Multicentre sleep-stage scoring agreement in the Sleep Revolution project.

Determining sleep stages accurately is an important part of the diagnostic process for numerous sleep disorders. However, as the sleep stage scoring is done manually following visual scoring rules there can be considerable variation in the sleep staging between different scorers. Thus, this study aimed to comprehensively evaluate the inter-rater agreement in sleep staging. A total of 50 polysomnography recordings were manually scored by 10 independent scorers from seven different sleep centres. We used the 10 scorings to calculate a majority score by taking the sleep stage that was the most scored stage for each epoch. The overall agreement for sleep staging was κ = 0.71 and the mean agreement with the majority score was 0.86. The scorers were in perfect agreement in 48% of all scored epochs. The agreement was highest in rapid eye movement sleep (κ = 0.86) and lowest in N1 sleep (κ = 0.41). The agreement with the majority scoring varied between the scorers from 81% to 91%, with large variations between the scorers in sleep stage-specific agreements. Scorers from the same sleep centres had the highest pairwise agreements at κ = 0.79, κ = 0.85, and κ = 0.78, while the lowest pairwise agreement between the scorers was κ = 0.58. We also found a moderate negative correlation between sleep staging agreement and the apnea-hypopnea index, as well as the rate of sleep stage transitions. In conclusion, although the overall agreement was high, several areas of low agreement were also found, mainly between non-rapid eye movement stages.

Breath-holding as a novel approach to risk stratification in COVID-19.

BACKGROUND: Despite considerable progress, it remains unclear why some patients admitted for COVID-19 develop adverse outcomes while others recover spontaneously. Clues may lie with the predisposition to hypoxemia or unexpected absence of dyspnea ('silent hypoxemia') in some patients who later develop respiratory failure. Using a recently-validated breath-holding technique, we sought to test the hypothesis that gas exchange and ventilatory control deficits observed at admission are associated with subsequent adverse COVID-19 outcomes (composite primary outcome: non-invasive ventilatory support, intensive care admission, or death). METHODS: Patients with COVID-19 (N = 50) performed breath-holds to obtain measurements reflecting the predisposition to oxygen desaturation (mean desaturation after 20-s) and reduced chemosensitivity to hypoxic-hypercapnia (including maximal breath-hold duration). Associations with the primary composite outcome were modeled adjusting for baseline oxygen saturation, obesity, sex, age, and prior cardiovascular disease. Healthy controls (N = 23) provided a normative comparison. RESULTS: The adverse composite outcome (observed in N = 11/50) was associated with breath-holding measures at admission (likelihood ratio test, p = 0.020); specifically, greater mean desaturation (12-fold greater odds of adverse composite outcome with 4% compared with 2% desaturation, p = 0.002) and greater maximal breath-holding duration (2.7-fold greater odds per 10-s increase, p = 0.036). COVID-19 patients who did not develop the adverse composite outcome had similar mean desaturation to healthy controls. CONCLUSIONS: Breath-holding offers a novel method to identify patients with high risk of respiratory failure in COVID-19. Greater breath-hold induced desaturation (gas exchange deficit) and greater breath-holding tolerance (ventilatory control deficit) may be independent harbingers of progression to severe disease.

A review of approaches for analysing obstructive sleep apnoea-related patterns in pulse oximetry data.

Overnight pulse oximetry allows the relatively non-invasive estimation of peripheral blood haemoglobin oxygen saturations (SpO2 ), and forms part of the typical polysomnogram (PSG) for investigation of obstructive sleep apnoea (OSA). While the raw SpO2 signal can provide detailed information about OSA-related pathophysiology, this information is typically summarized with simple statistics such as the oxygen desaturation index (ODI, number of desaturations per hour). As such, this study reviews the technical methods for quantifying OSA-related patterns in oximetry data. The technical methods described in literature can be broadly grouped into four categories: (i) Describing the detailed characteristics of desaturations events; (ii) Time series statistics; (iii) Analysis of power spectral distribution (i.e. frequency domain analysis); and (d) Non-linear analysis. These are described and illustrated with examples of oximetry traces. The utilization of these techniques is then described in two applications. First, the application of detailed oximetry analysis allows the accurate automated classification of PSG-defined OSA. Second, quantifications which better characterize the severity of desaturation events are better predictors of OSA-related epidemiological outcomes than standard clinical metrics. Finally, methodological considerations and further applications and opportunities are considered.

