bluuebunny/crossref_metadata_2025_split huggingface dataset:
409 Dynamic Phenotype Learning: A Novel Machine Learning Approach To Develop And Discover New OSA Sub-Types
Authors: Fernandez Chris , Rusk Sam , Nygate Yoav , Glattard Nick , Arguelles Jessica , Shi Jiaxiao , Hwang Dennis 
Published: May 2021

Abstract

Introduction
Current approach to processing polysomnography is labor intensive and produces metrics that are poor at identifying obstructive sleep apnea (OSA) phenotypes necessary to enhance personalized care. We describe our approach to utilize Dynamic Phenotype Learning (DPL) as an innovative machine learning technique to identify OSA subtypes that can better predict clinical risk and success with therapies.

Methods
This study is a collaboration between Kaiser Permanente Southern California (KPSC), a large integrated health system, and EnsoData Research, which specializes in applied A.I. analysis of physiologic waveforms. KPSC sleep medicine compiled a database of N=5,368–234,250 subjects that include Types I, II, III, or IV sleep study data, daily PAP data, patient reported data, and comprehensive electronic health record information, with present research applications to study the relationship between OSA and PAP adherence with cardiovascular outcomes, health economic impacts, novel coronavirus (COVID-19) outcomes, and predictive PAP adherence and OSA severity clinical decision tools. DPL is a machine learning method for studying known and new biomarkers and care-pathway indices, including personalized screening, diagnostic, treatment, adherence, and outcomes predictors, that can be rooted in physiologic data. DPL processes waveform signal data without scoring, annotations, or expert synthesis, by applying a novel machine learning mechanism that blurs supervised and unsupervised deep learning paradigms, to find relationships between physiome dynamics expressed in waveforms and phenotypes and endotypes of interest.

Results
We demonstrate DPL method with an illustrative study on known indices, to explain its ability to (1) lift theoretical-empirical predictive accuracy ceilings and (b) reduce several sources of bias and variance. We show DPL exceeds the ROC-AUC and PRC-AUC of equivalent deep learning models in N=30,000 Report-Demographic (ODI, PLMSI, Weight), Scoring (REM, OSA), and Waveform (EEG, PPG) datasets respectively to predict AHI, TST, Brain Age, and OSA-Insomnia. We present our current collaboration advancing DPL to identify specific phenotypes that better predict: (a) cardiovascular risk; (b) neurocognitive outcomes; (c) response to PAP and alternative therapies.

Conclusion
DPL methods are being applied to large and comprehensive patient dataset to identify phenotypic indices and biomarkers with potential to take us beyond the AHI, and uncover relationships between OSA sub-types, treatments, and health outcomes.
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417 Transgender Hormone Therapy and Sleep-Disordered Breathing
Authors: Gavidia Ronald , Dunietz Galit Levi , Matlen Lisa , Hershner Shelley , Stroumsa Daphna , Kaplish Neeraj , O’Brien Louise 
Published: May 2021

Abstract

Introduction
Sex hormones may affect human respiration during wakefulness and sleep. Testosterone has been associated with increased obstructive respiratory events contributing to sleep-disordered breathing (SDB) in men, whereas a protective effect against SDB has been attributed to estrogen in women. These associations, primarily observed in cisgender populations, have been rarely examined in transgender individuals on hormone replacement therapy (HRT). The present study investigated associations between HRT and SDB in transgender adults.

Methods
A chart review of medical records from transgender patients was conducted in a large academic sleep medicine center. Individuals were included if they were at least 18 years old, had one or more sleep complaints, and SDB testing results available. Participants were then stratified by affirmed gender (transmasculine and transfeminine) and by HRT status. We used descriptive statistics procedures to examine differences between gender and HRT groups. Associations between HRT and the apnea-hypopnea index (AHI) were estimated with age-adjusted linear regression models.

Results
Of the 194 individuals identified, 89 satisfied the inclusion criteria. Nearly half of participants were transmasculine (52%). The mean age was 38±13 years, and mean body mass index was 34.7±9.0 Kg/m2. Approximately 60% of participants were on HRT at the time of SDB evaluation. Transmasculine people who were prescribed testosterone had a significantly increased AHI and lower oxygen nadir in comparison to transmasculine individuals not on testosterone (AHI 36.8±37.8/hour vs.15.3±16.6/hour, p=0.01; oxygen nadir 83.4±8.3% vs. 89.1±2.4%, p=0.001). In contrast, differences between transfeminine people with and without feminizing HRT (androgen blocker + estrogen) were not statistically significant (AHI 21.4±27.7/hour vs. 27.7±26.0/hour, p=0.45; oxygen nadir 86.5±6.7% vs. 84.1±7.7%, p=0.29). Linear regression models adjusted for age found an association between HRT and AHI for transmasculine (β=16.7, 95% CI 2.7, 30.8), but not for transfeminine participants (β=-2.5, 95% CI -17.9, 12.9).

