Stable Breathing in Patients With Obstructive Sleep Apnea Is Associated With Increased Effort but Not Lowered Metabolic Rate

Is the time below 90% of SpO2 during sleep (T90%) a metric of good health? A longitudinal analysis of two cohorts

BACKGROUND

Novel wireless-based technologies can easily record pulse oximetry at home. One of the main parameters that are recorded in sleep studies is the time under 90% of SpO2 (T90%) and the oxygen desaturation index 3% (ODI-3%). We assessed the association of T90% and/or ODI-3% in two different scenarios (a community-based study and a clinical setting) with all-cause mortality (primary outcome).

METHODS

We included all individuals from the Sleep Heart Health Study (SHHS, community-based cohort) and Santiago Obstructive Sleep Apnea (SantOSA, clinical cohort) with complete data at baseline and follow-up. Two measures of hypoxemia (T90% and ODI-3%) were our primary exposures. The adjusted hazard ratios (HRs) per standard deviation (pSD) between T90% and incident all-cause mortality (primary outcome) were determined by adjusted Cox regression models. In the secondary analysis, to assess whether T90% varies across clinical factors, anthropometrics, abdominal obesity, metabolic rate, and SpO2, we conducted linear regression models. Incremental changes in R2 were conducted to test the hypothesis.

RESULTS

A total of 4323 (56% male, median 64 years old, follow-up: 12 years, 23% events) and 1345 (77% male, median 55 years old, follow-up: 6 years, 11.6% events) patients were included in SHHS and SantOSA, respectively. Every 1 SD increase in T90% was associated with an adjusted HR of 1.18 [95% CI: 1.10-1.26] (p value < 0.001) in SHHS and HR 1.34 [95% CI: 1.04-1.71] (p value = 0.021) for all-cause mortality in SantOSA. Conversely, ODI-3% was not associated with worse outcomes. R2 explains 62% of the variability in T90%. The main contributors were baseline-mean change in SpO2, baseline SpO2, respiratory events, and age.

CONCLUSION

The findings suggest that T90% may be an important marker of wellness in clinical and community-based scenarios. Although this nonspecific metric varies across the populations, ventilatory changes during sleep rather than other physiological or comorbidity variables explain their variability.

Physiological Determinants of Snore Loudness

Rationale: The physiological factors modulating the severity of snoring have not been adequately described. Airway collapse or obstruction is generally the leading determinant of snore sound generation; however, we suspect that ventilatory drive is of equal importance. Objective: To determine the relationship between airway obstruction and ventilatory drive on snore loudness. Methods: In 40 patients with suspected or diagnosed obstructive sleep apnea (1-98 events/hr), airflow was recorded via a pneumotachometer attached to an oronasal mask, ventilatory drive was recorded using calibrated intraesophageal diaphragm electromyography, and snore loudness was recorded using a calibrated microphone attached over the trachea. "Obstruction" was taken as the ratio of ventilation to ventilatory drive and termed flow:drive, i.e., actual ventilation as a percentage of intended ventilation. Lower values reflect increased flow resistance. Using 165,063 breaths, mixed model analysis (quadratic regression) quantified snore loudness as a function of obstruction, ventilatory drive, and the presence of extreme obstruction (i.e., apneic occlusion). Results: In the presence of obstruction (flow:drive = 50%, i.e., doubled resistance), snore loudness increased markedly with increased drive (+3.4 [95% confidence interval, 3.3-3.5] dB per standard deviation [SD] change in ventilatory drive). However, the effect of drive was profoundly attenuated without obstruction (at flow:drive = 100%: +0.23 [0.08-0.39] dB per SD change in drive). Similarly, snore loudness increased with increasing obstruction exclusively in the presence of increased drive (at drive = 200% of eupnea: +2.1 [2.0-2.2] dB per SD change in obstruction; at eupneic drive: +0.14 [-0.08 to 0.28] dB per SD change). Further, snore loudness decreased substantially with extreme obstruction, defined as flow:drive <20% (-9.9 [-3.3 to -6.6] dB vs. unobstructed eupneic breathing). Conclusions: This study highlights that ventilatory drive, and not simply pharyngeal obstruction, modulates snore loudness. This new framework for characterizing the severity of snoring helps better understand the physiology of snoring and is important for the development of technologies that use snore sounds to characterize sleep-disordered breathing.

