bluuebunny/crossref_metadata_2025_split huggingface dataset:
378 Use of a Clinician’s Global Impression of Severity scale to measure insomnia severity in Alzheimer’s disease-dementia patients
Authors: Saretsky Todd , Ceesay Paulette , Zhong Wenjun , Herring W Joseph , Lines Christopher , Qureshi Zaina 
Published: May 2021

Abstract

Introduction
Full montage polysomnography (PSG) is the gold standard for the objective evaluation of sleep but is time consuming and inaccessible to most clinicians. A Clinician’s Global Impression of Severity (CGI-S) scale can be used in clinical practice to provide a subjective assessment of patients’ insomnia severity. However, the utility of a CGI-S scale for assessing insomnia in patients with Alzheimer’s disease (AD)-dementia is not well understood. In a recent Phase III randomized, placebo-controlled clinical trial (NCT02750306), patients on suvorexant with AD-dementia and insomnia showed improvements in both PSG total sleep time (TST) and CGI-S scores. We conducted additional analyses to examine the association between CGI-S and PSG-TST to inform on the possible use of a CGI-S scale to assess insomnia severity in patients with AD-dementia in real-world settings.

Methods
Patients (N=285) met clinical diagnostic criteria for both probable mild-to-moderate AD-dementia and insomnia. The primary endpoint was change-from-baseline in overnight PSG-TST at Week-4. A single-item CGI-S rating of insomnia with responses of 1 (normal, not ill at all) to 7 (among the most extremely ill patients) was completed by a trained rater at baseline and after 2 and 4 weeks. CGI-S was an exploratory endpoint. Post-hoc correlational analyses and analyses of distribution of change-from-baseline to Week-4 in CGI-S response categories were performed.

Results
Pearson correlation indicated a significant association at baseline between PSG-TST and CGI-S (r=-0.18, nominal p=0.004). A correlation of change-from-baseline to Week-4 also indicated an association between PSG-TST and CGI-S (r=-0.24, nominal p&lt;.0001). The distribution of change in CGI-S response category results at Week-4 showed that, compared to placebo, numerically less patients on suvorexant remained stable or worsened by &gt;1 response category (21.8% vs. 29.4%, respectively) and numerically more improved by ≥1 response category (73.3% vs. 67.9%, respectively).

Conclusion
Our findings suggest that a CGI-S scale may be a useful tool for assessing insomnia severity in mild-to-moderate AD-dementia patients. Future studies with these patients are needed to determine the utility of a CGI-S scale in real-world settings.

Support (if any)
This study was funded by Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA
385 Subjective-objective sleep discrepancy and quality of life in self-reported insomnia and sleep apnea
Authors: Ma Yan , Goldstein Michael , Davis Roger , Yeh Gloria 
Published: May 2021

Abstract

Introduction
Current diagnostic classifications define insomnia based on self-reported sleep difficulties. However, differences between self-reported and objectively measured sleep parameters (subjective-objective sleep discrepancy or sleep misperception) are very common. Insomnia and sleep apnea cause common impairments that overlap and have negative impacts on overall health. Previous studies have encouraged an in-depth understanding of subjective-objective sleep discrepancy to inform a role for behavioral, mind-body approaches to insomnia. In this study, utilizing patients with insomnia and comorbid sleep apnea, we aimed to understand associations between self-reported insomnia, sleep difficulties, sleep misperception and quality-of-life.

Methods
We conducted a secondary analysis using data from the Sleep Heart Health Study (a multi-site nationally representative sample) to examine the profile of subjective and objective sleep measures in people with insomnia (IS, n=73) and comorbid sleep apnea (IS+SA, n=143), compared to individuals with sleep apnea only (SA, n=296) and normal sleep controls (NSC, n=126). We also compared the magnitude of sleep misperception between these four groups and examined the corresponding impact of subjective insomnia complaints on quality-of-life.

Results
Sleep discrepancy was found in all four groups. After controlling for age, sex, mental health conditions, sleep apnea severity, and objectively measured sleep time, the presence of self-reported insomnia had the strongest association with sleep discrepancy on total sleep time (TST, β=-34.4, p&lt;0.001) and sleep onset latency (SOL, β=14.7, p&lt;0.001). Subjects who reported no difficulty falling asleep slightly underestimated their sleep onset latency, while those who reported difficulty substantially overestimated sleep latency. Self-reported insomnia had a significantly negative impact on the quality of life in both physical and mental components (p&lt;0.001).

