Publications by authors named "Eileen B Leary"

18 Publications

  • Page 1 of 1

Estimation of Apnea-Hypopnea Index using Deep Learning on 3D Craniofacial Scans.

IEEE J Biomed Health Inform 2021 May 7;PP. Epub 2021 May 7.

Obstructive sleep apnea (OSA) is characterized by decreased breathing events that occur through the night, with severity reported as the apnea-hypopnea index (AHI), which is associated with certain craniofacial features. In this study, we used data from 1366 patients collected as part of Stanford Technology Analytics and Genomics in Sleep (STAGES) across 11 US and Canadian sleep clinics and analyzed 3D craniofacial scans with the goal of predicting AHI, as measured using gold standard nocturnal polysomnography (PSG). First, the algorithm detects pre-specified landmarks on mesh objects and aligns scans in 3D space. Subsequently, 2D images and depth maps are generated by rendering and rotating scans by 45-degree increments. Resulting images were stacked as channels and used as input to multi-view convolutional neural networks, which were trained and validated in a supervised manner to predict AHI values derived from PSGs. The proposed model achieved a mean absolute error of 11.38 events/hour, a Pearson correlation coefficient of 0.4, and accuracy for predicting OSA of 67% using 10-fold cross-validation. The model improved further by adding patient demographics and variables from questionnaires. We also show that the model performed at the level of three sleep medicine specialists, who used clinical experience to predict AHI based on 3D scan displays. Finally, we created topographic displays of the most important facial features used by the model to predict AHI, showing importance of the neck and chin area. The proposed algorithm has potential to serve as an inexpensive and efficient screening tool for individuals with suspected OSA.
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http://dx.doi.org/10.1109/JBHI.2021.3078127DOI Listing
May 2021

Living to Dream-Reply.

JAMA Neurol 2021 Apr;78(4):495-496

Stanford University, Palo Alto, California.

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http://dx.doi.org/10.1001/jamaneurol.2021.0056DOI Listing
April 2021

Prediction of Patient Demographics using 3D Craniofacial Scans and Multi-view CNNs.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:1950-1953

3D data is becoming increasingly popular and accessible for computer vision tasks. A popular format for 3D data is the mesh format, which can depict a 3D surface accurately and cost-effectively by connecting points in the (x, y, z) plane, known as vertices, into triangles that can be combined to approximate geometrical surfaces. However, mesh objects are not suitable for standard deep learning techniques due to their non-euclidean structure. We present an algorithm which predicts the sex, age, and body mass index of a subject based on a 3D scan of their face and neck. This algorithm relies on an automatic pre-processing technique, which renders and captures the 3D scan from eight different angles around the x-axis in the form of 2D images and depth maps. Subsequently, the generated data is used to train three convolutional neural networks, each with a ResNet18 architecture, to learn a mapping between the set of 16 images per subject (eight 2D images and eight depth maps from different angles) and their demographics. For age and body mass index, we achieved a mean absolute error of 7.77 years and 4.04 kg/m on the respective test sets, while Pearson correlation coefficients of 0.76 and 0.80 were obtained, respectively. The prediction of sex yielded an accuracy of 93%. The developed framework serves as a proof of concept for prediction of more clinically relevant variables based on 3D craniofacial scans stored in mesh objects.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176333DOI Listing
July 2020

PSG Validation of minute-to-minute scoring for sleep and wake periods in a consumer wearable device.

PLoS One 2020 17;15(9):e0238464. Epub 2020 Sep 17.

Stanford University Center for Sleep Sciences and Medicine, Palo Alto, California, United States of America.

Background: Actigraphs are wrist-worn devices that record tri-axial accelerometry data used clinically and in research studies. The expense of research-grade actigraphs, however, limit their widespread adoption, especially in clinical settings. Tri-axial accelerometer-based consumer wearable devices have gained worldwide popularity and hold potential for a cost-effective alternative. The lack of independent validation of minute-to-minute accelerometer data with polysomnographic data or even research-grade actigraphs, as well as access to raw data has hindered the utility and acceptance of consumer-grade actigraphs.

