scholarly journals 0448 Scaled-Up Sleep Apnea Endotyping Using Polysomnography for Clinical Use

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A171-A172
Author(s):  
E Finnsson ◽  
S Æ Jónsson ◽  
G H Ólafsdóttir ◽  
D L Loftsdóttir ◽  
H Helgadóttir ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Scale Up ◽  
Heart Association ◽  
Original Method ◽  
Strongly Correlated ◽  
Loop Gain ◽  
Horizon 2020 ◽  
Arousal Threshold ◽  
High Loop ◽  
Ventilatory Drive

Abstract Introduction Sleep apnea is caused by several key endophenotypic traits namely pharyngeal collapsibility, poor muscle compensation, ventilatory instability (high loop gain), and arousability from sleep (low arousal threshold). Already, measures of these traits have shown promise for predicting outcomes of therapies (oral appliances, surgery, hypoglossal nerve stimulation, CPAP, or pharmaceuticals) and thus may be an integral part of future precision sleep medicine with treatments administered based on underlying pathophysiology. However, currently, the novel methods developed for endotyping from polysomnography are computationally expensive and can only be performed by the original authors or their collaborators due to the need for technological expertise. Here we present a cloud-based method for endotyping sleep apnea from polysomnography for use in the clinical arena. Methods For cloud-based use, we optimized the Phenotype Using Polysomnography (‘PUP’) method of Sands et.al. (2015-2018) by performing the following: Code was translated from MATLAB to Python; efficient methods were developed to detect breaths, calculate normalized ventilation (moving time-average), and model ventilatory drive (intended ventilation). The new implementation (‘PUP.py’) was validated by comparing the measured traits against the original values. Results 38 manually scored clinical polysomnographic studies were endophenotyped using the two implementations. Results of the new implementation (‘PUP.py’) were strongly correlated with the original (p<10-6 for all): collapsibility and compensation (ventilation at eupneic drive ‘Vpassive’: r=0.98; ventilation at arousal threshold, r=0.97), loop gain (r=0.96), and arousal threshold (r=0.92). Conclusion We successfully implemented the original method by Sands et.al. to scale up sleep apnea endotyping and make it available to a broader audience. Support This work was supported by the Icelandic Centre for Research RANNÍS, the European Union’s Horizon 2020 SME Instrument (733461), and the American Heart Association (15SDG25890059).

scholarly journals A scalable method of determining physiological endotypes of sleep apnea from a polysomnographic sleep study

SLEEP ◽  
2020 ◽  
Author(s):  
Eysteinn Finnsson ◽  
Guðrún H Ólafsdóttir ◽  
Dagmar L Loftsdóttir ◽  
Sigurður Æ Jónsson ◽  
Halla Helgadóttir ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Hypoglossal Nerve ◽  
Scaling Up ◽  
Strongly Correlated ◽  
Oral Appliances ◽  
Loop Gain ◽  
Sleep Study ◽  
Arousal Threshold ◽  
High Loop ◽  
Ventilatory Drive

Abstract Sleep apnea is caused by several endophenotypic traits, namely pharyngeal collapsibility, poor muscle compensation, ventilatory instability (high loop gain), and arousability from sleep (low arousal threshold). Measures of these traits have shown promise for predicting outcomes of therapies (e.g. oral appliances, surgery, hypoglossal nerve stimulation, CPAP, and pharmaceuticals), which may become an integral part of precision sleep medicine. Currently, the methods Sands et al. developed for endotyping sleep apnea from polysomnography (PSG) are embedded in the original authors’ code, which is computationally expensive and requires technological expertise to run. We present a reimplementation and validation of the integrity of the original authors’ code by reproducing the endo-Phenotyping Using Polysomnography (PUP) method of Sands et al. The original MATLAB methods were reprogrammed in Python; efficient algorithms were developed to detect breaths, calculate normalized ventilation (moving time-average), and model ventilatory drive (intended ventilation). The new implementation (PUPpy) was validated by comparing the endotypes from PUPpy with the original PUP results. Both endotyping methods were applied to 38 manually scored polysomnographic studies. Results of the new implementation were strongly correlated with the original (p < 10–6 for all): ventilation at eupnea V̇ passive (ICC = 0.97), ventilation at arousal onset V̇ active (ICC = 0.97), loop gain (ICC = 0.96), and arousal threshold (ICC = 0.90). We successfully implemented the original PUP method by Sands et al. providing further evidence of its integrity. Additionally, we created a cloud-based version for scaling up sleep apnea endotyping that can be used more easily by a wider audience of researchers and clinicians.