The hypoxic burden of sleep apnoea predicts cardiovascular disease-related mortality: the Osteoporotic Fractures in Men Study and the Sleep Heart Health Study.

AIMS: Apnoea-hypopnoea index (AHI), the universal clinical metric of sleep apnoea severity, poorly predicts the adverse outcomes of sleep apnoea, potentially because the AHI, a frequency measure, does not adequately capture disease burden. Therefore, we sought to evaluate whether quantifying the severity of sleep apnoea by the 'hypoxic burden' would predict mortality among adults aged 40 and older. METHODS AND RESULTS: The samples were derived from two cohort studies: The Outcomes of Sleep Disorders in Older Men (MrOS), which included 2743 men, age 76.3 ± 5.5 years; and the Sleep Heart Health Study (SHHS), which included 5111 middle-aged and older adults (52.8% women), age: 63.7 ± 10.9 years. The outcomes were all-cause and Cardiovascular disease (CVD)-related mortality. The hypoxic burden was determined by measuring the respiratory event-associated area under the desaturation curve from pre-event baseline. Cox models were used to calculate the adjusted hazard ratios for hypoxic burden. Unlike the AHI, the hypoxic burden strongly predicted CVD mortality and all-cause mortality (only in MrOS). Individuals in the MrOS study with hypoxic burden in the highest two quintiles had hazard ratios of 1.81 [95% confidence interval (CI) 1.25-2.62] and 2.73 (95% CI 1.71-4.36), respectively. Similarly, the group in the SHHS with hypoxic burden in the highest quintile had a hazard ratio of 1.96 (95% CI 1.11-3.43). CONCLUSION: The 'hypoxic burden', an easily derived signal from overnight sleep study, predicts CVD mortality across populations. The findings suggest that not only the frequency but the depth and duration of sleep related upper airway obstructions, are important disease characterizing features.

Quantifying the magnitude of pharyngeal obstruction during sleep using airflow shape.

RATIONALE AND OBJECTIVES: Non-invasive quantification of the severity of pharyngeal airflow obstruction would enable recognition of obstructive versus central manifestation of sleep apnoea, and identification of symptomatic individuals with severe airflow obstruction despite a low apnoea-hypopnoea index (AHI). Here we provide a novel method that uses simple airflow-versus-time ("shape") features from individual breaths on an overnight sleep study to automatically and non-invasively quantify the severity of airflow obstruction without oesophageal catheterisation. METHODS: 41 individuals with suspected/diagnosed obstructive sleep apnoea (AHI range 0-91 events·h-1) underwent overnight polysomnography with gold-standard measures of airflow (oronasal pneumotach: "flow") and ventilatory drive (calibrated intraoesophageal diaphragm electromyogram: "drive"). Obstruction severity was defined as a continuous variable (flow:drive ratio). Multivariable regression used airflow shape features (inspiratory/expiratory timing, flatness, scooping, fluttering) to estimate flow:drive ratio in 136 264 breaths (performance based on leave-one-patient-out cross-validation). Analysis was repeated using simultaneous nasal pressure recordings in a subset (n=17). RESULTS: Gold-standard obstruction severity (flow:drive ratio) varied widely across individuals independently of AHI. A multivariable model (25 features) estimated obstruction severity breath-by-breath (R2=0.58 versus gold-standard, p<0.00001; mean absolute error 22%) and the median obstruction severity across individual patients (R2=0.69, p<0.00001; error 10%). Similar performance was achieved using nasal pressure. CONCLUSIONS: The severity of pharyngeal obstruction can be quantified non-invasively using readily available airflow shape information. Our work overcomes a major hurdle necessary for the recognition and phenotyping of patients with obstructive sleep disordered breathing.

Phenotyping Pharyngeal Pathophysiology using Polysomnography in Patients with Obstructive Sleep Apnea.

