes, hypertension, preeclampsia, and eclampsia, contributing to maternal and neonatal morbidity and mortality. Furthermore, OSA increases risk of chronic cardiovascular disease, well beyond pregnancy. Studies suggest the prevalence of OSA increases during pregnancy, as maternal BMI rises. Continuous evaluation is necessary as the risk increases throughout the course of the pregnancy, however this is not currently a standard practice. Sleep specialists have not provided a specific approach to assist obstetricians addressing the OSA concern, leaving a gap in our maternal health management. REST Technologies has developed the REST-Tracker, a Remote Patient Monitoring platform for the management of OSA patients and has been funded by the NIH to assess the feasibility of using this platform in the obese pregnant population at risk of developing OSA. Here, we present preliminary data using the REST-Tracker to assess for OSA throughout pregnancy in an at-risk population.

Methods
The REST-Tracker system manages data obtained from a Ring-Oximeter worn nightly by subjects whos data is processed through the FDA cleared SleepImage ™ system, assessing for OSA, providing sAHI3%/sAHI4% metrics. Subjects recruited from a high-risk obstetrics clinic. Inclusion-criteria: BMI &gt;27, entry &lt; 16 weeks gestational-age, Target=30 subjects monitored nightly into and through the third trimester.

Results
To date, 23 reliable subjects, two drop-outs from miscarriages, 21 currently providing data, 9 left to recruit. Initial-BMI Ave=38 (+/-6.6), Ave Initial-GA=8.9wks(+/-2.4), Initial sAHI3%/4%= 5.3 (+/-3.1) sAHI4%=2.3(+/-2.0) average monitoring to-date=12.4(+/-5) with 0 subjects demonstrating sustained moderate (sAHI4% &gt; 15) or severe (sAHI4% &gt;30) OSA. None of the subjects to-date demonstrate SaO2 &lt; 90% to any significant degree, 3 subjects had the SaO2 drop &lt; 90% &gt;5 min on 3 nights, only 1 had 1 night with SaO2 &lt; 88% &gt;5 minutes (19 minutes).

Conclusion
If successful the REST-Tracker may provide new approach, improving maternal care for this at-risk population. Additionally, it allows expedited OSA management with longitudinal guidance to assess treatment modifications, necessary in a evolving medical scenario such as the third-trimester of pregnancy. Furthermore, this approach will allow insight into the pathophysiologic relationships between OSA and conditions such as gestational diabetes and hypertension.

Support (if any)
Research-supported-NIH#R43HL166038
0532 An Exploration of Targeted Metabolites and Their Associations with Sleep Latency in Persons with OSA
Authors: Zhou Sirui , Han Xue , Shi Zhenzhen , Zhang Qiang , Cox John , Pak Victoria 
Published: April 2024

Abstract

Introduction
Inflammation and oxidative stress are important pathways in the associated downstream obstructive sleep apnea (OSA)-related diseases such as cardiovascular disease and Alzheimer’s. Aspartic acid, an endogenously produced amino acid with anti-inflammatory and anti-oxidative properties, may potentially impact inflammation associated outcomes. This study explored associations between sleep latency (from the Pittsburgh Sleep Quality Index) and aspartic acid. Other targeted metabolites with similar anti-inflammatory and anti-oxidative properties as aspartic acid were also explored in persons with OSA.

Methods
This cross-sectional study included 88 participants newly diagnosed and untreated for OSA that underwent an overnight in-lab or at home sleep study recruited from the Emory Mechanisms of Sleepiness Symptoms Study (EMOSS). Fasting morning blood plasma samples were collected after an overnight sleep study. A multiple linear regression model was utilized to examine the association between metabolites of interest and sleep latency, controlling for baseline covariates of age, sex, body mass index (BMI), race, smoking status, and apnea hypopnea index (AHI).