scholarly journals O013 The pathogenesis of obstructive sleep apnea in individuals with comorbid insomnia and obstructive sleep apnoea (COMISA)

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A6-A7
Author(s):  
E Brooker ◽  
L Thomson ◽  
S Landry ◽  
B Edwards ◽  
S Drummond
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Upper Airway ◽  
Severity Index ◽  
Loop Gain ◽  
Comorbid Insomnia ◽  
Arousal Threshold ◽  
Obstructive Sleep ◽  
High Loop ◽  
Insomnia Severity

Abstract Obstructive sleep apnea (OSA) and Insomnia are prevalent sleep disorders which are highly comorbid. This frequent co-occurrence suggests a shared etiology may exist. OSA is caused by the interaction of four pathophysiological traits: a highly collapsible upper airway, elevated loop gain, a low arousal threshold, and poor muscle compensation. No study has ascertained whether these traits are influenced by insomnia. We aimed to quantify the four traits which contribute to OSA in individuals diagnosed with comorbid insomnia and OSA (COMISA). We non-invasively determined these traits in 52 COMISA patients (Age: 56±14 years) with mild-to-severe OSA (AHI=21.2±10.63 events/h) using polysomnography. Our results indicated that 83% of COMISA patients had a low arousal threshold and only 2% of patients exhibited a highly collapsible airway using previously defined thresholds. Multiple linear regression revealed the arousal threshold (b=0.24, 95%CI[0.11, 0.37], β=0.47, p<0.001) and loop gain (b=23.6, 95%CI[7.02, 40.18], β=0.33, p<0.01) were the strongest predictors of OSA severity in our sample. There was no significant relationship between the arousal threshold and insomnia severity measured by the insomnia severity index (ISI). Further work is being performed to compare these findings with a matched sample of OSA only participants. Our preliminary findings demonstrate OSA in COMISA is characterized by a mildly collapsible airway/low arousal threshold phenotype and is largely driven by non-anatomical factors including a low arousal threshold and high loop gain. OSA treatments which are effective in patients with mild anatomical compromise and raise the arousal threshold may provide therapeutic benefit in COMISA patients.

scholarly journals 0710 Hypoglossal Neurostimulation In Rem- Vs. Nrem-predominant Obstructive Sleep Apnea

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A270-A270
Author(s):  
C I Cabrera ◽  
B Szelestey ◽  
K Strohl ◽  
A Schell
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Univariate Analysis ◽  
Disease Process ◽  
Loop Gain ◽  
Upper Airway Stimulation ◽  
Arousal Threshold ◽  
Obstructive Sleep ◽  
Baseline Characteristics ◽  
Star Trial

Abstract Introduction Obstructive sleep apnea (OSA) is a chronic condition that requires appropriate treatment strategies to optimize outcomes while minimizing risk. In addition to anatomy, physiologic factors such as arousal threshold and loop gain (i.e. “endotypes”) play a known role in the disease process. Because loop gain tends to be higher and arousal threshold lower in NREM sleep, we hypothesize that patients with NREM-predominance may achieve less success with anatomical therapy such as upper airway stimulation (UAS). Our study aims to evaluate baseline characteristics and objective results related to NREM-predominance in patients treated with UAS. Methods Using data from the STAR trial, we identified patients (n=103) with at least 20 minutes of REM on baseline testing and complete demographic and disease data at baseline and month 18. Baseline NREM-predominant disease (percent NREM events > 50) was defined as a binary variable. We created two cohorts: 1) patients with REM-predominant disease and 2) those with NREM-predominant disease. ODI and AHI were evaluated at month 18. Results Overall 62% (n=64) of patients had NREM-predominant disease at baseline. Other baseline characteristics were similar between both groups. In univariate analysis, age was significantly associated with lower AHI in the NREM-predominant population (p<0.05) but not in the REM-predominant group (p>0.05). Results were similar for ODI. For both groups, increasing age was correlated negatively with increasing AHI; this correlation was stronger in the NREM-predominant group Conclusion A majority of patients in the STAR trial had NREM-predominant OSA at baseline. There appears to be an interaction between NREM-predominance and age as predictors of UAS outcomes. Support  

scholarly journals 475 Automatic detection of self-similarity and prediction of CPAP failure