Methods: Sleep clinic patients wore a consumer-grade wearable (Huami Arc) on their non-dominant wrist while undergoing an overnight polysomnography (PSG) study. The sample was split into two, 20 in a training group and 21 in a testing group. In addition to the Arc, the testing group also wore a research-grade actigraph (Philips Actiwatch Spectrum). Sleep was scored for each 60-s epoch on both devices using the Cole-Kripke algorithm.

Results: Based on analysis of our training group, Arc and PSG data were aligned best when a threshold of 10 units was used to examine the Arc data. Using this threshold value in our testing group, the Arc has an accuracy of 90.3%±4.3%, sleep sensitivity (or wake specificity) of 95.5%±3.5%, and sleep specificity (wake sensitivity) of 55.6%±22.7%. Compared to PSG, Actiwatch has an accuracy of 88.7%±4.5%, sleep sensitivity of 92.6%±5.2%, and sleep specificity of 60.5%±20.2%, comparable to that observed in the Arc.

Conclusions: An optimized sleep/wake threshold value was identified for a consumer-grade wearable Arc trained by PSG data. By applying this sleep/wake threshold value for Arc generated accelerometer data, when compared to PSG, sleep and wake estimates were adequate and comparable to those generated by a clinical-grade actigraph. As with other actigraphs, sleep specificity plateaus due to limitations in distinguishing wake without movement from sleep. Further studies are needed to evaluate the Arc's ability to differentiate between sleep and wake using other sources of data available from the Arc, such as high resolution accelerometry and photoplethysmography.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0238464PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7498244PMC
October 2020

Association of Rapid Eye Movement Sleep With Mortality in Middle-aged and Older Adults.

JAMA Neurol 2020 10;77(10):1241-1251

University of California San Francisco, San Francisco.

Importance: Rapid eye movement (REM) sleep has been linked with health outcomes, but little is known about the relationship between REM sleep and mortality.

Objective: To investigate whether REM sleep is associated with greater risk of mortality in 2 independent cohorts and to explore whether another sleep stage could be driving the findings.

Design, Setting, And Participants: This multicenter population-based cross-sectional study used data from the Outcomes of Sleep Disorders in Older Men (MrOS) Sleep Study and Wisconsin Sleep Cohort (WSC). MrOS participants were recruited from December 2003 to March 2005, and WSC began in 1988. MrOS and WSC participants who had REM sleep and mortality data were included. Analysis began May 2018 and ended December 2019.

Main Outcomes And Measures: All-cause and cause-specific mortality confirmed with death certificates.

Results: The MrOS cohort included 2675 individuals (2675 men [100%]; mean [SD] age, 76.3 [5.5] years) and was followed up for a median (interquartile range) of 12.1 (7.8-13.2) years. The WSC cohort included 1386 individuals (753 men [54.3%]; mean [SD] age, 51.5 [8.5] years) and was followed up for a median (interquartile range) of 20.8 (17.9-22.4) years. MrOS participants had a 13% higher mortality rate for every 5% reduction in REM sleep (percentage REM sleep SD = 6.6%) after adjusting for multiple demographic, sleep, and health covariates (age-adjusted hazard ratio, 1.12; fully adjusted hazard ratio, 1.13; 95% CI, 1.08-1.19). Results were similar for cardiovascular and other causes of death. Possible threshold effects were seen on the Kaplan-Meier curves, particularly for cancer; individuals with less than 15% REM sleep had a higher mortality rate compared with individuals with 15% or more for each mortality outcome with odds ratios ranging from 1.20 to 1.35. Findings were replicated in the WSC cohort despite younger age, inclusion of women, and longer follow-up (hazard ratio, 1.17; 95% CI, 1.03-1.34). A random forest model identified REM sleep as the most important sleep stage associated with survival.

Conclusions And Relevance: Decreased percentage REM sleep was associated with greater risk of all-cause, cardiovascular, and other noncancer-related mortality in 2 independent cohorts.
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http://dx.doi.org/10.1001/jamaneurol.2020.2108DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7550971PMC
October 2020

Proteomic biomarkers of sleep apnea.

Sleep 2020 11;43(11)

Department of Medicine and Clinical Research Core, Weill Cornell Medicine-Qatar, Qatar Foundation-Education City, Doha, Qatar.

Study Objectives: Obstructive sleep apnea (OSA) is characterized by recurrent partial to complete upper airway obstructions during sleep, leading to repetitive arousals and oxygen desaturations. Although many OSA biomarkers have been reported individually, only a small subset have been validated through both cross-sectional and intervention studies. We sought to profile serum protein biomarkers in OSA in unbiased high throughput assay.