Conclusion
Sleep discrepancy exists in normal controls, insomnia, and sleep apnea. Subjects with self-reported insomnia have a higher degree of sleep misperception. Both sleep apnea and self-reported insomnia are associated with negative QOL. Those with comorbid sleep apnea report the greatest sleep discrepancy and lowest QOL. Further research is needed to better understand individual profiles of misperception and insomnia phenotypes, apnea comorbidity and quality-of-life. Behavioral, mind-body interventions may offer strategies to address mental stress, sleep misperception, and insomnia.

Support (if any)
NCCIH T32AT000051 and NHLBI 5T32HL007901-22
390 Risk of Obstructive Sleep Apnea: A Comparison Between Ischemic and Hemorrhagic Stroke
Authors: Griesbach Grace , Robinson Stephanie , Howell Stefanie 
Published: May 2021

Abstract

Introduction
Obstructive Sleep Apnea (OSA) is common amongst stroke patients. Distinction between the prevalent types of stroke when evaluating sleep may prove useful to prevent recurrent strokes. There is a lack of information on how apnea prevalence following ischemic stroke compares to hemorrhagic stroke, particularly during the chronic period. Here we set out to retrospectively determine if apnea incidence was more marked in ischemic stroke patients compared to those that received a hemorrhagic stroke diagnose; while taking in account medications taken during the sleep evaluation.

Methods
Medical records were reviewed from 103 patients diagnosed with stroke that underwent a type I fully attended overnight polysomnography. Diagnosis of embolic, thromboembolic or hemorrhagic stroke was obtained from a neurological report that was typically confirmed by computed tomography or magnetic resonance imaging. Medications that the subject was taking at the time of the sleep study were documented by the sleep technologist.

Results
Arousals and microarousal index was higher in the ischemic group (p&lt;0.005). Age adjusted assessment of apnea and hypopnea events, indicated that subjects with an ischemic stroke had a higher incidence of apnea and hypopnea compared to the hemorrhagic group (p&lt;0.005). Ischemic subjects were also more likely to present severe apnea (AHI &gt; 30) (p&lt;0.005). In comparison, a higher percentage of hemorrhagic subjects had an AHI below 5 (p&lt;0.005). Type of hemorrhagic stroke did not have an impact on apnea/hypopnea variables. Those with an ischemic stroke were taking significantly more lipid lowering agents (p&lt;0.05). Logistic regression analysis indicated that the predictive probability of apnea (AHI &gt; 5) increased from 0.55 to 0.81 (p &lt; 0.05) when anti-hypertensive use was considered. An increase in predictive values for apnea was observed for indicators of diabetes such as use of anti-diabetics with or without consideration of diabetic history (p &lt; 0.05).

Conclusion
Data suggests that ischemic patients present a higher artherosclerotic load that may contribute to the higher incidence of severe OSA. These data indicate that it is relevant to consider stroke type when determining the risk of OSA thus facilitating new strategies for stroke recurrence prevention.
Support (if any):
391 Upper Airway Resistance Syndrome is associated with high Cyclic Alternating Pattern
Authors: Godoy Luciana , Soster Letícia , Bueno Clarissa , Togeiro Sonia , Poyares Dalva , Tufik Sergio , Palombini Luciana 
Published: May 2021

Abstract

Introduction
Upper Airway Resistance Syndrome (UARS) is suspected in individuals with excessive daytime sleepiness, fatigue, and sleep fragmentation associated with increased respiratory effort. UARS can negatively impact daytime function. Conventional polysomnography parameters do not demonstrate significant abnormalities in UARS patients but increase in RERAs and arousal index. Cyclic alternating pattern (CAP) is a periodic electroencephalogram activity of non-REM sleep that expresses a condition of sleep instability. The objective of the study was to compare CAP components between UARS patients and health individuals.

Methods
Fifteen subjects with UARS and 15 age- and sex- matched controls had their sleep study blinded analyzed. UARS criteria were the presence of sleepiness (Epworth Sleepiness Scale – ESS - ≥ 10) and/or fatigue (Modified Fatigue Impact Scale ≥ 38) associated with an apnea/hypopnea index (AHI) ≤ 5 and a respiratory disturbance index (RDI) &gt; 5 events/hour of sleep, and/or flow limitation in more than 30% of total sleep time. Control group criteria were AHI &lt; 5 events/hour, RDI ≤ 5 events/hour and &lt; 30% of TST with flow limitation and ESS &lt; 10, without sleep, clinical, neurological, or psychiatric disorder. CAP electroencephalogram of both groups was analyzed.