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A187-A187
Author(s):  
Eline Oppersma ◽  
Wolfgang Ganglberger ◽  
Haoqi Sun ◽  
Robert Thomas ◽  
Michael Westover
Keyword(s):  
Sleep Apnea ◽  
Significant Risk ◽  
Periodic Breathing ◽  
Driving Mechanism ◽  
Central Apnea ◽  
Loop Gain ◽  
Breathing Patterns ◽  
Self Similarity ◽  
High Loop ◽  
Cpap Failure

Abstract Introduction Sleep disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. Tolerance and efficacy of continuous positive airway pressure (CPAP), the primary form of therapy for sleep apnea, is often poor. High loop gain (HLG) is a driving mechanism of central sleep apnea or periodic breathing. The current study aimed to develop a computational approach to detect HLG based on self-similarity in respiratory oscillations during sleep solely using breathing patterns, measured via Respiratory Inductance Plethysmography (RIP). To quantify the potential utility of the developed similarity metric, the presented algorithm was used to predict acute CPAP failure. Methods We developed an algorithm for detecting apneas as periods with reduced breathing effort, manifested in the RIP signal as low signal amplitude. Our algorithm calculates self-similarity in breathing patterns between consecutive periods of apnea or hypopnea. Working under the assumption that high loop gain induces self-similar respiratory oscillations and increases the risk of failure during CPAP, the full night similarity, computed during diagnostic non-CPAP polysomnography (PSG), was used to predict failure of CPAP, which we defined as titration central apnea index (CAI)>10. Central apnea labels are obtained both from manual scoring by sleep technologists, and from an automated algorithm developed for this study. The Massachusetts General Hospital (MGH) sleep database was used, including 2466 PSG pairs of diagnostic and CPAP titration PSG recordings. Results Diagnostic CAI based on technologist labels predicted failure of CPAP with an AUC of 0.82 ±0.03. Based on automatically generated labels, the combination of full night similarity and automatically generated CAI resulted in an AUC of 0.85 ±0.02. A subanalysis was performed on a population with technologist labeled diagnostic CAI>5. Full night similarity predicted failure with an AUC of 0.57 ±0.07 for manual and 0.65 ±0.06 for automated labels. Conclusion This study showed that central apnea labels can be derived in an automated way. The proposed self-similarity feature, as a surrogate estimate of expressed respiratory high loop gain and computed from easily accessible effort signals, can detect periodic breathing regardless of admixed obstructive features such as flow-limitation, and can aid prediction of CPAP failure or success. Support (if any):

scholarly journals Variations in loop gain and arousal threshold during NREM sleep are affected by time of day over a 24-hour period in participants with obstructive sleep apnea

2020 ◽  
Vol 129 (4) ◽  
pp. 800-809
Author(s):  
Shipra Puri ◽  
Mohamad El-Chami ◽  
David Shaheen ◽  
Blake Ivers ◽  
Gino S. Panza ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Nrem Sleep ◽  
Time Of Day ◽  
Loop Gain ◽  
Arousal Threshold ◽  
Obstructive Sleep ◽  
Critical Closing Pressure ◽  
Chemoreflex Sensitivity ◽  
Closing Pressure

Loop gain and the arousal threshold during non-rapid eye movement (NREM) sleep are greater in the morning compared with the afternoon and evening. Loop gain measures are correlated to chemoreflex sensitivity and the critical closing pressure measured during NREM sleep in the evening, morning, and afternoon. Breathing (in)stability and efficaciousness of treatments for obstructive sleep apnea may be modulated by a circadian rhythmicity in loop gain and the arousal threshold.