Neural ventilatory drive decline as a predominant mechanism of obstructive sleep apnoea events

BACKGROUND

In the classic model of obstructive sleep apnoea (OSA), respiratory events occur with sleep-related dilator muscle hypotonia, precipitating increased neural ventilatory 'drive'. By contrast, a drive-dependent model has been proposed, whereby falling drive promotes dilator muscle hypotonia to precipitate respiratory events. Here we determine the extent to which the classic versus drive-dependent models of OSA are best supported by direct physiological measurements.

METHODS

In 50 OSA patients (5-91 events/hour), we recorded ventilation ('flow', oronasal mask and pneumotach) and ventilatory drive (calibrated intraoesophageal diaphragm electromyography, EMG) overnight. Flow and drive during events were ensemble averaged; patients were classified as drive dependent if flow fell/rose simultaneously with drive. Overnight effects of lower drive on flow, genioglossus muscle activity (EMGgg) and event risk were quantified (mixed models).

RESULTS

On average, ventilatory drive fell (rather than rose) during events (-20 (-42 to 3)%_baseline, median (IQR)) and was strongly correlated with flow (R=0.78 (0.24 to 0.94)). Most patients (30/50, 60%) were classified as exhibiting drive-dependent event pathophysiology. Lower drive during sleep was associated with lower flow (-17 (-20 to -14)%/drive) and EMGgg (-3.5 (-3.8 to -3.3)%_max/drive) and greater event risk (OR: 2.2 (1.8 to 2.5) per drive reduction of 100%_eupnoea); associations were concentrated in patients with drive-dependent OSA (ie, flow: -37 (-40 to -34)%/drive, OR: 6.8 (5.3 to 8.7)). Oesophageal pressure-without tidal volume correction-falsely suggested rising drive during events (classic model).

CONCLUSIONS

In contrast to the prevailing view, patients with OSA predominantly exhibit drive-dependent event pathophysiology, whereby flow is lowest at nadir drive, and lower drive raises event risk. Preventing ventilatory drive decline is therefore considered a target for OSA intervention.

Ventilatory Drive Withdrawal Rather Than Reduced Genioglossus Compensation as a Mechanism of Obstructive Sleep Apnea in REM Sleep

Rationale: REM sleep is associated with reduced ventilation and greater obstructive sleep apnea (OSA) severity than non-REM (nREM) sleep for reasons that have not been fully elucidated. Objectives: Here, we use direct physiological measurements to determine whether the pharyngeal compromise in REM sleep OSA is most consistent with 1) withdrawal of neural ventilatory drive or 2) deficits in pharyngeal pathophysiology per se (i.e., increased collapsibility and decreased muscle responsiveness). Methods: Sixty-three participants with OSA completed sleep studies with gold standard measurements of ventilatory "drive" (calibrated intraesophageal diaphragm EMG), ventilation (oronasal "ventilation"), and genioglossus EMG activity. Drive withdrawal was assessed by examining these measurements at nadir drive (first decile of drive within a stage). Pharyngeal physiology was assessed by examining collapsibility (lowered ventilation at eupneic drive) and responsiveness (ventilation-drive slope). Mixed-model analysis compared REM sleep with nREM sleep; sensitivity analysis examined phasic REM sleep. Measurements and Main Results: REM sleep (⩾10 min) was obtained in 25 patients. Compared with drive in nREM sleep, drive in REM sleep dipped to markedly lower nadir values (first decile, estimate [95% confidence interval], -21.8% [-31.2% to -12.4%] of eupnea; P < 0.0001), with an accompanying reduction in ventilation (-25.8% [-31.8% to -19.8%] of eupnea; P < 0.0001). However, there was no effect of REM sleep on collapsibility (ventilation at eupneic drive), baseline genioglossus EMG activity, or responsiveness. REM sleep was associated with increased OSA severity (+10.1 [1.8 to 19.8] events/h), but this association was not present after adjusting for nadir drive (+4.3 [-4.2 to 14.6] events/h). Drive withdrawal was exacerbated in phasic REM sleep. Conclusions: In patients with OSA, the pharyngeal compromise characteristic of REM sleep appears to be predominantly explained by ventilatory drive withdrawal rather than by preferential decrements in muscle activity or responsiveness. Preventing drive withdrawal may be the leading target for REM sleep OSA.