Methods: A highly multiplexed aptamer array (SomaScan) was used to profile 1300 proteins in serum samples from 713 individuals in the Stanford Sleep Cohort, a patient-based registry. Outcome measures derived from overnight polysomnography included Obstructive Apnea Hypopnea Index (OAHI), Central Apnea Index (CAI), 2% Oxygen Desaturation index, mean and minimum oxygen saturation indices during sleep. Additionally, a separate intervention-based cohort of 16 individuals was used to assess proteomic profiles pre- and post-intervention with positive airway pressure.

Results: OAHI was associated with 65 proteins, predominantly pathways of complement, coagulation, cytokine signaling, and hemostasis which were upregulated. CAI was associated with two proteins including Roundabout homolog 3 (ROBO3), a protein involved in bilateral synchronization of the pre-Bötzinger complex and cystatin F. Analysis of pre- and post intervention samples revealed IGFBP-3 protein to be increased while LEAP1 (Hepicidin) to be decreased with intervention. An OAHI machine learning classifier (OAHI >=15 vs OAHI<15) trained on SomaScan protein measures alone performed robustly, achieving 76% accuracy in a validation dataset.

Conclusions: Multiplex protein assays offer diagnostic potential and provide new insights into the biological basis of sleep disordered breathing.
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http://dx.doi.org/10.1093/sleep/zsaa086DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7686561PMC
November 2020

Update in Sleep 2019.

Am J Respir Crit Care Med 2020 06;201(12):1473-1479

Sleep Disorders Center, Neurological Institute.

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http://dx.doi.org/10.1164/rccm.202003-0586UPDOI Listing
June 2020

Design of a deep learning model for automatic scoring of periodic and non-periodic leg movements during sleep validated against multiple human experts.

Sleep Med 2020 05 23;69:109-119. Epub 2020 Jan 23.

Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Glostrup, Denmark. Electronic address:

Objective: Currently, manual scoring is the gold standard of leg movement scoring (LMs) and periodic LMs (PLMS) in overnight polysomnography (PSG) studies, which is subject to inter-scorer variability. The objective of this study is to design and validate an end-to-end deep learning system for the automatic scoring of LMs and PLMS in sleep.

Methods: The deep learning system was developed, validated and tested, with respect to manual annotations by expert technicians on 800 overnight PSGs using a leg electromyography channel. The study includes data from three cohorts, namely, the Wisconsin Sleep Cohort (WSC), Stanford Sleep Cohort (SSC) and MrOS Sleep Study. The performance of the system was further compared against individual expert technicians and existing PLM detectors.

Results: The system achieved an F1 score of 0.83, 0.71, and 0.77 for the WSC, SSC, and an ancillary study (Osteoporotic Fractures in Men Study, MrOS) cohorts, respectively. In a total of 60 PSGs from the WSC and the SSC scored by nine expert technicians, the system performed better than two and comparable to seven of the individual scorers with respect to a majority-voting consensus of the remaining scorers. In 60 PSGs from the WSC scored accurately for PLMS, the system outperformed four previous PLM detectors, which were all evaluated on the same data, with an F1 score of 0.85.

Conclusions: The proposed system performs better or comparable to individual expert technicians while outperforming previous automatic detectors. Thereby, the study validates fully automatic methods for scoring LMs in sleep.
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http://dx.doi.org/10.1016/j.sleep.2019.12.032DOI Listing
May 2020

Factors associated with fatigue in patients with insomnia.

J Psychiatr Res 2019 10 28;117:24-30. Epub 2019 Jun 28.

Department of Psychiatry and Behavioral Medicine, Stanford University Center for Sleep Sciences and Medicine, Stanford University, CA, USA.