scholarly journals Targeting Endotypic Traits with Medications for the Pharmacological Treatment of Obstructive Sleep Apnea. A Review of the Current Literature

2019 ◽  
Vol 8 (11) ◽  
pp. 1846 ◽  
Author(s):  
Taranto-Montemurro ◽  
Messineo ◽  
Wellman
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Muscle Activity ◽  
Upper Airway ◽  
Loop Gain ◽  
Arousal Threshold ◽  
The Past ◽  
Obstructive Sleep ◽  
Near Future ◽  
Made In

Obstructive sleep apnea (OSA) is a highly prevalent condition with few therapeutic options. To date there is no approved pharmacotherapy for this disorder, but several attempts have been made in the past and are currently ongoing to find one. The recent identification of multiple endotypes underlying this disorder has oriented the pharmacological research towards tailored therapies targeting specific pathophysiological traits that contribute differently to cause OSA in each patient. In this review we retrospectively analyze the literature on OSA pharmacotherapy dividing the medications tested on the basis of the four main endotypes: anatomy, upper airway muscle activity, arousal threshold and ventilatory instability (loop gain). We show how recently introduced drugs for weight loss that modify upper airway anatomy may play an important role in the management of OSA in the near future, and promising results have been obtained with drugs that increase upper airway muscle activity during sleep and reduce loop gain. The lack of a medication that can effectively increase the arousal threshold makes this strategy less encouraging, although recent studies have shown that the use of certain sedatives do not worsen OSA severity and could actually improve patients’ sleep quality.

Different antimuscarinics when combined with atomoxetine have differential effects on obstructive sleep apnea severity

2021 ◽  
Author(s):  
Atqiya Aishah ◽  
Richard Lim ◽  
Scott A. Sands ◽  
Luigi Taranto-Montemurro ◽  
Andrew Wellman ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Upper Airway ◽  
Loop Gain ◽  
Double Blind ◽  
Receptor Selectivity ◽  
Arousal Threshold ◽  
Solifenacin Succinate ◽  
Obstructive Sleep ◽  
Airway Physiology

The combination of the noradrenergic agent atomoxetine plus the anti-muscarinic oxybutynin has recently been shown to improve upper airway physiology and reduce obstructive sleep apnea (OSA) severity. However, the effects of different anti-muscarinics when combined with atomoxetine is limited. This study aimed to determine the effects of atomoxetine combined with two different anti-muscarinics with varying M-subtype receptor selectivity on OSA severity and upper airway physiology.10 people with predominantly severe OSA completed a double-blind, randomised, placebo-controlled, cross-over trial. Participants completed 3 overnight in-laboratory sleep studies after either 80mg atomoxetine+5mg solifenacin succinate (ato-sol) or 80mg atomoxetine+2mg biperiden hydrochloride (ato-bip) or placebo. OSA severity, ventilatory stability (loop gain), respiratory-arousal threshold (via epiglottic manometry), next day subjective sleepiness (Karolinska Sleepiness Scale:KSS) and alertness were compared between conditions. Neither drug combination altered the apnea/hypopnoea index versus placebo (p=0.63). Ato-sol caused a shift towards milder respiratory events with reduced frequency of obstructive apneas (13±14vs. 22±17events/h; mean±SD, p=0.04) and increased hypopneas during NREM (38±21vs. 24±18events/h, p=0.006) with improved nadir oxygenation versus placebo (83±4vs. 80±8%, p=0.03). Both combinations reduced loop gain by ~10% versus placebo; sleep efficiency and arousal threshold were unaltered. Ato-bip reduced next-day sleepiness versus placebo (KSS=4.3±2.2vs. 5.6±1.6, p=0.03).Atomoxetine+biperiden hydrochloride reduces perceived sleepiness and atomoxetine+solifenacin modestly improves upper airway function in people with OSA but to a lesser extent versus recently published atomoxetine+oxybutynin (broad M-subtype receptor selectivity) findings. These results provide novel mechanistic insight into the role of noradrenergic and anti-muscarinic agents on sleep and breathing and are important for pharmacotherapy development for OSA.

scholarly journals O001 Reboxetine reduces obstructive sleep apnea severity: a randomized trial