A longitudinal study of the accuracy of positive pressure therapy machine-detected apnea-hypopnea events

STUDY OBJECTIVES

During positive airway pressure (PAP) therapy for sleep apnea syndromes, the machine detected respiratory event index (REI_FLOW) is an important method for clinicians to evaluate the beneficial effects of PAP. There are concerns about the accuracy of this detection, which also confounds a related question-how common and severe are residual events on PAP.

METHODS

Subjects with OSA who underwent a split night polysomnography were recruited prospectively. Those treated with PAP and tracked by the EncoreAnywhere system were analyzed. The ones who stopped PAP within one month were excluded for this analysis. Compliance, therapy data and waveform data were analyzed. Machine detected versus manually scored events were compared at the 1st, 3rd, 6th and 12th month from PAP initiation. Logistic regression was used to determine factors associated with a high REI_FLOW difference.

RESULTS

One hundred and seventy-nine patients with a mean age 59.06 ± 13.97 years old, median body mass index 33.60 (29.75-38.75) kg/m², and median baseline AHI 46.30 (31.50-65.90) times/hour were included. The difference between the machine detected REI_FLOW and manually scored REI_FLOW was 10.72 ±8.43 in the first month and remained stable for up to 12 months. Male sex and large leak ≥ 1.5% were more frequent in patients who had an REI_FLOW difference of ≥ 5 / hour of use. A titration arousal index ≥ 15/ hour of sleep, and higher ratio of unstable to stable breathing were also associated with an REI_FLOW difference ≥ 5 times/hour of use.

CONCLUSIONS

There is a substantial and sustained difference between manual and automated event estimates during PAP therapy, and some associated factors were identified.

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.

An efficient design for real-time obstructive sleep apnea OSA detection through esophageal pressure Pes signal

Obstructive Sleep Apnea (OSA) is a potentially common sleep disorder in which the upper airways are collapsed either partially or completely. The golden standard method for treating OSA, is the full night Continuous Positive Airway Pressure (CPAP). Yet, due to the ensuing discomfort, it incurs on patients, researchers have been motivated to investigate other alternatives, whereby, OSA can be effectively treated. Recently, an increasingly popular OSA treatment has been developed that consists in activating the protrusion muscles of the tongue by stimulating the Hypoglossal Nerve (HGN). In this context, the present work is conducted to propose the design of apnea detector module as part of an implantable HGN stimulator based on the esophageal Pressure Pes signal as a new approach for controlling OSA occurrence. Specifically, an effective real-time apnea event detecting algorithm is put forward. Following the achievement of satisfactory simulation results, attained through the Modelsim simulation tool, we proceeded with assessing the possibility of its hardware implementation on a Field-Programmable Gate Array (FPGA) device. To this end, the apnea detector module was synthesized and designed. The low power consumption and the small size, characterizing this module, which have made it possible to integrate it as part of a wirelessly-powered implantable HGN stimulator.