Results
The mean age was 50.1 (13.1), the mean BMI was 36.2 (9.2), mean AHI was 33.2 (26.2), and 56% were male. Almost half (43%) reported hypertension, and 6.8% reported heart failure; other demographics include 5.7% and 1.1% for stroke and myocardial infarction, respectively. We observed no statistically significant associations between aspartic acid and sleep latency. Other targeted metabolites of interest with similar properties to aspartic acid that were explored included C16 ceramides, sphinganine, and sphingosine-1-phosphate, which were also not associated with sleep latency.

Conclusion
Targeted metabolites within the inflammatory and oxidative stress pathways were not associated with sleep latency in persons with OSA. Future work should explore whether or not other inflammation or oxidative-stress related metabolites or pathways may have a role in order to improve sleep.

Support (if any)
0533 Sleep Spindle Features in Obstructive Sleep Apnea: Results from the Sleep Heart Health Study
Authors: Chen Daniel , Reid Kathryn , Knutson Kristen , Grimaldi Daniela 
Published: April 2024

Abstract

Introduction
Obstructive sleep apnea (OSA) is associated with altered cognitive function and dementia, but the mechanisms underlying these associations remain unclear. One proposed mechanism is that OSA, by chronically fragmenting sleep, decreases the number and activity of sleep spindles, which are important for memory consolidation. Few studies have examined sleep spindle characteristics in people with OSA and with conflicting results. This study aims to examine sleep spindle characteristics in people with and without OSA from a multicenter community-based prospective cohort study, the Sleep Heart Health Study (SHHS).

Methods
The analysis was conducted on 5804 subjects of which 2830 had no OSA (apnea-hypopnea index (AHI) &lt; 5, 61±12 years, 65% women), and 2974 had OSA (AHI ≥5, 65±11 years, 39% women). Spindle density (n/min) and frequency (Hz) were measured during non-rapid eye movement stage 2 from the left central electroencephalographic derivation. Arousal index (AI, n/hr) was used as a measure of sleep fragmentation and Epworth Sleepiness Scale (ESS) as a measure of subjective daytime sleepiness. Linear regression models adjusted for age, sex, BMI and education level were used to compare spindle features in OSA vs non OSA, and to examine the association between spindle features with AHI, oxygen desaturation degree and duration, AI and ESS in OSA group.

Results
In fully adjusted models, spindle density was lower in OSA compared to no OSA (-0.067 spindles per minute, p=&lt; 0.001). In OSA group, lower sleep spindle density was associated with higher AHI (β= -0.003, p=&lt; 0.001), higher AI (β= -0.002, p=0.020) and higher ESS score (β= -0.007, p=0.007) indicating higher daytime sleepiness. Spindle density was not associated with oxygen desaturation degree or duration. Spindle frequency was similar between OSA and non OSA.

Conclusion
These findings indicate that spindle density is reduced in people with OSA compared to those without OSA, and this reduction correlates with OSA severity (AHI) and levels of daytime sleepiness. Reduced spindle density in OSA might contribute to OSA-related cognitive impairment and future work is needed to investigate the relationship between reduced spindle density and cognitive function in OSA.

Support (if any)
Center for Circadian and Sleep Medicine
0534 The Accuracy of Predicting Snoring Frequency Using Snoring Surveys
Authors: Tracy Emma , Ediger Tatiana , Kumar Ajay Karthick Senthil , Song Xin , Maidens John , He David , Moyen Nicole 
Published: April 2024

Abstract

Introduction
Frequent snoring can be an indicator of sleep apnea and if left untreated, can lead to cardiovascular disease and other chronic conditions. Thus, it is crucial for health professionals to easily identify frequent snorers. Many questionnaires attempt to predict snoring frequency; however, they are lengthy, require manual scoring, and/or do not have data showing snoring predictability. Therefore, we investigated which questions from various snoring surveys best predict snoring frequency.

Methods
Seventy-two adults (60 male, 12 female; 42 ± 11 y) completed the Snore Outcomes Survey (SOS; 8 items), Pittsburgh Sleep Quality Survey (PSQI; 19 items), and the STOP-Bang survey (STOP; 8 items) before starting the study. To determine snoring frequency, subjects slept with SnoreClock, an audio-based app, for 1-7 nights (241 total nights). SnoreClock reports snoring frequency based on the cumulative minutes spent snoring out of the total recording time. Stepwise multiple linear regressions were used to identify the survey questions that best predicted mean snoring frequency.