Although fatigue is common in insomnia, the clinical associates of fatigue in patients with insomnia are largely unknown. We aimed to investigate the clinical associates of fatigue in patients with insomnia. Patients visiting the Stanford Sleep Medicine Center completed the Insomnia Severity Index (ISI), Insomnia Symptom Questionnaire (ISQ), the Fatigue Severity Scale (FSS), the Epworth Sleepiness Scale (ESS), and the Patient Health Questionnaire (PHQ-9). Among 6367 patients, 2024 were diagnosed with insomnia (age 43.06 ± 15.19 years; 1110 women and 914 men) according to the ISI and the ISQ. Insomnia patients with severe fatigue (n = 1306) showed higher insomnia symptoms, daytime sleepiness, depression and longer habitual sleep duration than those without severe fatigue (n = 718). Higher insomnia symptoms, daytime sleepiness and depressive symptoms, and longer habitual sleep duration, independently predicted higher fatigue scores. Among insomnia patients with daytime sleepiness (ESS≥10), only habitual sleep duration and depression predicted fatigue scores. The interaction between insomnia severity and daytime sleepiness significantly predicted the severity of fatigue. Depression was a significant mediator between insomnia and fatigue. For 598 insomnia patients undergoing overnight polysomnography (PSG), no significant correlations were found between fatigue and any PSG parameters. The current study suggests that managing insomnia or depression may reduce the fatigue of insomnia patients, whereas arbitrary efforts to prolong sleep duration may worsen their fatigue.
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http://dx.doi.org/10.1016/j.jpsychires.2019.06.021DOI Listing
October 2019

Non-invasive machine learning estimation of effort differentiates sleep-disordered breathing pathology.

Physiol Meas 2019 02 26;40(2):025008. Epub 2019 Feb 26.

Stanford Center for Sleep Sciences and Medicine, Stanford University, 3165 Porter Drive, MC 5480, Palo Alto, CA 94304-5480, United States of America. Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, University of Copenhagen, Glostrup, Denmark. Biomedical Engineering, Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark.

Objective: Obstructive sleep-disordered breathing (SDB) events, unlike central events, are associated with increased respiratory effort. Esophageal pressure (P ) monitoring is the gold standard for measuring respiratory effort, but it is typically poorly tolerated because of its invasive nature. The objective was to investigate whether machine learning can be applied to routinely collected non-invasive, polysomnography (PSG) measures to accurately model peak negative P .

Approach: One thousand one hundred and nineteen patients from the Stanford Sleep Clinic with PSGs containing P served as the sample. The selected non-invasive PSG signals included nasal pressure, oral airflow, thoracoabdominal effort, and snoring. A long short-term memory neural network was implemented to achieve a context-based mapping between the non-invasive features and the P values. A hold-out dataset served as a prospective validation of the algorithm without needing to undertake a costly new study with the impractically invasive P .

Main Results: The median difference between the measured and predicted P was 0.61 cmHO with an interquartile range (IQR) of 2.99 cmHO and 5th and 95th percentiles of  -5.85 cmHO and 5.47 cmHO, respectively. The model performed well when compared to actual esophageal pressure signal (ρ   =  0.581, p   =  0.01; IQR  =  0.298; ρ   =  0.106; ρ   =  0.843).

Significance: A significant difference in predicted P was shown between normal breathing and all obstructive SDB events; whereas, central apneas did not significantly differ from normal breathing. The developed system may be used as a tool for quantifying respiratory effort from the existing clinical practice of PSG without the need for P , improving characterization of SDB events as obstructive or not.
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http://dx.doi.org/10.1088/1361-6579/ab0559DOI Listing
February 2019

Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy.

Nat Commun 2018 12 6;9(1):5229. Epub 2018 Dec 6.

Center for Sleep Science and Medicine, Stanford University, Stanford, 94304, CA, USA.

Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph-a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.
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http://dx.doi.org/10.1038/s41467-018-07229-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6283836PMC
December 2018

Periodic limb movements in sleep: Prevalence and associated sleepiness in the Wisconsin Sleep Cohort.

Clin Neurophysiol 2018 11 6;129(11):2306-2314. Epub 2018 Sep 6.

Stanford University Center for Sleep Sciences and Medicine, Psychiatry and Behavioral Medicine, Stanford University, CA, USA. Electronic address:

Objectives: Periodic limb movements in sleep (PLMS) are thought to be prevalent in elderly populations, but their impact on quality of life remains unclear. We examined the prevalence of PLMS, impact of age on prevalence, and association between PLMS and sleepiness.