RATIONALE: Therapies for obstructive sleep apnea (OSA) could be administered on the basis of a patient's own phenotypic causes ("traits") if a clinically applicable approach were available. OBJECTIVES: Here we aimed to provide a means to quantify two key contributors to OSA-pharyngeal collapsibility and compensatory muscle responsiveness-that is applicable to diagnostic polysomnography. METHODS: Based on physiological definitions, pharyngeal collapsibility determines the ventilation at normal (eupneic) ventilatory drive during sleep, and pharyngeal compensation determines the rise in ventilation accompanying a rising ventilatory drive. Thus, measuring ventilation and ventilatory drive (e.g., during spontaneous cyclic events) should reveal a patient's phenotypic traits without specialized intervention. We demonstrate this concept in patients with OSA (N = 29), using a novel automated noninvasive method to estimate ventilatory drive (polysomnographic method) and using "gold standard" ventilatory drive (intraesophageal diaphragm EMG) for comparison. Specialized physiological measurements using continuous positive airway pressure manipulation were employed for further comparison. The validity of nasal pressure as a ventilation surrogate was also tested (N = 11). MEASUREMENTS AND MAIN RESULTS: Polysomnography-derived collapsibility and compensation estimates correlated favorably with those quantified using gold standard ventilatory drive (R = 0.83, P < 0.0001; and R = 0.76, P < 0.0001; respectively) and using continuous positive airway pressure manipulation (R = 0.67, P < 0.0001; and R = 0.64, P < 0.001; respectively). Polysomnographic estimates effectively stratified patients into high versus low subgroups (accuracy, 69-86% vs. ventilatory drive measures; P < 0.05). Traits were near-identical using nasal pressure versus pneumotach (N = 11, R ≥ 0.98, both traits; P < 0.001). CONCLUSIONS: Phenotypes of pharyngeal dysfunction in OSA are evident from spontaneous changes in ventilation and ventilatory drive during sleep, enabling noninvasive phenotyping in the clinic. Our approach may facilitate precision therapeutic interventions for OSA.

Identifying obstructive sleep apnoea patients responsive to supplemental oxygen therapy.

A possible precision-medicine approach to treating obstructive sleep apnoea (OSA) involves targeting ventilatory instability (elevated loop gain) using supplemental inspired oxygen in selected patients. Here we test whether elevated loop gain and three key endophenotypic traits (collapsibility, compensation and arousability), quantified using clinical polysomnography, can predict the effect of supplemental oxygen on OSA severity.36 patients (apnoea-hypopnoea index (AHI) >20 events·h-1) completed two overnight polysomnographic studies (single-blinded randomised-controlled crossover) on supplemental oxygen (40% inspired) versus sham (air). OSA traits were quantified from the air-night polysomnography. Responders were defined by a ≥50% reduction in AHI (supine non-rapid eye movement). Secondary outcomes included blood pressure and self-reported sleep quality.Nine of 36 patients (25%) responded to supplemental oxygen (ΔAHI=72±5%). Elevated loop gain was not a significant univariate predictor of responder/non-responder status (primary analysis). In post hoc analysis, a logistic regression model based on elevated loop gain and other traits (better collapsibility and compensation; cross-validated) had 83% accuracy (89% before cross-validation); predicted responders exhibited an improvement in OSA severity (ΔAHI 59±6% versus 12±7% in predicted non-responders, p=0.0001) plus lowered morning blood pressure and "better" self-reported sleep.Patients whose OSA responds to supplemental oxygen can be identified by measuring their endophenotypic traits using diagnostic polysomnography.

Dynamic loop gain increases upon adopting the supine body position during sleep in patients with obstructive sleep apnoea.