Results
The single question that was the best predictor of average snoring frequency was from the SOS: “Please describe when you snore” [I don’t snore, I snore very rarely, I snore in only certain positions, I snore most of the time, I snore all of the time] (y=9.9x+0; R2=0.62; P&lt; 0.001). PSQI had two significant questions regarding sleep quality and enthusiasm to get things done (R2=0.45; P&lt; 0.001). STOP had three key predictors: daytime sleepiness, age over 50, and observed choking/gasping during sleep (R2=0.51; P&lt; 0.001). The model including all survey questions identified two questions as most important in predicting snoring: the STOP choking question (β=23.07) and the SOS describe snoring question (β= 8.15; R2=0.64; P&lt; 0.001).

Conclusion
Subjects can potentially be rapidly screened for snore-related health issues by asking the singular question “Describe when you snore.” This one question has an R2=0.62 in terms of predicting snoring frequency compared to three separate snoring surveys, and therefore may be used to accelerate patient care.

Support (if any)
This study was funded by Eight Sleep, Inc.
0538 Healthcare Resource Utilization in Treated Obstructive Sleep Apnea Patients with Comorbid Insomnia
Authors: Pépin Jean-Louis , Malik Anita , Woodford Caleb , Malhotra Atul , Sterling Kimberly , Cistulli Peter , McConnell Will , Alpert Naomi , Cole Kate 
Published: April 2024

Abstract

Introduction
Insomnia and obstructive sleep apnea (OSA), comorbidly known as COMISA, are the two most common sleep disorders, with each impairing treatment efficacy for the other. Real-world studies on treatment of patients with COMISA are lacking, thus this study aimed to examine the clinical and economic impact of treating OSA with positive airway pressure (PAP) therapy in this population.

Methods
This retrospective study linked administrative claims data to PAP device usage data for newly diagnosed OSA patients initiating PAP (index date) between January 2015 and April 2019. Adults with evidence of insomnia (insomnia prescription fill or insomnia ICD-9/10 diagnosis code) in the year prior and in the two-year period after index were included. Inverse probability of treatment weighting and Wilcoxon rank-sum tests were used to compare healthcare resource utilization (HCRU) over 2 years across varying levels of PAP adherence.

Results
From a sample of 26,965 COMISA patients, 28% met the criteria for consistently-adherent, 44% were intermediately-adherent, and 28% were non-adherent to PAP therapy over 2 years after index. The majority of intermediately-adherent patients had consistent PAP use in the first year of therapy, with decreased use in the second year. Consistently-adherent patients had fewer emergency room (ER) visits per patient than intermediately-adherent patients or non-adherent patients (year 1: 0.49 vs 0.59 [P&lt; 0.001], vs 0.68 [P&lt; 0.001]; year 2: 0.46 vs 0.54 [P&lt; 0.001], vs. 0.60 [P&lt; 0.001]). Consistently-adherent patients also had fewer all-cause hospitalizations per patient than intermediately- and non-adherent patients in both follow-up years (year 1: 0.10 vs 0.15 [P&lt; 0.001], vs. 0.14 [P&lt; 0.001]; year 2: 0.09 vs 0.12 [P&lt; 0.001], vs. 0.13 [P&lt; 0.001]). Based on major diagnostic category (MDC) codes, consistently-adherent patients saw the largest reductions in hospitalizations related to the respiratory, circulatory, nervous, and hepatobiliary systems, as well as those for alcohol/drug use or induced mental disorders.

Conclusion
This study showed reductions in all-cause hospitalizations and ER visits for COMISA patients who were consistently-adherent to PAP therapy over 2 years compared to those with lower adherence. PAP therapy is one facet of COMISA treatment that, if adhered to, can improve health outcomes.

Support (if any)
ResMed