Conclusion
These findings suggest differential associations between HRT and AHI among transgender individuals, with transmasculine on testosterone having a significant increase in AHI. Prospective studies with large sample sizes are warranted to evaluate these associations.

Support (if any)
Dr. Gavidia’s work was supported by an NIH/NINDS T32-NS007222 grant
422 Influence of Sex-Specific Differences in Inpatient Sleep Testing Approach for Diagnosis of Obstructive Sleep Apnea
Authors: Orbea Cinthya Pena , McIntire Cody , Wang Lu , Mehra Reena 
Published: May 2021

Abstract

Introduction
Increased attention has been focused on sex-specific differences in approaches to diagnostic testing for obstructive sleep apnea (OSA) given differences in hypoxia, arousal thresholds and sleep state dependent influences, but with sparse data available for inpatient testing. We postulate that women are more likely to have a lesser degree of sleep apnea on inpatient home sleep apnea testing (HSAT) versus polysomnography (PSG).

Methods
The Cleveland Clinic Sleep Laboratory registry was queried for inpatient sleep testing (HSAT or PSG conducted over the last 15 years. Demographics, comorbidities, and sleep study (Nihon Kohden®) data were collated. Logistic regression was used to examine sleep study type predictive of OSA at various severity thresholds (apnea hypopnea index (AHI, 3 or 4% hypopnea rule)&gt;5,&gt;15 and &gt;30 and hypoxia (11% (median) time spent with SaO2&lt;90%) adjusted for age, race and body mass index and comorbidities (hypertension, coronary artery disease, arrhythmias, heart failure, diabetes, stroke, chronic obstructive pulmonary disease, mood disorders, respiratory failure and epilepsy with a sex interaction term) (OR, 95%CI presented).

Results
The analytic sample was comprised of 639 patients: age:55.8±16.3 years, 45% female, 73% Caucasian, BMI:37.5 ± 13.3kg/m2, 74% had OSA and 51% HSAT. Men had higher AHI:16.2 [5.9, 42.3] vs 8.2 [2.9, 20.7]p&lt;0.001, higher arousal index:33.1[18.9,.54] vs 25.3 [15.6, 39.2]p=0.003. Women had higher BMI:40.2 ±14.7, vs 36 ±11.7kg/m2,p&lt;0.001. Unlike AHI&gt;5, at AHI&gt;15, men had lower odds of OSA: OR=0.51:0.32–0.80,p=0.004 for HSAT versus PSG compared to women: OR=1.03:0.61–1.72,p=0.92; interaction p-value=0.046. Men had lower odds of OSA (AHI &gt;30): OR=0.57(0.35,0.92,p=0.022) in HSAT vs PSG; albeit sex-interaction was not statistically significant. Men versus women had 2-versus 3-fold higher hypoxia ie. OR=2.04:1.22–3.41,p=0.006 in men undergoing HSAT versus PSG with strength of association higher in women: OR=3.03:1.68–5.46,p=0.001, interaction p-value=0.32

Conclusion
We unexpectedly observe sex-specific differences in inpatient sleep testing such that men had an overall lower odds of detection of moderate to severe and OSA and nocturnal hypoxia relative to women with HSAT versus PSG. Future investigation focused on concurrent inpatient PSG and HSAT should verify these sex-specific findings and clarify potential biophysiologic rationale

Support (if any)
Transformative Neuroscience Research Development Program: Multimodal Neurocardiorespiratory Physiologic Sleep Signal Repository Transformative Resource Facilitating Transdisciplinary Research Opportunities
423 Evaluation of Electronic Medical Record Artificial Intelligence Screening Tools for Undiagnosed OSA
Authors: Rusk Sam , Nygate Yoav , Turkington Fred , Fernandez Chris , Glattard Nick , Watson Nathaniel 
Published: May 2021

Abstract

Introduction
The STOP-Bang is a concise, simple and widely adopted obstructive sleep apnea (OSA) screening tool. However, it has limited predictive ability and is susceptible to subjective reporting bias. Artificial Intelligence (AI) methodologies can be utilized together with existing data in electronic medical records (EMRs) to create new screening tools to increase diagnostic sensitivity and facilitate discovery of preclinical OSA phenotypes.