BACKGROUND AND OBJECTIVE: Obstructive sleep apnoea (OSA) is typically worse in the supine versus lateral sleeping position. One potential factor driving this observation is a decrease in lung volume in the supine position which is expected by theory to increase a key OSA pathogenic factor: dynamic ventilatory control instability (i.e. loop gain). We aimed to quantify dynamic loop gain in OSA patients in the lateral and supine positions, and to explore the relationship between change in dynamic loop gain and change in lung volume with position. METHODS: Data from 20 patients enrolled in previous studies on the effect of body position on OSA pathogenesis were retrospectively analysed. Dynamic loop gain was calculated from routinely collected polysomnographic signals using a previously validated mathematical model. Lung volumes were measured in the awake state with a nitrogen washout technique. RESULTS: Dynamic loop gain was significantly higher in the supine than in the lateral position (0.77 ± 0.15 vs 0.68 ± 0.14, P = 0.012). Supine functional residual capacity (FRC) was significantly lower than lateral FRC (81.0 ± 15.4% vs 87.3 ± 18.4% of the seated FRC, P = 0.021). The reduced FRC we observed on moving to the supine position was predicted by theory to increase loop gain by 10.2 (0.6, 17.1)%, a value similar to the observed increase of 8.4 (-1.5, 31.0)%. CONCLUSION: Dynamic loop gain increased by a small but statistically significant amount when moving from the lateral to supine position and this may, in part, contribute to the worsening of OSA in the supine sleeping position.

Quantifying the ventilatory control contribution to sleep apnoea using polysomnography.

Elevated loop gain, consequent to hypersensitive ventilatory control, is a primary nonanatomical cause of obstructive sleep apnoea (OSA) but it is not possible to quantify this in the clinic. Here we provide a novel method to estimate loop gain in OSA patients using routine clinical polysomnography alone. We use the concept that spontaneous ventilatory fluctuations due to apnoeas/hypopnoeas (disturbance) result in opposing changes in ventilatory drive (response) as determined by loop gain (response/disturbance). Fitting a simple ventilatory control model (including chemical and arousal contributions to ventilatory drive) to the ventilatory pattern of OSA reveals the underlying loop gain. Following mathematical-model validation, we critically tested our method in patients with OSA by comparison with a standard (continuous positive airway pressure (CPAP) drop method), and by assessing its ability to detect the known reduction in loop gain with oxygen and acetazolamide. Our method quantified loop gain from baseline polysomnography (correlation versus CPAP-estimated loop gain: n=28; r=0.63, p<0.001), detected the known reduction in loop gain with oxygen (n=11; mean±sem change in loop gain (ΔLG) -0.23±0.08, p=0.02) and acetazolamide (n=11; ΔLG -0.20±0.06, p=0.005), and predicted the OSA response to loop gain-lowering therapy. We validated a means to quantify the ventilatory control contribution to OSA pathogenesis using clinical polysomnography, enabling identification of likely responders to therapies targeting ventilatory control.

Increased Flow Limitation During Sleep Is Associated With Increased Psychomotor Vigilance Task Lapses in Individuals With Suspected OSA.

BACKGROUND: Impaired daytime vigilance is an important consequence of OSA, but several studies have reported no association between objective measurements of vigilance and the apnea-hypopnea index (AHI). Notably, the AHI does not quantify the degree of flow limitation, that is, the extent to which ventilation fails to meet intended ventilation (ventilatory drive). RESEARCH QUESTION: Is flow limitation during sleep associated with daytime vigilance in OSA? STUDY DESIGN AND METHODS: Nine hundred ninety-eight participants with suspected OSA completed a 10-min psychomotor vigilance task (PVT) before same-night in-laboratory polysomnography. Flow limitation frequency (percent of flow-limited breaths) during sleep was quantified using airflow shapes (eg, fluttering and scooping) from nasal pressure airflow. Multivariable regression assessed the association between flow limitation frequency and the number of lapses (response times > 500 ms, primary outcome), adjusting for age, sex, BMI, total sleep time, depression, and smoking status. RESULTS: Increased flow limitation frequency was associated with decreased vigilance: a 1-SD (35.3%) increase was associated with 2.1 additional PVT lapses (95% CI, 0.7-3.7; P = .003). This magnitude was similar to that for age, where a 1-SD increase (13.5 years) was associated with 1.9 additional lapses. Results were similar after adjusting for AHI, hypoxemia severity, and arousal severity. The AHI was not associated with PVT lapses (P = .20). In secondary exploratory analysis, flow limitation frequency was associated with mean response speed (P = .012), median response time (P = .029), fastest 10% response time (P = .041), slowest 10% response time (P = .018), and slowest 10% response speed (P = .005). INTERPRETATION: Increased flow limitation during sleep was associated with decreased daytime vigilance in individuals with suspected OSA, independent of the AHI. Flow limitation may complement standard clinical metrics in identifying individuals whose vigilance impairment most likely is explained by OSA.