Results
We found higher CAP rate (p = 0.05) and CAP index in N1 stage (p &lt; 0.001) and in N3 stage (p &lt; 0.001) in UARS patients compared to control group. Considering only CAP phase A1 analysis, UARS patients presented higher CAP rate (p = 0.05) and CAP index in N1 stage (p &lt; 0.001) as well as CAP index in N3 stage (p &lt; 0.001) compared to control group. Considering only CAP phase A2 analysis, UARS patients also presented higher number of CAP in N1 stage (p = 0.05). There was no significant difference for CAP phase A3 between groups.

Conclusion
Although UARS is associated with high arousal index, we found increase in CAP phase A1 and A2, which do not include necessarily AASM arousals, suggesting not only sleep fragmentation but also sleep instability.

Support (if any)
Associação Fundo de Incentivo à Pesquisa (AFIP) and Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP).
395 Exercise Capacity is Maintained in Older Military Personnel with Moderate to Severe Obstructive Sleep Apnea
Authors: Arora Subodh , Powell Tyler , Foster Shannon , Hansen Shana , Morris Michael 
Published: May 2021

Abstract

Introduction
The relationship between moderate to severe OSA and exercise capacity remains unclear. Prior studies showing a reduction in VO2 max in this population have mostly involved middle-aged and overweight patients. We looked to determine if this trend in VO2 max was present in a similarly aged population of military personnel with previously undiagnosed moderate to severe OSA.

Methods
We studied 170 middle-aged male military members who underwent cardiopulmonary exercise testing (CPET) and polysomnography (PSG) as a part of a comprehensive evaluation for an established military program. For analysis, patients were categorized either into an OSA group (apnea-hypopnea index (AHI) ≥ 15 events/h) or control group (AHI &lt; 15 events/h). VO2 max was compared between groups.

Results
Mean AHI was 29.0 in the OSA group (n =58) versus 7.4 in the controls (n = 112). Patients were of similar age (53.1 vs. 53.7 years) and BMI was slightly higher in the OSA group (27.5kg/m2 versus 26.3 kg/m2, P = .0077). Percent-predicted VO2 max was supernormal in both groups, though was comparatively lower in the OSA group (117% vs. 125%; P &lt;.001). There was a trend toward a blunted heart rate response to exercise in the OSA group as represented by peak heart rate (163 vs. 178; p=0.07).

Conclusion
Older military personnel with moderate to severe OSA have normal exercise capacity. This may suggest that the low-arousal OSA phenotype often noted in military personnel does not significantly influence exercise capacity or that regular exercise helps limit its impact. It remains likely that the effect of untreated OSA on exercise capacity is influenced by several variables including age, BMI, OSA phenotype, and regularity of exercise. Trends in VO2 max and peak heart rate noted in this study may suggest that untreated OSA in certain populations can negatively impact exercise capacity

Support (if any)
No external funding
399 Using Machine Learning to Inform Extraction of Clinical Data from Sleep Study Reports
Authors: Mazzotti Diego , Staley Bethany , Keenan Brendan , Pack Allan , Schwab Richard , Boland Mary Regina 
Published: May 2021

Abstract

Introduction
In-laboratory and home sleep studies are important tools for diagnosing sleep disorders. However, a limited amount of measurements is used to inform disease severity and only specific measures, if any, are stored as structured fields into electronic health records (EHR). We propose a sleep study data extraction approach based on supervised machine learning to facilitate the development of specialized format-specific parsers for large-scale automated sleep data extraction.

Methods
Using retrospective data from the Penn Medicine Sleep Center, we identified 64,100 sleep study reports stored in Microsoft Word documents of varying formats, recorded from 2001–2018. A random sample of 200 reports was selected for manual annotation of formats (e.g., layout) and type (e.g. baseline, split-night, home sleep apnea tests). Using text mining tools, we extracted 71 document property features (e.g., section dimensions, paragraph and table elements, regular expression matches). We identified 14 different formats and 7 study types. We used these manual annotations as multiclass outcomes in a random forest classifier to evaluate prediction of sleep study format and type using document property features. Out-of-bag (OOB) error rates and multiclass area under the receiver operating curve (mAUC) were estimated to evaluate training and testing performance of each model.

Results
We successfully predicted sleep study format and type using random forest classifiers. Training OOB error rate was 5.6% for study format and 8.1% for study type. When evaluating these models in independent testing data, the mAUC for classification of study format was 0.85 and for study type was 1.00. When applied to the large universe of diagnostic sleep study reports, we successfully extracted hundreds of discrete fields in 38,252 reports representing 33,696 unique patients.