Methods
The study comprised two independent retrospective sleep study datasets: 1) Type III HSATS (N=5583) and, 2) Type I polysomnograms (N=1037). Each contained raw sleep study waveforms, manually scored sleep events (respiratory, arousal, sleep staging), and standard report indices (apnea-hypopnea index; AHI, arousal index). Additionally, the first dataset contained 90 EMR based metadata variables and the second dataset contained 54 EMR based metadata variables. Three random forest models were trained to detect OSA diagnostic thresholds (AHI&gt; 5, AHI&gt;15, and AHI&gt;30) over three different screening models: STOP-Bang, P-Bang (blood-pressure, BMI, age, neck-size, gender), and Common Clinical Data Set (CCDS)-OSA (all metadata variables simulating EMR CCDS standard).

Results
CCDS-OSA ROC-AUC exceeded STOP-Bang and P-Bang for both sleep study collections, resulting in AHI&gt;15 ROC-AUC values of 0.73 and 0.71 (CCDS-OSA) compared to AHI&gt;15 ROC-AUC values of 0.68 and 0.69 (STOP-Bang). Additionally, we analyzed the Gini feature importance ranking of the trained CCDS-OSA model to evaluate which variables showed highest predictive value of OSA. The ranking revealed the top 5 features were the five physiologic based STOP-Bang parameters, followed by EMR based physiologic measurements such as HDL, triglycerides, systolic BP, and disease conditions such as diabetes, hypertension, and depression.

Conclusion
This study shows that while STOP-Bang contains data critical to OSA screening, a variety of other EMR-based parameters can improve performance of OSA detection. AI-based EMR screening can provide a critical tool for more systematic and accurate screening of undiagnosed sleep apnea. Nationwide standards facilitating patient EMR data interoperable health information exchange, particularly the United States Core Data for Interoperability (USCDI CCDS), holds promise to foster broad clinical and research opportunities. Resulting data sharing will allow application of AI screening tools at the population health scale with ubiquitous, existing EMR data to improve population sleep health.
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428 Baroreflex sensitivity during handgrip in obstructive sleep apnea with and without CPAP
Authors: Pal Amrita , Martinez Fernando , Aysola Ravi , Harper Ronald , Henderson Luke , Macey Paul 
Published: May 2021

Abstract

Introduction
Obstructive sleep apnea (OSA) disrupts multiple aspect of autonomic regulation; it is unclear whether intervention with continuous positive airway pressure (CPAP) can correct such disruptions. One key index of autonomic regulation is baroreflex sensitivity (BRS), an index that indicates heart rate (HR) changes to blood pressure (BP) alterations, and which is a significant measure for evaluating long-term cardiovascular changes induced by OSA. BRS can be assessed from BP and HR changes during an autonomic challenge task such as handgrip (HG). In a cross-sectional study, we assessed BRS during HG in untreated OSA (OSA_un) and CPAP treated OSA (CPAP), together with healthy control (CON) participants to determine if CPAP can recover BRS.

Methods
We collected ECG and continuous beat-by-beat BP from 95 people: 32 newly-diagnosed OSA_un (51.5±13.9years; AHI 21.0±15.3events/hour; 20male); 31 CPAP (49.4±14.0years; 22.4±14.1events/hour in initial diagnosis; 23male); and 32 CON (44.1±13.8years; 10male). We acquired data over 7 mins, during which people performed three 30s HGs (60 s baseline, 90 s recovery, 80% maximum strength). We calculated BRS over the 7 min period using sequence analysis in AcqKnowledge 5.0 BRS, followed by group comparisons using ANOVA. We also analyzed BP, HR and their variabilities: BPV and HRV (sympathetic-vagal).

Results
Mean arterial BP increases during HG were similar in all groups, although baseline mean arterial BP was higher in OSA_unc and CPAP, relative to CON (p &lt; 0.05; OSA_un:mean±std, 90±11mmHg; CPAP: 88±10mmHg; CON 82±13mmHg). BRS was lower in OSA_un and CPAP, relative to CON (p &lt; 0.05; OSA_un: 13.1±7.6 ms/mmHg; CPAP: 13.7±9.0 ms/mmHg; control 18.3±11.9 ms/mmHg). Other cardiovascular measures of BPV, HR and HRV in addition to BP showed significant increases in response to HG, but these changes were similar in all 3 groups.