Sleep stage continuity is associated with objective daytime sleepiness in patients with suspected obstructive sleep apnea.

STUDY OBJECTIVES: Excessive daytime sleepiness (EDS) in patients with obstructive sleep apnea (OSA) is poorly explained by standard clinical sleep architecture metrics. We hypothesized that reduced sleep stage continuity mediates this connection independently from standard sleep architecture metrics. METHODS: 1,907 patients with suspected OSA with daytime sleepiness complaints underwent in-lab diagnostic polysomnography and next-day Multiple Sleep Latency Test (MSLT). Sleep architecture was evaluated with novel sleep-stage continuity quantifications (mean sleep stage duration and probability of remaining in each sleep stage), and conventional metrics (total N1, N2, N3 and REM times; and sleep onset latency). Multivariate analyses were utilized to identify variables associated with moderate EDS (5 ≤ mean daytime sleep latency (MSL) ≤ 10 minutes) and severe EDS (MSL < 5 minutes). RESULTS: Compared to those without EDS, participants with severe EDS had lower N3 sleep continuity (mean N3 period duration 10.4 vs 13.7 minutes, p<0.05), less N3 time (53.8 vs 76.5 minutes, p<0.05); greater total sleep time (374.0 vs 352.5 minutes, p<0.05) and greater N2 time (227.5 vs 186.8 minutes, p<0.05). After adjusting for standard sleep architecture metrics using multivariate logistic regression, decreased mean wake and N3 period duration, and the decreased probability of remaining in N2 and N3 sleep remained significantly associated with severe EDS, while the decreased probability of remaining in wake and N2 sleep were associated with moderate EDS. CONCLUSIONS: Patients with OSA with EDS experience lower sleep continuity, noticeable especially during N3 sleep and wake. Sleep-stage continuity quantifications assist in characterizing the sleep architecture and are associated with objective daytime sleepiness highlighting the need for more detailed evaluations of sleep quality.

Flow Limitation Is Associated with Excessive Daytime Sleepiness in Individuals without Moderate or Severe Obstructive Sleep Apnea.

Rationale: Moderate-Severe Obstructive Sleep Apnea (OSA, AHI>15) disturbs sleep through frequent bouts of apnea and is associated with daytime sleepiness. However, many individuals without moderate-severe OSA (i.e., AHI<15) also report sleepiness. Objective: To test the hypothesis that sleepiness in the AHI<15 group is a consequence of substantial flow limitation, in the absence of overt reductions in airflow (i.e., apnea/hypopnea). Methods: N=1886 participants from the MESA sleep cohort were analyzed for frequency of flow limitation from polysomnogram recorded nasal airflow signal. Excessive daytime sleepiness (EDS) was defined by Epworth Sleepiness Scale ≥11. Covariate-adjusted logistic regression assessed the association between EDS (binary dependent variable) and frequency of flow limitation (continuous) in individuals with an AHI<15. Results: N=772 individuals with an AHI<15 were included in primary analysis. Flow limitation was associated with EDS (odds ratio of 2.04, CI95% [1.17-3.54], per 2 standard deviation (2SD) increase in flow limitation frequency) after adjusting for age, sex, BMI, race/ethnicity, and sleep duration. This effect size did not appreciably change after additionally adjusting for AHI. Conclusions: In individuals with an AHI<15, increasing flow limitation frequency by 2SD is associated with a 2-fold increase in risk of EDS. Future studies should investigate addressing flow limitation in low AHI individuals as a potential mechanism for ameliorating sleepiness.

Review and perspective on sleep-disordered breathing research and translation to clinics.

Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of sleep disorders, sleep research and diagnostic practices still rely on outdated metrics and laborious methods reducing the diagnostic capacity and preventing timely diagnosis and treatment. Consequently, a significant portion of individuals affected by sleep-disordered breathing remain undiagnosed or are misdiagnosed. Taking advantage of state-of-the-art scientific, technological, and computational advances could be an effective way to optimize the diagnostic and treatment pathways. We discuss state-of-the-art multidisciplinary research, review the shortcomings in the current practices of SDB diagnosis and management in adult populations, and provide possible future directions. We critically review the opportunities for modern data analysis methods and machine learning to combine multimodal information, provide a perspective on the pitfalls of big data analysis, and discuss approaches for developing analysis strategies that overcome current limitations. We argue that large-scale and multidisciplinary collaborative efforts based on clinical, scientific, and technical knowledge and rigorous clinical validation and implementation of the outcomes in practice are needed to move the research of sleep-disordered breathing forward, thus increasing the quality of diagnostics and treatment.