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A1-A1
Author(s):  
T Altree ◽  
A Aishah ◽  
K Loffler ◽  
R Grunstein ◽  
D Eckert
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Respiratory Control ◽  
Upper Airway ◽  
Apnea Hypopnea Index ◽  
Loop Gain ◽  
Double Blind ◽  
Obstructive Sleep ◽  
High Loop ◽  
Control Stability

Abstract Introduction Noradrenergic and muscarinic processes are crucial for pharyngeal muscle control during sleep. Selective norepinephrine reuptake inhibitors (SNRIs) such as reboxetine combined with an antimuscarinic reduce obstructive sleep apnea (OSA) severity. The effects of reboxetine alone on OSA severity are unknown. Methods Double-blind, placebo-controlled, three-way crossover trial in 16 people with OSA. Each participant completed three overnight polysomnograms (~1-week washout). Single doses of reboxetine 4mg, placebo, or reboxetine+oxybutynin 5mg were administered before sleep (randomized order). The primary outcome was apnea-hypopnea index (AHI). Secondary outcomes included other polysomnography parameters, next day sleepiness and alertness. Endotyping analysis was performed to determine the medications’ effects on OSA pathophysiological mechanisms. Results Reboxetine reduced the AHI by 5.4 [95% CI -10.4 to -0.3] events/h, P=0.03 (men: -24±27%; women: -0.7±32%). The addition of oxybutynin did not further reduce AHI. Reboxetine alone and reboxetine+oxybutynin reduced overnight hypoxemia versus placebo (e.g. 4% oxygen desaturation index 10.4±12.8 vs. 10.6±12.8 vs. 15.7±14.7 events/h, P=0.02). Mechanistically, reboxetine and reboxetine+oxybutynin improved pharyngeal collapsibility and respiratory control stability. Men had higher baseline loop gain. Larger reductions in AHI with reboxetine occurred in those with high loop gain. Neither drug intervention changed next day sleepiness or alertness. Discussion A single 4mg dose of reboxetine modestly reduces OSA severity without further improvement with the addition of an antimuscarinic. Reboxetine increases breathing stability via improvements in pharyngeal collapsibility and respiratory control. These findings provide new insight into the role of SNRIs on upper airway stability during sleep and have important implications for pharmacotherapy development for OSA.

scholarly journals A method for measuring and modeling the physiological traits causing obstructive sleep apnea

2011 ◽  
Vol 110 (6) ◽  
pp. 1627-1637 ◽  
Author(s):  
Andrew Wellman ◽  
Danny J. Eckert ◽  
Amy S. Jordan ◽  
Bradley A. Edwards ◽  
Chris L. Passaglia ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Positive Airway Pressure ◽  
Upper Airway ◽  
Physiological Model ◽  
Physiological Traits ◽  
Loop Gain ◽  
Ventilatory Control ◽  
Obstructive Sleep ◽  
Ventilatory Drive

There is not a clinically available technique for measuring the physiological traits causing obstructive sleep apnea (OSA). Therefore, it is often difficult to determine why an individual has OSA or to what extent the various traits contribute to the development of OSA. In this study, we present a noninvasive method for measuring four important physiological traits causing OSA: 1) pharyngeal anatomy/collapsibility, 2) ventilatory control system gain (loop gain), 3) the ability of the upper airway to dilate/stiffen in response to an increase in ventilatory drive, and 4) arousal threshold. These variables are measured using a single maneuver in which continuous positive airway pressure (CPAP) is dropped from an optimum to various suboptimum pressures for 3- to 5-min intervals during sleep. Each individual's set of traits is entered into a physiological model of OSA that graphically illustrates the relative importance of each trait in that individual. Results from 14 subjects (10 with OSA) are described. Repeatability measurements from separate nights are also presented for four subjects. The measurements and model illustrate the multifactorial nature of OSA pathogenesis and how, in some individuals, small adjustments of one or another trait (which might be achievable with non-CPAP agents) could potentially treat OSA. This technique could conceivably be used clinically to define a patient's physiology and guide therapy based on the traits.

Sign in / Sign up

Export Citation Format

Share Document