Conclusion
We accurately classified a sample of sleep study reports according to their format and type, using a random forest multiclass classification method. This informed the development and successful deployment of custom data extraction tools for sleep study reports. The ability to leverage these data can improve understanding of sleep disorders in the clinical setting and facilitate implementation of large-scale research studies within the EHR.

Support (if any)
American Heart Association (20CDA35310360).
400 Utility of the STOP Questionnaire in Predicting Sleep Disordered Breathing in Older Women Veterans
Authors: Kelly Monica , Moghtaderi Isabel , McGowan Sarah Kate , Carlson Gwendolyn , Josephson Karen , Mitchell Michael , Swistun Dominika , Fung Constance , Zeidler Michelle , Badr Safwan , Martin Jennifer 
Published: May 2021

Abstract

Introduction
Sleep disordered breathing (SDB) is underdiagnosed in older women, despite a significant increase in SDB prevalence post-menopause. Few studies have assessed the diagnostic accuracy of SDB screening questionnaires in older women, particularly older Women Veterans (WV). WV have higher rates of SDB compared to non-Veteran women and are particularly vulnerable to sleep disorders in general. We examined the diagnostic accuracy of the STOP questionnaire compared to home sleep apnea testing (HSAT) that includes sleep time estimation (i.e., WatchPAT) in older WV.

Methods
Cross-sectional baseline data obtained from chart review were combined from two behavioral sleep intervention studies targeting WV with sleep difficulties (i.e., insomnia symptoms) or SDB risk factors (e.g., hypertension, obesity). A total of 136 older WV (50-91y; age=60.0±7.8y) completed the STOP questionnaire (yes/no: snoring, tiredness, observed breathing pauses, and high blood pressure [BP]) and had an apnea-hypopnea index (AHI) available from their baseline HSAT (WatchPAT). Sensitivity, specificity, and positive and negative likelihood ratios (+LR/-LR) were calculated to characterize the diagnostic accuracy of STOP≥2 for AHI≥5 (mild SDB) or AHI≥15 (moderate SDB).

Results
70.6% (n=96) of participants endorsed a STOP≥2, 83.8% (n=114) demonstrated an AHI≥5 and 46.3% (n=63) demonstrated an AHI≥15. For AHI≥5, sensitivity was 73.7% (95% CI=64.6,81.5%), specificity was 45.5% (95% CI=24.4,67.8%), +LR was 1.35 (95% CI=0.91, 2.01), and -LR was 0.58 (95% CI=0.33,1.00). For AHI≥15, sensitivity was 76.2% (95% CI=63.8,86%), specificity was 34.2% (95% CI=23.5,46.3%), +LR was 1.16 (95% CI=0.93,1.44), and -LR was 0.70 (95% CI=0.30,1.20).

Conclusion
The likelihood ratios for STOP≥2 limited the utility of the STOP vs. an HSAT system with sleep scoring in determining AHI. While the STOP correctly identified 3/4 of older WV with SDB on WatchPAT, it correctly identified &lt;50% of older WV without SDB. Screening measures that better capture predictors of moderate SDB in women at risk for SDB are needed, especially in older women who may not present clinically with the common SDB symptoms (i.e. snoring, tiredness, observed breathing pauses, and high BP). STOP compared to polysomnography studies are also needed.

Support (if any)
VA HSR&D IIR-13–058, IIR 16–244 and RCS 20–191; NIH/NHLBI K24 HL143055, VAGLAHS GRECC, VA Office of Academic Affiliations, and AASM Foundation.
404 Sleep-Disordered Breathing In Native Hawaiians/Pacific Islanders With Associated Comorbidities And Adherence To Therapy
Authors: Ryujin Darin , Sundar Krishna , Gilles Allyson 
Published: May 2021

Abstract

Introduction
Sleep-disordered breathing in Native Hawaiians and Pacific Islanders (NHPIs), its relationship to type 2 diabetes mellitus (DM), chronic renal, and heart disease, is not well known. NHPIs comprise only 1.3% of Utah’s population, but have the highest rates of DM and deaths due to diabetic kidney disease in Utah. This study assessed the nature of sleep-disordered breathing, its association with demographic variables, and comorbidities, and adherence patterns to positive airway pressure (PAP) therapy.

Methods
University of Utah sleep clinics patient databases from 2014 were evaluated to identify NHPIs using first/last names. Electronic medical records were reviewed to confirm patient ethnic origin, demographic data, and comorbidities. The most recent PAP downloads were obtained.