Nasal Pressure Derived Airflow Limitation and Ventilation Measurements are Resilient to Reduced Signal Quality.

Obstructive sleep apnea is a disorder characterized by partial or complete airway obstructions during sleep. Our previously published algorithms use the minimally invasive nasal pressure signal routinely collected during diagnostic polysomnography (PSG) to segment breaths and estimate airflow limitation (using flow:drive) and minute ventilation for each breath. The first aim of this study was to investigate the effect of airflow signal quality on these algorithms, which can be influenced by oronasal breathing and signal-to-noise ratio (SNR). It was hypothesized that these algorithms would make inaccurate estimates when the expiratory portion of breaths is attenuated to simulate oronasal breathing, and pink noise is added to the airflow signal to reduce SNR. At maximum SNR and 0% expiratory amplitude, the average error was 2.7% for flow:drive, -0.5% eupnea for ventilation, and 19.7 milliseconds for breath duration (n = 257,131 breaths). At 20 dB and 0% expiratory amplitude, the average error was -15.1% for flow:drive, 0.1% eupnea for ventilation, and 28.4 milliseconds for breath duration (n = 247,160 breaths). Unexpectedly, simulated oronasal breathing had a negligible effect on flow:drive, ventilation, and breath segmentation algorithms across all SNRs. Airflow SNR ≥ 20 dB had a negligible effect on ventilation and breath segmentation, whereas airflow SNR ≥ 30 dB had a negligible effect on flow:drive. The second aim of this study was to explore the possibility of correcting these algorithms to compensate for airflow signal asymmetry and low SNR. An offset based on estimated SNR applied to individual breath flow:drive estimates reduced the average error to ≤ 1.3% across all SNRs at patient and breath levels, thereby facilitating for flow:drive to be more accurately estimated from PSGs with low airflow SNR.Clinical Relevance- This study demonstrates that our airflow limitation, ventilation, and breath segmentation algorithms are robust to reduced airflow signal quality.

Pathophysiology Underlying Demographic and Obesity Determinants of Sleep Apnea Severity.

Rationale: Sleep apnea is the manifestation of key endotypic traits, including greater pharyngeal collapsibility, reduced dilator muscle compensation, and elevated chemoreflex loop gain. Objectives: We investigated how endotypic traits vary with obesity, age, sex, and race/ethnicity to influence sleep apnea disease severity (apnea-hypopnea index [AHI]). Methods: Endotypic traits were estimated from polysomnography in a diverse community-based cohort study (Multi-Ethnic Study of Atherosclerosis, N = 1,971; age range, 54-93 yr). Regression models assessed associations between each exposure (continuous variables per 2 standard deviations [SDs]) and endotypic traits (per SD) or AHI (events/h), independent of other exposures. Generalizability was assessed in two independent cohorts. Results: Greater AHI was associated with obesity (+19 events/h per 11 kg/m2 [2 SD]), male sex (+13 events/h vs. female), older age (+7 events/h per 20 yr), and Chinese ancestry (+5 events/h vs. White, obesity adjusted). Obesity-related increase in AHI was best explained by elevated collapsibility (+0.40 SD) and greater loop gain (+0.38 SD; percentage mediated, 26% [95% confidence interval (CI), 20-32%]). Male-related increase in AHI was explained by elevated collapsibility (+0.86 SD) and reduced compensation (-0.40 SD; percentage mediated, 57% [95% CI, 50-66%]). Age-related AHI increase was explained by elevated collapsibility (+0.37 SD) and greater loop gain (+0.15 SD; percentage mediated, 48% [95% CI, 34-63%]). Increased AHI with Chinese ancestry was explained by collapsibility (+0.57 SD; percentage mediated, 87% [95% CI, 57-100]). Black race was associated with reduced collapsibility (-0.30 SD) and elevated loop gain (+0.29 SD). Similar patterns were observed in the other cohorts. Conclusions: Different subgroups exhibit different underlying pathophysiological pathways to sleep apnea, highlighting the variability in mechanisms that could be targeted for intervention.

Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls.

Introduction: Visual sleep scoring has several shortcomings, including inter-scorer inconsistency, which may adversely affect diagnostic decision-making. Although automatic sleep staging in adults has been extensively studied, it is uncertain whether such sophisticated algorithms generalize well to different pediatric age groups due to distinctive EEG characteristics. The preadolescent age group (10-13-year-olds) is relatively understudied, and thus, we aimed to develop an automatic deep learning-based sleep stage classifier specifically targeting this cohort. Methods: A dataset (n = 115) containing polysomnographic recordings of Icelandic preadolescent children with sleep-disordered breathing (SDB) symptoms, and age and sex-matched controls was utilized. We developed a combined convolutional and long short-term memory neural network architecture relying on electroencephalography (F4-M1), electrooculography (E1-M2), and chin electromyography signals. Performance relative to human scoring was further evaluated by analyzing intra- and inter-rater agreements in a subset (n = 10) of data with repeat scoring from two manual scorers. Results: The deep learning-based model achieved an overall cross-validated accuracy of 84.1% (Cohen's kappa κ = 0.78). There was no meaningful performance difference between SDB-symptomatic (n = 53) and control subgroups (n = 52) [83.9% (κ = 0.78) vs. 84.2% (κ = 0.78)]. The inter-rater reliability between manual scorers was 84.6% (κ = 0.78), and the automatic method reached similar agreements with scorers, 83.4% (κ = 0.76) and 82.7% (κ = 0.75). Conclusion: The developed algorithm achieved high classification accuracy and substantial agreements with two manual scorers; the performance metrics compared favorably with typical inter-rater reliability between manual scorers and performance reported in previous studies. These suggest that our algorithm may facilitate less labor-intensive and reliable automatic sleep scoring in preadolescent children.

Frequency of flow limitation using airflow shape.

STUDY OBJECTIVES: The presence of flow limitation during sleep is associated with adverse health consequences independent of obstructive sleep apnea (OSA) severity (apnea-hypopnea index, AHI), but remains extremely challenging to quantify. Here we present a unique library and an accompanying automated method that we apply to investigate flow limitation during sleep. METHODS: A library of 117,871 breaths (N = 40 participants) were visually classified (certain flow limitation, possible flow limitation, normal) using airflow shape and physiological signals (ventilatory drive per intra-esophageal diaphragm EMG). An ordinal regression model was developed to quantify flow limitation certainty using flow-shape features (e.g. flattening, scooping); breath-by-breath agreement (Cohen's ƙ); and overnight flow limitation frequency (R2, %breaths in certain or possible categories during sleep) were compared against visual scoring. Subsequent application examined flow limitation frequency during arousals and stable breathing, and associations with ventilatory drive. RESULTS: The model (23 features) assessed flow limitation with good agreement (breath-by-breath ƙ = 0.572, p < 0.001) and minimal error (overnight flow limitation frequency R2 = 0.86, error = 7.2%). Flow limitation frequency was largely independent of AHI (R2 = 0.16) and varied widely within individuals with OSA (74[32-95]%breaths, mean[range], AHI > 15/h, N = 22). Flow limitation was unexpectedly frequent but variable during arousals (40[5-85]%breaths) and stable breathing (58[12-91]%breaths), and was associated with elevated ventilatory drive (R2 = 0.26-0.29; R2 < 0.01 AHI v. drive). CONCLUSIONS: Our method enables quantification of flow limitation frequency, a key aspect of obstructive sleep-disordered breathing that is independent of the AHI and often unavailable. Flow limitation frequency varies widely between individuals, is prevalent during arousals and stable breathing, and reveals elevated ventilatory drive. Clinical trial registration: The current observational physiology study does not qualify as a clinical trial.

Assessing the Physiologic Endotypes Responsible for REM- and NREM-Based OSA.