Multicomponent Analysis of Sleep Using Electrocortical, Respiratory, Autonomic and Hemodynamic Signals Reveals Distinct Features of Stable and Unstable NREM and REM Sleep

A new concept of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep is proposed, that of multi-component integrative states that define stable and unstable sleep, respectively, NREM_S, NREM_US REM_S, and REM_US. Three complementary data sets are used: obstructive sleep apnea (20), healthy subjects (11), and high loop gain sleep apnea (50). We use polysomnography (PSG) with beat-to-beat blood pressure monitoring, and electrocardiogram (ECG)-derived cardiopulmonary coupling (CPC) analysis to demonstrate a bimodal, rather than graded, characteristic of NREM sleep. Stable NREM (NREM_S) is characterized by high probability of occurrence of the <1 Hz slow oscillation, high delta power, stable breathing, blood pressure dipping, strong sinus arrhythmia and vagal dominance, and high frequency CPC. Conversely, unstable NREM (NREM_US) has the opposite features: a fragmented and discontinuous <1 Hz slow oscillation, non-dipping of blood pressure, unstable respiration, cyclic variation in heart rate, and low frequency CPC. The dimension of NREM stability raises the possibility of a comprehensive integrated multicomponent network model of NREM sleep which captures sleep onset (e.g., ventrolateral preoptic area-based sleep switch) processes, synaptic homeostatic delta power kinetics, and the interaction of global and local sleep processes as reflected in the spatiotemporal evolution of cortical “UP” and “DOWN” states, while incorporating the complex dynamics of autonomic-respiratory-hemodynamic systems during sleep. Bimodality of REM sleep is harder to discern in health. However, individuals with combined obstructive and central sleep apnea allows ready recognition of REM_S and REM_US (stable and unstable REM sleep, respectively), especially when there is a discordance of respiratory patterns in relation to conventional stage of sleep.

Neural memory of the genioglossus muscle during sleep is stage-dependent in healthy subjects and obstructive sleep apnoea patients

KEY POINTS

In most patients with obstructive sleep apnoea (OSA), there is a spontaneous resolution of the breathing disorders during slow wave sleep (SWS) for yet unknown reasons related to non-anatomical factors. Some recently identified forms of neural memory specific of upper airway muscles may play a role in this phenomenon. In the present study, we show for the first time that a form of memory of the genioglossus (tongue) muscle is greatly enhanced during SWS compared to non-rapid eye movement stage 2 sleep. The present study represents a step forward in understanding the mechanisms responsible for the spontaneous development of stable breathing during SWS in OSA patients and may help the discovery of novel therapeutic strategies for this disease.

ABSTRACT

Several studies have shown that obstructive sleep apnoea (OSA) improves during slow wave sleep (SWS) for reasons that remain unclear. Recent studies have identified forms of neural memory such as short-term potentiation or after-discharge that can occur in response to upper airway obstruction. Neural memory may play a role in the development of stable breathing during SWS by increasing upper airway muscles activity in this sleep stage. We hypothesize that the after-discharge of the genioglossus muscle following upper airway obstruction is enhanced during SWS compared to non-rapid eye movement stage 2 (N2). During sleep, we performed five-breath drops in continuous positive airway pressure (CPAP-drop) to simulate obstructive events and reflexively activate the genioglossus. Immediately afterwards, CPAP was returned to an optimal level. Once the post-drop ventilation returned to eupnoea, the genioglossus after-discharge was measured as the time it took for genioglossus activity to return to baseline levels. In total, 171 CPAP-drops were analysed from a group of 16 healthy subjects and 19 OSA patients. A mixed-model analysis showed that after-discharge duration during SWS was 208% (95% confidence interval = 112% to 387%, P = 0.022) greater than during N2 after adjusting for covariates (ventilatory drive, CPAP levels). There was also a non-significant trend for a -35% reduction in after-discharge duration following an arousal vs. no-arousal from sleep (95% confidence interval = -59.5% to 5%, P = 0.08). Genioglossus after-discharge is two-fold greater in SWS vs. N2, which could partly explain the breathing stabilization described in OSA patients during this sleep stage.