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.

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.

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.

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 (R 2, %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 R 2=0.86, error=7.2%). Flow limitation frequency was largely independent of AHI (R 2=0.16) and varied widely within individuals with OSA (74[32-95]%breaths, mean[range], AHI>15/hr, 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 (R 2=0.26-0.29; R 2<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.

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.

Assessing ventilatory instability using the response to spontaneous sighs during sleep in preterm infants

Study Objectives

Periodic breathing (PB) is common in newborns and is an obvious manifestation of ventilatory control instability. However, many infants without PB may still have important underlying ventilatory control instabilities that go unnoticed using standard clinical monitoring. Methods to detect infants with "subclinical" ventilatory control instability are therefore required. The current study aimed to assess the degree of ventilatory control instability using simple bedside recordings in preterm infants.

Methods

Respiratory inductance plethysmography (RIP) recordings were analyzed from ~20 minutes of quiet sleep in 20 preterm infants at 36 weeks post-menstrual age (median [range]: 36 [34-40]). The percentage time spent in PB was also calculated for each infant (%PB). Spontaneous sighs were identified and breath-by-breath measurements of (uncalibrated) ventilation were derived from RIP traces. Loop gain (LG, a measure of ventilatory control instability) was calculated by fitting a simple ventilatory control model (gain, time-constant, delay) to the post-sigh ventilatory pattern. For comparison, periodic inter-breath variability was also quantified using power spectral analysis (ventilatory oscillation magnitude index [VOMI]).

Results

%PB was strongly associated with LG (r2 = 0.77, p < 0.001) and moderately with the VOMI (r2 = 0.21, p = 0.047). LG (0.52 ± 0.05 vs. 0.30 ± 0.03; p = 0.0025) and the VOMI (-8.2 ± 1.1 dB vs. -11.8 ± 0.9 dB; p = 0.026) were both significantly higher in infants that displayed PB vs. those without.

Conclusions

LG and VOMI determined from the ventilatory responses to spontaneous sighs can provide a practical approach to assessing ventilatory control instability in preterm infants. Such simple techniques may help identify infants at particular risk for ventilatory instabilities with concomitant hypoxemia and its associated consequences.

Quantifying the Arousal Threshold Using Polysomnography in Obstructive Sleep Apnea

Study Objectives

Precision medicine for obstructive sleep apnea (OSA) requires noninvasive estimates of each patient's pathophysiological "traits." Here, we provide the first automated technique to quantify the respiratory arousal threshold-defined as the level of ventilatory drive triggering arousal from sleep-using diagnostic polysomnographic signals in patients with OSA.

Methods

Ventilatory drive preceding clinically scored arousals was estimated from polysomnographic studies by fitting a respiratory control model (Terrill et al.) to the pattern of ventilation during spontaneous respiratory events. Conceptually, the magnitude of the airflow signal immediately after arousal onset reveals information on the underlying ventilatory drive that triggered the arousal. Polysomnographic arousal threshold measures were compared with gold standard values taken from esophageal pressure and intraoesophageal diaphragm electromyography recorded simultaneously (N = 29). Comparisons were also made to arousal threshold measures using continuous positive airway pressure (CPAP) dial-downs (N = 28). The validity of using (linearized) nasal pressure rather than pneumotachograph ventilation was also assessed (N = 11).

Results

Polysomnographic arousal threshold values were correlated with those measured using esophageal pressure and diaphragm EMG (R = 0.79, p < .0001; R = 0.73, p = .0001), as well as CPAP manipulation (R = 0.73, p < .0001). Arousal threshold estimates were similar using nasal pressure and pneumotachograph ventilation (R = 0.96, p < .0001).

Conclusions

The arousal threshold in patients with OSA can be estimated using polysomnographic signals and may enable more personalized therapeutic interventions for patients with a low arousal threshold.

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 obstructiveversuscentral 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=0.58versusgold-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.

Ventilatory control sensitivity in patients with obstructive sleep apnea is sleep stage dependent

Study Objectives

The severity of obstructive sleep apnea (OSA) is known to vary according to sleep stage; however, the pathophysiology responsible for this robust observation is incompletely understood. The objective of the present work was to examine how ventilatory control system sensitivity (i.e. loop gain) varies during sleep in patients with OSA.

