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 Algorithm for automatic detection of self-similarity and prediction of residual central respiratory events during continuous positive airway pressure

SLEEP ◽  
2020 ◽  
Author(s):  
Eline Oppersma ◽  
Wolfgang Ganglberger ◽  
Haoqi Sun ◽  
Robert J Thomas ◽  
M Brandon Westover
Keyword(s):  
Continuous Positive Airway Pressure ◽  
Airway Pressure ◽  
Positive Airway Pressure ◽  
Significant Risk ◽  
Periodic Breathing ◽  
Driving Mechanism ◽  
Central Apnea ◽  
Self Similarity ◽  
Respiratory Events ◽  
High Loop

Abstract Study Objectives Sleep-disordered breathing is a significant risk factor for cardiometabolic and neurodegenerative diseases. High loop gain (HLG) is a driving mechanism of central sleep apnea or periodic breathing. This study presents a computational approach that identifies “expressed/manifest” HLG via a cyclical self-similarity feature in effort-based respiration signals. Methods Working under the assumption that HLG increases the risk of residual central respiratory events during continuous positive airway pressure (CPAP), the full night similarity, computed during diagnostic non-CPAP polysomnography (PSG), was used to predict residual central events during CPAP (REC), which we defined as central apnea index (CAI) higher than 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 sleep database was used, including 2466 PSG pairs of diagnostic and CPAP titration PSG recordings. Results Diagnostic CAI based on technologist labels predicted REC with an area under the curve (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 higher than 5. Full night similarity predicted REC with an AUC of 0.57 ± 0.07 for manual and 0.65 ± 0.06 for automated labels. Conclusions The proposed self-similarity feature, as a surrogate estimate of expressed respiratory HLG and computed from easily accessible effort signals, can detect periodic breathing regardless of admixed obstructive features such as flow limitation and can aid the prediction of REC.

446 Acute effect of acetazolamide in high loop gain sleep apnea

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A176-A176
Author(s):  
Yuenan Ni ◽  
Robert Thomas
Keyword(s):  
Logistic Regression ◽  
Sleep Apnea ◽  
Multiple Logistic Regression Analysis ◽  
Periodic Breathing ◽  
Acute Effect ◽  
Loop Gain ◽  
Multivariable Logistic Regression Model ◽  
Arousal Index ◽  
Obstructive Sleep ◽  
High Loop

Abstract Introduction Obstructive sleep apnea is a disease with different driver phenotypes, including high loop gain (HLG). Acetazolamide (AZT) reduces HLG through multiple mechanisms. The acute effect of AZT used during titration polysomnography in HLG sleep apnea (HLGSA, predominantly obstructive) is described here. HLGSA is a NREM-dominant disease. Methods HLGSA was identified by one or more of the following: 1) baseline or titration CAHI of 5 or more, baseline or titration periodic breathing, or high residual apnea on CPAP in the absence of large leak. Retrospective analysis of polysomnograms from patients with HLGSA who underwent a PAP titration study and took ATZ (125 or 250 mg) after a baseline component of PAP titration. A responder was defined as a minimum reduction of the AHI3% of 50%. Multivariable logistic regression model estimated responder predictors. Results Two hundred and thirty-six patients with a median age of 60 (50.25–68) years and 189 (80.1%) males, were included. 69 patients were given 125 mg ATZ and 157 patients took 250 mg after about 3 hours of initial drug-free titration. Compared to PAP alone, PAP plus ATZ reduced the breathing related arousal index (8.45[3.03–15.60] vs. 4.8[2.1–10.15], p<0.001), AHI3% (19.09[7.34–37.28] vs. 10.63[4.46–20.56], p<0.001), AHI4% (1.89[0.23–8.58] vs. 1.19 [0.42–4.70], p=0.001), RDI (24.01[10.55–41.46] vs. 13.55[7.24–25.66], p<0.001). ATZ minimally improved the Min SpO2 (90[87–92] vs. 91[88–92], p=0.014). 101 patients were responders. Multiple logistic regression analysis showed that the NREM AHI3% was the only predictor for responder status with ATZ exposure (OR 1.022, 95%CI [1.004–1.041], p=0.018) Conclusion ATZ acutely improves PAP efficacy in HLGSA. The NREM AHI3% is a predictor for the ATZ responders. Support (if any) This study was supported by American Academy of Sleep Medicine Foundation, category-I award to RJT