Methods: We identified limb movements in 2335 Wisconsin Sleep Cohort polysomnograms collected over 12 years. Prevalence of periodic limb movement index (PLMI) ≥15 was calculated at baseline (n = 1084). McNemar's test assessed changes in prevalence over time. Association of sleepiness and PLMS evaluated using linear mixed modeling and generalized estimating equations. Models adjusted for confounders.

Results: Prevalence of PLMI ≥15 at baseline was 25.3%. Longitudinal prevalence increased significantly with age (p = 2.97 × 10). Sleepiness did not differ significantly between PLMI groups unless stratified by restless legs syndrome (RLS) symptoms. The RLS+/PLM+ group was sleepier than the RLS+/PLM- group. Multiple Sleep Latency Test trended towards increased alertness in the RLS-/PLM+ group compared to RLS-/PLM-.

Conclusions: A significant number of adults have PLMS and prevalence increased with age. No noteworthy association between PLMI category and sleepiness unless stratified by RLS symptoms.

Significance: Our results indicate that RLS and PLMS may have distinct clinical consequences and interactions that can help guide treatment approach.
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http://dx.doi.org/10.1016/j.clinph.2018.08.022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7750028PMC
November 2018

Sleep disorders, depression and anxiety are associated with adverse safety outcomes in healthcare workers: A prospective cohort study.

J Sleep Res 2018 12 1;27(6):e12722. Epub 2018 Aug 1.

Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts.

The objective of the study was to determine if sleep disorder, depression or anxiety screening status was associated with safety outcomes in a diverse population of hospital workers. A sample of shift workers at four hospitals participated in a prospective cohort study. Participants were screened for five sleep disorders, depression and anxiety at baseline, then completed prospective monthly surveys for the next 6 months to capture motor vehicle crashes, near-miss crashes, occupational exposures and medical errors. We tested the associations between sleep disorders, depression and anxiety and adverse safety outcomes using incidence rate ratios adjusted for potentially confounding factors in a multivariable negative binomial regression model. Of the 416 hospital workers who participated, two in five (40.9%) screened positive for a sleep disorder and 21.6% screened positive for depression or anxiety. After multivariable adjustment, screening positive for a sleep disorder was associated with 83% increased incidence of adverse safety outcomes. Screening positive for depression or anxiety increased the risk by 63%. Sleep disorders and mood disorders were independently associated with adverse outcomes and contributed additively to risk. Our findings suggest that screening for sleep disorders and mental health screening can help identify individuals who are vulnerable to adverse safety outcomes. Future research should evaluate sleep and mental health screening, evaluation and treatment programmes that may improve safety.
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http://dx.doi.org/10.1111/jsr.12722DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6314290PMC
December 2018

Breathing Disturbances Without Hypoxia Are Associated With Objective Sleepiness in Sleep Apnea.

Sleep 2017 11;40(11)

Department of Psychiatry and Behavioral Medicine, Stanford University Center for Sleep Sciences and Medicine, Stanford University, CA.

Study Objectives: To determine whether defining two subtypes of sleep-disordered breathing (SDB) events-with or without hypoxia-results in measures that are more strongly associated with hypertension and sleepiness.

Methods: A total of 1022 participants with 2112 nocturnal polysomnograms from the Wisconsin Sleep Cohort were analyzed with our automated algorithm, developed to detect breathing disturbances and desaturations. Breathing events were time-locked to desaturations, resulting in two indices-desaturating (hypoxia-breathing disturbance index [H-BDI]) and nondesaturating (nonhypoxia-breathing disturbance index [NH-BDI]) events-regardless of arousals. Measures of subjective (Epworth Sleepiness Scale) and objective (2981 multiple sleep latency tests from a subset of 865 participants) sleepiness were analyzed, in addition to clinically relevant clinicodemographic variables. Hypertension was defined as BP ≥ 140/90 or antihypertensive use.

Results: H-BDI, but not NH-BDI, correlated strongly with SDB severity indices that included hypoxia (r ≥ 0.89, p ≤ .001 with 3% oxygen-desaturation index [ODI] and apnea hypopnea index with 4% desaturations). A doubling of desaturation-associated events was associated with hypertension prevalence, which was significant for ODI but not H-BDI (3% ODI OR = 1.06, 95% CI = 1.00-1.12, p < .05; H-BDI OR 1.04, 95% CI = 0.98-1.10) and daytime sleepiness (β = 0.20 Epworth Sleepiness Scale [ESS] score, p < .0001; β = -0.20 minutes in MSL on multiple sleep latency test [MSLT], p < .01). Independently, nondesaturating event doubling was associated with more objective sleepiness (β = -0.52 minutes in MSL on MSLT, p < .001), but had less association with subjective sleepiness (β = 0.12 ESS score, p = .10). In longitudinal analyses, baseline nondesaturating events were associated with worsening of H-BDI over a 4-year follow-up, suggesting evolution in severity.