Methods

Loop gain was estimated using signals collected from standard diagnostic polysomnographic recordings performed in 44 patients with OSA. Loop gain measurements associated with nonrapid eye movement (NREM) stage 2 (N2), stage 3 (N3), and REM sleep were calculated and compared. The sleep period was also split into three equal duration tertiles to investigate how loop gain changes over the course of sleep.

Results

Loop gain was significantly lower (i.e. ventilatory control more stable) in REM (Mean ± SEM: 0.51 ± 0.04) compared with N2 sleep (0.63 ± 0.04; p = 0.001). Differences in loop gain between REM and N3 (p = 0.095), and N2 and N3 (p = 0.247) sleep were not significant. Furthermore, N2 loop gain was significantly lower in the first third (0.57 ± 0.03) of the sleep period compared with later second (0.64 ± 0.03, p = 0.012) and third (0.64 ± 0.03, p = 0.015) tertiles. REM loop gain also tended to increase across the night; however, this trend was not statistically significant [F(2, 12) = 3.49, p = 0.09].

Conclusions

These data suggest that loop gain varies between REM and NREM sleep and modestly increases over the course of sleep. Lower loop gain in REM is unlikely to contribute to the worsened OSA severity typically observed in REM sleep, but may explain the reduced propensity for central sleep apnea in this sleep stage.

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.

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.

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.

A graphical method for comparing nocturnal oxygen saturation profiles in individuals and populations: Application to healthy infants and preterm neonates

STUDY OBJECTIVES

Pulse-oximetry (SpO₂ ) allows the identification of important clinical physiology. However, summary statistics such as mean values and desaturation incidence do not capture the complexity of the information contained within continuous recordings. The aim of this study was to develop an objective method to quantify important SpO₂ characteristics; and assess its utility in healthy infant and preterm neonate cohorts.

METHODS

An algorithm was developed to calculate the desaturation incidence, depth, and duration. These variables are presented using three plots: SpO₂ cumulative-frequency relationship; desaturation-depth versus incidence; desaturation-duration versus incidence. This method was applied to two populations who underwent nocturnal pulse-oximetry: (1) thirty-four healthy term infants studied at 2-weeks, 3, 6, 12, and 24-months of age and (2) thirty-seven neonates born <26 weeks and studied at discharge from NICU (37-44 weeks post-conceptual age).

RESULTS

The maturation in healthy infants was characterized by reduced desaturation index (27.2/h vs 3.3/h at 2-weeks and 24-months, P < 0.01), and increased percentage of desaturation events ≥6-s in duration (27.8% vs 43.2% at 2-weeks and 3-months, P < 0.01). Compared with term-infants, preterm infants had a greater desaturation incidence (54.8/h vs 27.2/h, P < 0.01), and these desaturations were deeper (52.9% vs 37.6% were ≥6% below baseline, P < 0.01). The incidence of longer desaturations (≥14-s) in preterm infants was correlated with healthcare utilization over the first 24-months (r = 0.63, P < 0.01).

CONCLUSIONS

This tool allows the objective comparison of extended oximetry recordings between groups and for individuals; and serves as a basis for the development of reference ranges for populations.

Loop Gain Predicts the Response to Upper Airway Surgery in Patients With Obstructive Sleep Apnea

Study Objectives

Upper airway surgery is often recommended to treat patients with obstructive sleep apnea (OSA) who cannot tolerate continuous positive airways pressure. However, the response to surgery is variable, potentially because it does not improve the nonanatomical factors (ie, loop gain [LG] and arousal threshold) causing OSA. Measuring these traits clinically might predict responses to surgery. Our primary objective was to test the value of LG and arousal threshold to predict surgical success defined as 50% reduction in apnea-hypopnea index (AHI) and AHI <10 events/hour post surgery.

Methods

We retrospectively analyzed data from patients who underwent upper airway surgery for OSA (n = 46). Clinical estimates of LG and arousal threshold were calculated from routine polysomnographic recordings presurgery and postsurgery (median of 124 [91-170] days follow-up).