Conclusion
BRS during HG was reduced in both OSA_un and CPAP compared to CON, while HG evoked similar overall changes in BP and HR in all three groups. Although CPAP reduces sympathetic tone measured as Muscle Sympathetic Nerve Activity (MSNA), BRS appears to be unaffected by the intervention. Irreversible changes in the baroreflex network may occur with OSA that are not altered with CPAP usage.

Support (if any)
NR-017435, HL135562
437 Efficacy of a novel oral appliance and the influence of OSA pathophysiological traits on treatment response
Authors: Tong Benjamin , Bull Christopher , Chiang Alan , Donegan Michelle , Brown Elizabeth , Kwan Benjamin , Eckert Danny 
Published: May 2021

Abstract

Introduction
Oral appliance therapy is a recommended alternative to CPAP for OSA. Approximately 50% of patients have a major reduction in OSA severity but successful treatment outcome remains challenging to predict. Previous prediction methods have focused on clinical variables which have poor predictive value. OSA is recognised as a heterogenous disorder caused by 4 pathophysiological traits. The influence of OSA pathophysiological traits on oral appliance treatment outcome has been explored in recent physiological studies using simplified phenotyping methods. In this preliminary report, we prospectively compared differences in the 4 OSA phenotypes between responders and incomplete responders to a novel oral appliance with built in oral airway using gold standard phenotyping methodology.

Methods
Data from 22 people with OSA (AHI&gt;10events/h) have been analysed to date. A diagnostic in-laboratory PSG was initially conducted to confirm OSA. A detailed physiology PSG was carried out prior to commencement of oral appliance therapy. For this study night participants were instrumented with standard PSG equipment, nasal mask, pneumotachograph, epiglottic pressure catheter and intramuscular electrodes inserted perorally into the genioglossus to quantify baseline OSA phenotypic traits. Pcrit was quantified via CPAP dial downs and the non-anatomical traits were quantified from naturally occurring apneas and hypopneas. Participants were then fitted with a next generation novel oral appliance with a built-in oral airway (Oventus O2Vent Optima™) and titrated to at least 75% of maximum mandibular advancement. After acclimatization to therapy, participants were invited to undergo a treatment efficacy PSG.

Results
Oral appliance therapy reduced the AHI by 52% (21[15,31] vs. 11[7,16] events/h, p&lt;0.001). 46% of participants responded to oral appliance therapy based on the definition of AHI &lt; 10events/h. Preliminary analyses indicated that estimates of baseline upper airway collapsibility tended to be different in responders versus non-responders (responders have less collapsible airways).

Conclusion
The novel oral appliance reduced OSA severity by 50% with resolution of OSA in half of participants. Baseline pharyngeal collapsibility may be an important physiological predictor of treatment outcome.
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453 Cost-Effectiveness of Requalifying for Positive Airway Pressure Treatment After Initial Nonadherence
Authors: May Anna , Wang Lu , Singer Mendel 
Published: May 2021

Abstract

Introduction
Obstructive sleep apnea (OSA) is effectively treated with continuous positive airway pressure (CPAP). However, many people are not able to become adherent in the initial 90-day trial window for this therapy. Medicare requires a polysomnography and repeat trial documenting adherence before continuing payment for these services. Oral appliance therapy (OAT) is also an OSA first-line therapy but is less effective than CPAP.

Methods
We created a decision tree to model 4 strategies over a 5-year time horizon: (1) current policy, (2) direct referral for CPAP equipment, (3) OAT followed by CPAP under current policy, and (4) OAT followed by direct CPAP referral in a the Medicare population with mild-moderate OSA and nonadherence to a first attempt at CPAP therapy. Medicare fee schedules in 2020 defined costs. Incremental cost-effectiveness (ICER) was used to identify the supreme strategy

Results
The current policy was the most expensive. Both the current policy and direct DME referral were dominated by starting with OAT. OAT followed by titration was the most cost-effective strategy with an ICER of $42,586.47. The ICER was sensitive to adherence in the direct CPAP strategy and probability of getting CPAP equipment (vs. lost to follow-up).

Conclusion
Starting with OAT therapy in those that were CPAP nonadherent on first attempt is cost-effective. Despite decreased effectiveness, the increase adherence to OAT make it an attractive option for retrial of OSA therapy. If OAT therapy fails, the current policy is more cost-effective than direct CPAP referral.