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 A Detection and Therapeutic Device to Overcome Sleep Apnea in Infants

2020 ◽  
Vol 2 (1) ◽  
pp. 35
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Airway Pressure ◽  
Upper Airway ◽  
Periodic Breathing ◽  
Breathing Patterns ◽  
Efficient System ◽  
Pressure Therapy ◽  
Obstructive Sleep ◽  
Upper Airway Structure

Among the various sleep-disordered breathing patterns infant’s experience, like periodic breathing, premature apnea, obstructive sleep apnea, has been considered a major cause of concern. Upper airway structure, mechanics of the pulmonary system, etc., are a few reasons why the infants are vulnerable to obstructive sleep-disordered. An imbalance in the viscoelastic properties of the pharynx, dilators, and pressure can lead to airway collapse. A low level of oxygen in blood or hypoxemia is considered a characteristic in infants with severe OSA. Invasive treatments like nasopharyngeal tubes, continuous positive airway pressure (CPAP), or tracheostomy are found to be helpful in most cases where infants experience sleep apnea. This paper proposes an efficient system for monitoring obstructive sleep apnea in infants on a long-term basis, and if any anomaly is detected, the device provides Continuous Airway Pressure therapy until the abnormality is normalized.

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 0704 Treatment-emergent Central Sleep Apnea Predicts Residual Respiratory Instability During Cpap Use At 6 Months

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A268-A268
Author(s):  
R J Thomas
Keyword(s):  
Sleep Apnea ◽  
Odds Ratio ◽  
Airway Pressure ◽  
Positive Airway Pressure ◽  
Medical Center ◽  
Central Sleep Apnea ◽  
Periodic Breathing ◽  
Arousal Index ◽  
High Loop ◽  
Central Apneas

Abstract Introduction The prevalence, severity, significance, and predictors of residual sleep apnea during use of continuous positive airway pressure (CPAP) remain uncertain. High loop gain is associated with or induces periodic breathing and central sleep apnea (CSA). Treatment-emergent CSA (TE-CSA) is often considered a transient phenomenon of no long-term clinical significance. Standard polysomnographic features were assessed as risk factors for high residual apnea during compliant CPAP use. Methods Patients with sleep apnea (mean AHI 53.6, SD:33/hour of sleep) who underwent split night studies were prospectively entered in a database. They were all treated with positive airway pressure at the Beth Israel Deaconess Medical Center (Boston) and tracked by the EncoreAnywhere system. Machine detected AHI (AHIm) was extracted for a week average at month 6. The manual scored AHI(AHIs) was calculated from the last waveform graph during every month. Logistic regression assessed predictors of elevated automated (5 or greater) or manual (10 or greater) residual events//hour of use. Results A total of 69 CPAP compliant (average of at least 4 hours) subjects were analyzed. Age: 59.5 (range 17-81), gender: 47/69 male. 44/69 had an elevated manual AHI, while 20/69 had an elevated autodetected AHI. The only predictors of high residual apnea were TE-CSA (5 or more central apneas and hypopneas/hour of sleep): Odds Ratio 3.6 (CI: 1.07-12-3), p: 0.39. and the treatment component arousal index: Odds Ratio 1.06 (CI: 1.01-1.11), p: 0.018. Machine estimated AHI, which under-detected events by a factor of 3 or more, was not associated with any measure. Conclusion Residual apnea is common after 6 months of compliant CPAP use, and the only predictors identified were TE-CSA and treatment component arousal index. Support This study is supported by American Academy of Sleep Medicine Foundation, Category-I award to RJT

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 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).

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