Results

Surgery reduced both the AHI (39.1 ± 4.2 vs. 26.5 ± 3.6 events/hour; p < .005) and estimated arousal threshold (-14.8 [-22.9 to -10.2] vs. -9.4 [-14.5 to -6.0] cmH2O) but did not alter LG (0.45 ± 0.08 vs. 0.45 ± 0.12; p = .278). Responders to surgery had a lower baseline LG (0.38 ± 0.02 vs. 0.48 ± 0.01, p < .05) and were younger (31.0 [27.3-42.5] vs. 43.0 [33.0-55.3] years, p < .05) than nonresponders. Lower LG remained a significant predictor of surgical success after controlling for covariates (logistic regression p = .018; receiver operating characteristic area under curve = 0.80).

Conclusions

Our study provides proof-of-principle that upper airway surgery most effectively resolves OSA in patients with lower LG. Predicting the failure of surgical treatment, consequent to less stable ventilatory control (elevated LG), can be achieved in the clinic and may facilitate avoidance of surgical failures.

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.

Temporal associations between arousal and body/limb movement in children with suspected obstructed sleep apnoea

The inter-relationship between arousal events and body and/or limb movements during sleep may significantly impact the performance and clinical interpretation of actigraphy. As such, the objective of this study was to quantify the temporal association between arousals and body/limb movement. From this, we aim to determine whether actigraphy can predict arousal events in children, and identify the impact of arousal-related movements on estimates of sleep/wake periods. Thirty otherwise healthy children (5-16 years, median 9 years, 21 male) with suspected sleep apnoea were studied using full polysomnography and customised raw tri-axial accelerometry measured at the left fingertip, left wrist, upper thorax, left ankle and left great toe. Raw data were synchronised to within 0.1 s of the polysomnogram. Movements were then identified using a custom algorithm. On average 67.5% of arousals were associated with wrist movement. Arousals associated with movement were longer than those without movement (mean duration: 12.2 s versus 7.9 s respectively, p  <  0.01); movements during wake and arousal were longer than other sleep movements (wrist duration: 6.26 s and 9.89 s versus 2.35 s respectively, p  <  0.01); and the movement index (movements/h) did not predict apnoea-hypopnoea index (ρ  =  -0.11). Movements associated with arousals are likely to unavoidably contribute to actigraphy's poor sensitivity for wake. However, as sleep-related movements tend to be shorter than those during wake or arousal, incorporating movement duration into the actigraphy scoring algorithm may improve sleep staging performance. Although actigraphy-based measurements cannot reliably predict all arousal events, actigraphy can likely identify longer events that may have the greatest impact on sleep quality.

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.

Characterization of movements during restless sleep in children: a pilot study

Actigraphy is effective at monitoring circadian rhythms, but often misidentifies periods of restless sleep (defined here as sleep periods with movement) as wake, and periods of quiet wake as sleep. This limitation restricts the effectiveness of actigraphy for investigating sleep disorders. Our objective in this study was to investigate a time-frequency representation of movement during sleep and wake which could ultimately aid in improving classification performance by reducing false wake detections. As a pilot study, we investigate the characteristics of manually labelled movements from six patients (aged 6-12 years, 3 male) during sleep and wake using the over complete discrete wavelet decomposition. The difference between the median wavelet coefficients were analyzed for 30 movement segments from six movement categories during sleep and wake. We found that, in general, the temporal location of high energy coefficients and the energy of the high frequency bands differed between movements during sleep and wake. This indicates that we are able to differentiate movement during sleep and wake with a time-frequency representation. This representation may improve the sleep and wake classification performance by identifying movements specific to sleep and wake. This will likely improve the poor specificity inherent in conventional actigraphy.