BACKGROUND: Patients with OSA can have the majority of their respiratory events in rapid eye movement (REM) sleep or in non-rapid eye movement (NREM) sleep. No previous studies have linked the different physiologic conditions in REM and NREM sleep to the common polysomnographic patterns seen in everyday clinical practice, namely REM predominant OSA (REMOSA) and NREM predominant OSA (NREMOSA). RESEARCH QUESTION: (1) How does OSA physiologic condition change with sleep stage in patients with NREMOSA and REMOSA? (2) Do patients with NREMOSA and REMOSA have different underlying OSA pathophysiologic conditions? STUDY DESIGN AND METHODS: We recruited patients with three polysomnographic patterns. (1) REMOSA: twice as many respiratory events in REM sleep, (2) NREMOSA: twice as many events in NREM sleep, and (3) uniform OSA: equal number of events in NREM/REM sleep. We deployed a noninvasive phenotyping method to determine OSA endotype traits (Vpassive, Vactive, loop gain, arousal threshold) in NREM sleep, REM sleep, and total night sleep in each group of patients (NREMOSA, REMOSA, uniform OSA). RESULTS: Patients with NREMOSA have significantly worse ventilatory control stability in NREM sleep compared with REM sleep (loop gain, 0.546 [0.456,0.717] in NREM vs 0.365 [0.238,0.459] in REM sleep; P = .0026). Patients with REMOSA displayed a significantly more collapsible airway (ie, lower Vpassive) in REM compared with NREM sleep (98.4 [97.3,99.2] %Veupnea in NREM vs 95.9 [86.4,98.9] %Veupnea in REM sleep; P < .0001). The major between-group difference across the whole night was a significantly higher loop gain in the NREMOSA group (0.561 [0.429,0.675]) compared with the REMOSA group (0.459 [0.388,0.539]; P = .0033). INTERPRETATION: This study is the first to link long-recognized polysomnographic patterns of OSA to underlying physiologic differences. Patients with NREMOSA have a higher loop gain in NREM sleep; patients with REMOSA have a worsening of Vpassive in REM sleep.

Effect of Hypopnea Scoring Criteria on Noninvasive Assessment of Loop Gain and Surgical Outcome Prediction.

Rationale: Unstable ventilatory control (high loop gain) is a causal factor in the development of obstructive sleep apnea. Methods for quantifying loop gain using polysomnography have been developed that predict favorable responses to upper airway surgery. However, this method is reliant on respiratory event scoring and hence may be affected by hypopnea scoring criteria.Objectives: To determine to what extent differences in hypopnea scoring influence loop gain measurement.Methods: We performed a retrospective analysis of 46 polysomnograms before and after upper airway surgery. Polysomnograms were rescored according to three different American Academy of Sleep Medicine hypopnea definitions (2007Alternative, 2012Recommended, and 2012Acceptable criteria). Loop gain and apnea-hypopnea indexes (AHIs) were compared between criteria using linear regression and Bland-Altman limits of agreement (LOA). Responders to surgery were classified by a 50% or greater reduction in AHI and AHIpostsurgery less than 10 events per hour. Responders were determined separately for each American Academy of Sleep Medicine criterion. Receiver operating characteristic curve analysis predicting surgical outcome was performed for each loop gain measurement derived from each criterion.Results: A near-perfect agreement was found between loop gains derived using the 2007Alternative and 2012Recommended criteria (r2 = 0.99; bias = -0.003; LOA, -0.016 to 0.010). Greater variability was found for 2012Acceptable compared to the 2007Alternative (r2 = 0.70; bias = -0.015; LOA, -0.099 to 0.070) and 2012Recommended (r2 = 0.69; bias = +0.018; LOA, -0.068 to 0.104) criteria. Both 2007Alternative and 2012Recommended loop gains significantly predicted surgical response with similar areas under the curve (AUCs; 2007Alternative AUC = 0.86 [95% confidence interval (CI), 0.75-0.97]; 2012Recommended AUC = 0.84 [95% CI, 0.71-0.97]). 2012Acceptable loop gains were a poor predictor of surgical response (AUC = 0.62 [95% CI, 0.43-0.80]).Conclusions: Loop gain measured noninvasively by polysomnography can be influenced by respiratory event scoring. We recommend caution when using the 2012Acceptable criteria with this method, because such findings may not be directly generalizable to other loop gain values derived from other scoring criteria.