Support (if any)
This study was supported Career Development Award IK2CX001882 from the United States (U.S.) Department of Veterans Affairs Clinical Sciences Research and Development Service. The contents of this work do not represent the views of the Department of Veterans Affairs or the United States government.
454 Obesity does not modify the effect of 4 months of CPAP on leptin or adiponectin
Authors: Pak Victoria , Maislin David , Keenan Brendan , Townsend Raymond , Benediktsdottir Bryndis , Guo Xiaofeng , Pack Allan , Gislason Thorarinn , Kuna Samuel 
Published: May 2021

Abstract

Introduction
Leptin and adiponectin are cytokines produced by adipocytes. Leptin is involved in the pathogenesis of obesity and adiponectin is cardioprotective. Previous studies in adults with obstructive sleep apnea (OSA) assessing the effect of continuous positive airway pressure (CPAP) on these cytokines are conflicting, and whether obesity modifies the effect remains unknown. We examined the baseline and follow up levels of changes in plasma leptin and adiponectin following 4 months of CPAP treatment among obese (BMI&gt;30) and non-obese (BMI≤30) participants.

Methods
We evaluated 221 adults (84.6% males) in the Penn Icelandic Sleep Apnea (PISA) Study, with mean (±SD) body mass index (BMI) 31.7±4.9 kg/m2 and apnea-hypopnea index (AHI) 35.7±15.6 events/hour. Associations between changes in natural log of the biomarkers in obese and non-obese participants were evaluated, controlling for a priori baseline covariates of age, baseline BMI, race, sex, site, and current smoking status.

Results
The mean proportional change (from baseline to follow-up) in log-transformed adiponectin and leptin in CPAP adherent participants was not significantly different between BMI groups. The baseline to follow up change in leptin post-CPAP was 1.01 (95% CI 0.95–1.08) in obese participants and 1.05 (95% CI 0.96–1.14) in non-obese participants. For adiponectin, the change post-CPAP was 1.04 (95% CI 0.95–1.15) in obese participants and 1.08 (95% CI 0.96–1.22) in non-obese participants.

Conclusion
CPAP treatment did not have an impact on leptin or adiponectin levels. We also find no evidence for obesity modifying the effect of four months of CPAP on leptin or adiponectin.

Support (if any)
1P01-1HL094307
462 Redefining Positive Airway Pressure Adherence Phenotypes Utilizing Deep Neural Networks and Unsupervised Clustering
Authors: Nygate Yoav , Rusk Sam , Fernandez Chris , Glattard Nick , Watson Nathaniel , Hevener William , Beine Bretton , Munafo Dominic 
Published: May 2021

Abstract

Introduction
Improving positive airway pressure (PAP) adherence is crucial to obstructive sleep apnea (OSA) treatment success. We have previously shown the potential of utilizing Deep Neural Network (DNN) models to accurately predict future PAP usage, based on predefined compliance phenotypes, to enable early patient outreach and interventions. These phenotypes were limited, based solely on usage patterns. We propose an unsupervised learning methodology for redefining these adherence phenotypes in order to assist with the creation of more precise and personalized patient categorization.

Methods
We trained a DNN model to predict PAP compliance based on daily usage patterns, where compliance was defined as the requirement for 4 hours of PAP usage a night on over 70% of the recorded nights. The DNN model was trained on N=14,000 patients with 455 days of daily PAP usage data. The latent dimension of the trained DNN model was used as a feature vector containing rich usage pattern information content associated with overall PAP compliance. Along with the 455 days of daily PAP usage data, our dataset included additional patient demographics such as age, sex, apnea-hypopnea index, and BMI. These parameters, along with the extracted usage patterns, were applied together as inputs to an unsupervised clustering algorithm. The clusters that emerged from the algorithm were then used as indicators for new PAP compliance phenotypes.

Results
Two main clusters emerged: highly compliant and highly non-compliant. Furthermore, in the transition between the two main clusters, a sparse cluster of struggling patients emerged. This method allows for the continuous monitoring of patients as they transition from one cluster to the other.

Conclusion
In this research, we have shown that by utilizing historical PAP usage patterns along with additional patient information we can identify PAP specific adherence phenotypes. Clinically, this allows focus of PAP adherence program resources to be targeted early on to patients susceptible to treatment non-adherence. Furthermore, the transition between the two main phenotypes can also indicate when personalized intervention is necessary to maximize treatment success and outcomes. Lastly, providers can transition patients in the highly non-compliant group more quickly to alternative therapies.
Support (if any):