Measuring leg movements during sleep using accelerometry: comparison with EMG and piezo-electric scored events

Periodic Limb Movements during Sleep (PLMS) can cause significant disturbance to sleep, resulting in daytime sleepiness and reduced quality of life. In conventional clinical practice, PLMS are measured using overnight electromyogram (EMG) of the tibialis anterior muscle, although historically they have also been measured using piezo-electric gauges placed over the muscle. However, PLMS counts (PLM index) do not correlate well with clinical symptomology. In this study, we propose that because EMG and piezo derived signals measure muscle activation rather than actual movement, they may count events with no appreciable movement of the limb and therefore no contribution to sleep disturbance. The aim of this study is thus to determine the percentage of clinically scored limb movements which are not associated with movement of the great toe measured using accelerometry. 9 participants were studied simultaneously with an overnight diagnostic polysomnogram (including EMG and piezo instrumentation of the right leg) and high temporal resolution accelerometry of the right great toe. Limb movements were scored, and peak acceleration during each scored movement was quantified. Across the participant population, 54.9% (range: 26.7-76.3) and 39.0% (range: 4.8-69.6) of limb movements scored using piezo and EMG instrumentation respectively, were not associated with toe movement measured with accelerometry. If sleep disturbance is the consequence of the limb movements, these results may explain why conventional piezo or EMG derived PLMI is poorly correlated with clinical symptomology.

Characterising non-linear dynamics in nocturnal breathing patterns of healthy infants using recurrence quantification analysis

Breathing dynamics vary between infant sleep states, and are likely to exhibit non-linear behaviour. This study applied the non-linear analytical tool recurrence quantification analysis (RQA) to 400 breath interval periods of REM and N-REM sleep, and then using an overlapping moving window. The RQA variables were different between sleep states, with REM radius 150% greater than N-REM radius, and REM laminarity 79% greater than N-REM laminarity. RQA allowed the observation of temporal variations in non-linear breathing dynamics across a night's sleep at 30s resolution, and provides a basis for quantifying changes in complex breathing dynamics with physiology and pathology.

Application of recurrence quantification analysis to automatically estimate infant sleep states using a single channel of respiratory data

Previous work has identified that non-linear variables calculated from respiratory data vary between sleep states, and that variables derived from the non-linear analytical tool recurrence quantification analysis (RQA) are accurate infant sleep state discriminators. This study aims to apply these discriminators to automatically classify 30 s epochs of infant sleep as REM, non-REM and wake. Polysomnograms were obtained from 25 healthy infants at 2 weeks, 3, 6 and 12 months of age, and manually sleep staged as wake, REM and non-REM. Inter-breath interval data were extracted from the respiratory inductive plethysmograph, and RQA applied to calculate radius, determinism and laminarity. Time-series statistic and spectral analysis variables were also calculated. A nested cross-validation method was used to identify the optimal feature subset, and to train and evaluate a linear discriminant analysis-based classifier. The RQA features radius and laminarity and were reliably selected. Mean agreement was 79.7, 84.9, 84.0 and 79.2 % at 2 weeks, 3, 6 and 12 months, and the classifier performed better than a comparison classifier not including RQA variables. The performance of this sleep-staging tool compares favourably with inter-human agreement rates, and improves upon previous systems using only respiratory data. Applications include diagnostic screening and population-based sleep research.

Development of a continuous multisite accelerometry system for studying movements during sleep

Actigraphy has proven to be a useful tool in the assessment of circadian rhythms, and more recently in the automatic staging of sleep and wake states. Whilst accuracy of commercial systems appears good over 24 hour periods, the sensitivity of detecting wake during time in bed is poor. One possible explanation for these poor results is the technical limitations of currently available commercial actigraphs. In particular, raw data is generally not available to the user. Instead, activity counts for each epoch (typically between 10-60 secs) are calculated using various algorithms, from which sleep state is identified. Consequently morphologically different movements observed during sleep and wake states may not be detected as such. In this paper, the development of a continuous multisite, accelerometry system (CMAS) is described. Initial results, comparing data collected using a commercial actigraph (Actiwatch- Mini Motionlogger), and the continuous multisite accelerometry system are presented. The CMAS is able to differentiate brief movement "twitches" from postural changes.