Conclusions: In SDB, nondesaturating events are independently associated with objective daytime sleepiness, beyond the effect of desaturating events.
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http://dx.doi.org/10.1093/sleep/zsx152DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251661PMC
November 2017

Sleep-stage transitions during polysomnographic recordings as diagnostic features of type 1 narcolepsy.

Sleep Med 2015 Dec 7;16(12):1558-66. Epub 2015 Jul 7.

Stanford Center for Sleep Sciences and Medicine, Stanford University, 3165 Porter Drive, Palo Alto, CA 94304, USA. Electronic address:

Objective: Type 1 narcolepsy/hypocretin deficiency is characterized by excessive daytime sleepiness, sleep fragmentation, and cataplexy. Short rapid eye movement (REM) latency (≤15 min) during nocturnal polysomnography (PSG) or during naps of the multiple sleep latency test (MSLT) defines a sleep-onset REM sleep period (SOREMP), a diagnostic hallmark. We hypothesized that abnormal sleep transitions other than SOREMPs can be identified in type 1 narcolepsy.

Methods: Sleep-stage transitions (one to 10 epochs to one to five epochs of any other stage) and bout length features (one to 10 epochs) were extracted from PSGs. The first 15 min of sleep were excluded when a nocturnal SOREMP was recorded. F(0.1) measures and receiver operating characteristic curves were used to identify specific (≥98%) features. A data set of 136 patients and 510 sex- and age-matched controls was used for the training. A data set of 19 cases and 708 sleep-clinic patients was used for the validation.

Results: (1) ≥5 transitions from ≥5 epochs of stage N1 or W to ≥2 epochs of REM sleep, (2) ≥22 transitions from ≥3 epochs of stage N2 or N3 to ≥2 epochs of N1 or W, and (3) ≥16 bouts of ≥6 epochs of N1 or W were found to be highly specific (≥98%). Sensitivity ranged from 16% to 30%, and it did not vary substantially with and without medication or a nocturnal SOREMP. In patients taking antidepressants, nocturnal SOREMPs occurred much less frequently (16% vs. 36%, p < 0.001).

Conclusions: Increased sleep-stage transitions notably from ≥2.5 min of W/N1 into REM are specifically diagnostic for narcolepsy independent of a nocturnal SOREMP.
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http://dx.doi.org/10.1016/j.sleep.2015.06.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8066516PMC
December 2015

Effects of continuous positive airway pressure on neurocognitive function in obstructive sleep apnea patients: The Apnea Positive Pressure Long-term Efficacy Study (APPLES).

Sleep 2012 Dec 1;35(12):1593-602. Epub 2012 Dec 1.

Stanford University, Stanford, CA, USA.

Study Objective: To determine the neurocognitive effects of continuous positive airway pressure (CPAP) therapy on patients with obstructive sleep apnea (OSA).

Design, Setting, And Participants: The Apnea Positive Pressure Long-term Efficacy Study (APPLES) was a 6-month, randomized, double-blind, 2-arm, sham-controlled, multicenter trial conducted at 5 U.S. university, hospital, or private practices. Of 1,516 participants enrolled, 1,105 were randomized, and 1,098 participants diagnosed with OSA contributed to the analysis of the primary outcome measures.

Intervention: Active or sham CPAP MEASUREMENTS: THREE NEUROCOGNITIVE VARIABLES, EACH REPRESENTING A NEUROCOGNITIVE DOMAIN: Pathfinder Number Test-Total Time (attention and psychomotor function [A/P]), Buschke Selective Reminding Test-Sum Recall (learning and memory [L/M]), and Sustained Working Memory Test-Overall Mid-Day Score (executive and frontal-lobe function [E/F])

Results: The primary neurocognitive analyses showed a difference between groups for only the E/F variable at the 2 month CPAP visit, but no difference at the 6 month CPAP visit or for the A/P or L/M variables at either the 2 or 6 month visits. When stratified by measures of OSA severity (AHI or oxygen saturation parameters), the primary E/F variable and one secondary E/F neurocognitive variable revealed transient differences between study arms for those with the most severe OSA. Participants in the active CPAP group had a significantly greater ability to remain awake whether measured subjectively by the Epworth Sleepiness Scale or objectively by the maintenance of wakefulness test.