Results
Of 106 NHPIs were identified, data available for 104 patients (71 males, 33 females) was analyzed. Mean age of males was 47 + 13 years and females 48±13 years. Prevalence rates of obesity were 13% (female 9%, male 15%) with BMI≥30, 33% (female 24%, male 23%) with BMI≥35, and 49% (female 58%, Male 23%) with BMI≥40). Majority of patients had severe OSA (61% males with AHI≥30; 39% females with ≥ 30), with overall mean AHI of 47±38. A high prevalence of comorbidities was noted: 61% hypertension (male 58%; female 67%), diabetes 54% (male 48%, female 67%), renal disease 20% (male 21%, female 18%), coronary artery disease 13% (male 14%, female 9%), and congestive heart failure 13% (male 15%, female 9%). Prevalence of lung disease was low 13% (male 9%, female 18%).

Conclusion
NHPIs evaluated for sleep-disordered breathing have high rates of obesity, severe OSA, and concerning comorbidities. PAP adherence in this group was poor compared to overall adherence for patients seen in University of Utah sleep clinics (~70%). Further research is required to assess the relationships between OSA, associated comorbidities, and disease outcomes. Addressing low rates of PAP adherence in this population may afford opportunities to improve health outcomes.

Support (if any)
n/a
406 Clinical Validation of AI Scoring in Adult and Pediatric Clinical PSG Samples Compared to Prospective, Double-Blind Scoring Panel
Authors: Fernandez Chris , Rusk Sam , Glattard Nick , Nygate Yoav , Turkington Fred , Watson Nathaniel 
Published: May 2021

Abstract

Introduction
Despite an appreciable rise in sleep wellness and sleep medicine A.I. research publications, public data corpuses, institutional support, and health A.I. research funding opportunities, the availability of controlled-retrospective, hybrid-retrospective-prospective, and prospective-RCT quality clinical validation study evidence is limited with respect to their potential clinical impact. Furthermore, only a few practical examples of A.I. technologies are validated, in use today clinically, and widely adopted, to assist in sleep diagnoses and treatment. In this study, we contribute to this growing body of clinical A.I. validation evidence and experimental design methodologies with an interoperable A.I. scoring engine in Adult and Pediatric populations.

Methods
Stratified random sampling with proportionate allocation was applied to a database of N&gt;10,000 retrospective diagnostic clinical polysomnography (PSG), selected by evidence grading standards, with controls applied for OSA severity, diagnoses; sleep, psychiatric, neurologic, neurodevelopmental, cardiac, pulmonary, metabolic disorders, medications; benzodiazepines, antidepressants, stimulants, opiates, sleep aids, demographic groups of interest; sex, adult age, pediatric age, BMI, weight, height, and patient-reported sleepiness, to establish representative N=100 Adult and N=100 Pediatric samples. Double Blinded scoring was prospectively collected for each sample by 3 experienced RPSGT certified sleep technologists randomized from a pool of 9 scorers. Sensitivity (PA), Specificity (NA), Accuracy (OA), Kappa (K), and 95% Bootstrap CI’s are presented for sleep stages, OSA/CSA, hypopnea 3%/4%, arousals, limb movements, Cheyenne-Stokes respiration, periodic breathing, atrial fibrillation, and other events, and normative, mild, moderate, and severe OSA categories for global-AHI and REM-AHI. Results for Sleep Staging and OSA Severity Diagnostic Accuracy are summarized.

Results
A.I. scoring performance meet but in most cases exceeded initial clinical validation study (N=72 Adults, 2017) PA, NA, OA, K point-estimates and confidence-interval results for the 26 event types and 8 AHI-categories evaluated. The Adult sample showed 87%/94% Sensitivity/Specificity across all stages (Wake/N1/N2/N3/REM) and 94%/96% Sensitivity/Specificity for AHI&gt;=15. The Pediatric sample showed 87%/93% Sensitivity/Specificity staging, 89%/98% Sensitivity/Specificity AHI&gt;=15. Observed Accuracy was &gt;90% for Adults and Pediatrics all 26 events and 7 AHI-categories analyzed, except REM-AHI&gt;=5 (85%/82% Adults/Pediatrics).

Conclusion
We provide clinical validation evidence that demonstrates interoperable A.I. scoring performance in representative Adult and Pediatric patient clinical PSG samples when compared to prospective, double-blind scoring panel.
Support (if any):