Pediatric obstructive sleep apnea assessment using pulse oximetry and dual RIP bands

The diagnosis of Obstructive Sleep Apnea (OSA) in children presents a challenging diagnostic problem given the high prevalence (2-3%), the resource intensity of the overnight polysomnography investigation, and the realisation that OSA poses a serious threat to the healthy growth and development of children. Previous attempts to develop OSA diagnostic systems using home pulse oximetry studies have failed to meet the accuracy requirements - particularly the low false normal rate (FNR) - required for a pre-PSG screening test. Thus the aim of this study is to investigate the feasibility of an OSA severity diagnostic system based on both oximetry and dual respiratory inductance plethysmography (RIP) bands. A total of 90 PSG studies (30 each of normal, mild/moderate and severe OSA) were retrospectively analyzed. Quantifications of oxygen desaturations (S), respiratory events (E) and heart rate arousals (A) were calculated and extracted and an empirical rule-based SEA classifier model for normal, mild/moderate and severe OSA defined and developed. In addition, an automated classifier using a decision tree algorithm was trained and tested using a 10-fold cross-validation. The empirical classification system showed a correct classification rate (CCR) of 0.83 (Cohen's Kappa κ=0.81, FNR=0.08), and the decision tree classifier achieved a CCR of 0.79 (κ=0.73, FNR=0.08) when compared to gold standard PSG assessment. The relatively high CCR, and low FNR indicate that a OSA severity system based on dual RIP and oximetry is feasible for application as a pre-PSG screening tool.

Characterising infant inter-breath interval patterns during active and quiet sleep using recurrence plot analysis

Breathing patterns are characteristically different between active and quiet sleep states in infants. It has been previously identified that breathing dynamics are governed by a non-linear controller which implies the need for a nonlinear analytical tool. Further, it has been shown that quantified nonlinear variables are different between adult sleep states. This study aims to determine whether a nonlinear analytical tool known as recurrence plot analysis can characterize breath intervals of active and quiet sleep states in infants. Overnight polysomnograms were obtained from 32 healthy infants. The 6 longest periods each of active and quiet sleep were identified and a software routine extracted inter-breath interval data for recurrence plot analysis. Determinism (DET), laminarity (LAM) and radius (RAD) values were calculated for an embedding dimension of 4, 6, 8 and 16, and fixed recurrence of 0.5, 1, 2, 3.5 and 5%. Recurrence plots exhibited characteristically different patterns for active and quiet sleep. Active sleep periods typically had higher values of RAD, DET and LAM than for quiet sleep, and this trend was invariant to a specific choice of embedding dimension or fixed recurrence. These differences may provide a basis for automated sleep state classification, and the quantitative investigation of pathological breathing patterns.

Investigating parameters participating in the infant respiratory control system attractor

Theoretically, any participating parameter in a non-linear system represents the dynamics of the whole system. Taken's time delay embedding theory provides the fundamental basis for allowing non-linear analysis to be performed on physiological, time-series data. In practice, only one measurable parameter is required to be measured to convey an accurate representation of the system dynamics. In this paper, the infant respiratory control system is represented using three variables-a digitally sampled respiratory inductive plethysmography waveform, and the derived parameters tidal volume and inter-breath interval time series data. For 14 healthy infants, these data streams were analysed using recurrence plot analysis across one night of sleep. The measured attractor size of these variables followed the same qualitative trends across the nights study. Results suggest that the attractor size measures of the derived IBI and tidal volume are representative surrogates for the raw respiratory waveform. The extent to which the relative attractor sizes of IBI and tidal volume remain constant through changing sleep state could potentially be used to quantify pathology, or maturation of breathing control.

Developing robust recurrence plot analysis techniques for investigating infant respiratory patterns

Recurrence plot analysis is a useful non-linear analysis tool. There are still no well formalised procedures for carrying out this analysis on measured physiological data, and systemising analysis is often difficult. In this paper, the recurrence based embedding is compared to radius based embedding by studying a logistic attractor and measured breathing data collected from sleeping human infants. Recurrence based embedding appears to be a more robust method of carrying out a recurrence analysis when attractor size is likely to be different between datasets. In the infant breathing data, the radius measure calculated at a fixed recurrence, scaled by average respiratory period, allows the accurate discrimination of active sleep from quiet sleep states (AUC=0.975, Sn=098, Sp=0.94).