Conclusions: CPAP treatment improved both subjectively and objectively measured sleepiness, especially in individuals with severe OSA (AHI > 30). CPAP use resulted in mild, transient improvement in the most sensitive measures of executive and frontal-lobe function for those with severe disease, which suggests the existence of a complex OSA-neurocognitive relationship.

Clinical Trial Information: Registered at clinicaltrials.gov. Identifier: NCT00051363.

Citation: Kushida CA; Nichols DA; Holmes TH; Quan SF; Walsh JK; Gottlieb DJ; Simon RD; Guilleminault C; White DP; Goodwin JL; Schweitzer PK; Leary EB; Hyde PR; Hirshkowitz M; Green S; McEvoy LK; Chan C; Gevins A; Kay GG; Bloch DA; Crabtree T; Demen WC. Effects of continuous positive airway pressure on neurocognitive function in obstructive sleep apnea patients: the Apnea Positive Pressure Long-term Efficacy Study (APPLES). SLEEP 2012;35(12):1593-1602.
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http://dx.doi.org/10.5665/sleep.2226DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3490352PMC
December 2012

The association between obstructive sleep apnea and neurocognitive performance--the Apnea Positive Pressure Long-term Efficacy Study (APPLES).

Sleep 2011 Mar 1;34(3):303-314B. Epub 2011 Mar 1.

Arizona Respiratory Center, University of Arizona, Tucson, AZ, USA.

Study Objectives: To determine associations between obstructive sleep apnea (OSA) and neurocognitive performance in a large cohort of adults.

Study Design: Cross-sectional analyses of polysomnographic and neurocognitive data from 1204 adult participants with a clinical diagnosis of obstructive sleep apnea (OSA) in the Apnea Positive Pressure Long-term Efficacy Study (APPLES), assessed at baseline before randomization to either continuous positive airway pressure (CPAP) or sham CPAP.

Measurements: Sleep and respiratory indices obtained by laboratory polysomnography and several measures of neurocognitive performance.

Results: Weak correlations were found for both the apnea hypopnea index (AHI) and several indices of oxygen desaturation and neurocognitive performance in unadjusted analyses. After adjustment for level of education, ethnicity, and gender, there was no association between the AHI and neurocognitive performance. However, severity of oxygen desaturation was weakly associated with worse neurocognitive performance on some measures of intelligence, attention, and processing speed.

Conclusions: The impact of OSA on neurocognitive performance is small for many individuals with this condition and is most related to the severity of hypoxemia.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041706PMC
http://dx.doi.org/10.1093/sleep/34.3.303DOI Listing
March 2011

The Apnea Positive Pressure Long-term Efficacy Study (APPLES): rationale, design, methods, and procedures.

J Clin Sleep Med 2006 Jul;2(3):288-300

Stanford Universit)y, Stanfbrd, CA, USA.

Study Objective: To assess the size, time course, and durability of the effects of long-term continuous positive airway pressure (CPAP) therapy on neurocognitive function, mood, sleepiness, and quality of life in patients with obstructive sleep apnea.

Design: Randomized, double-blinded, 2-arm, sham-controlled, multicenter, long-term, intention-to-treat trial of CPAP therapy.

Setting: Sleep clinics and laboratories at 5 university medical centers and community-based hospitals.

Patients Or Participants: Target enrollment is 1100 randomly assigned subjects across 5 clinical centers.

Interventions: Active versus sham (subtherapeutic) CPAP.

Measurements And Results: A battery of conventional and novel tests designed to evaluate neurocognitive function, mood, sleepiness, and quality of life.

Conclusions: The Apnea Positive Pressure Long-term Efficacy Study (APPLES) is designed to study obstructive sleep apnea and test the effects of CPAP through a comprehensive, controlled, and long-term trial in a large sample of subjects with obstructive sleep apnea.
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July 2006