The effect of continuous positive airway pressure on cardiopulmonary coupling

Cardiopulmonary Coupling

Cardiopulmonary coupling (CPC) is a technique that generates sleep spectrogram by calculating the cross-spectral power and coherence of heart rate variability and respiratory tidal volume fluctuations. There are several forms of CPC in the sleep spectrogram, which may provide information about normal sleep physiology and pathological sleep states. Since CPC can be calculated from any signal recording containing heart rate and respiration information, such as photoplethysmography (PPG) or blood pressure, it can be widely used in various applications, including wearables and non-contact devices. When derived from PPG, an automatic apnea-hypopnea index can be calculated from CPC-oximetry as PPG can be obtained from oximetry alone. CPC-based sleep profiling reveals the effects of stable and unstable sleep on sleep apnea, insomnia, cardiovascular regulation, and metabolic disorders. Here, we introduce, with examples, the current knowledge and understanding of the CPC technique, especially the physiological basis, analytical methods, and its clinical applications.

Cardiopulmonary coupling and serum cardiac biomarkers in obesity hypoventilation syndrome and obstructive sleep apnea with morbid obesity

STUDY OBJECTIVES

The main cause of death in patients with obesity hypoventilation syndrome (OHS) is cardiac rather than respiratory failure. Here, we investigated autonomic-respiratory coupling and serum cardiac biomarkers in patients with OHS and obstructive sleep apnea (OSA) with comparable body mass index (BMI) and apnea-hypopnea index (AHI).

METHODS

Cardiopulmonary coupling (CPC) and cyclic variation of heart rate (CVHR) analysis was performed on the electrocardiogram signal from the overnight polysomnogram. Cardiac serum biomarkers were obtained in patients with OHS and OSA with a BMI > 40kg/m². Samples were obtained at baseline and after 3 months of positive airway pressure (PAP) therapy in both groups.

RESULTS

Patients with OHS (n=15) and OSA (n=36) were recruited. No group differences in CPC, CVHR and serum biomarkers were observed at baseline and after 3 months of PAP therapy. An improvement in several CPC metrics, including the sleep apnea index, unstable sleep (low frequency coupling and elevated low frequency coupling narrow band [e-LFC_NB]) and CVHR were observed in both groups with PAP use. However, distinct differences in response characteristics were noted. e-LFC_NB coupling correlated with highly sensitive troponin (hs-troponin-T, p<0.05) in the combined cohort. Baseline hs-troponin-T inversely correlated with awake oxygen saturation in the OHS group (p<0.05).

CONCLUSIONS

PAP therapy can significantly improve CPC stability in obese patients with OSA or OHS, with key differences. e-LFC_NB may function as a surrogate biomarker for early subclinical cardiac disease. Low awake oxygen saturation could also increase this biomarker in OHS.

CLINICAL TRIAL REGISTRATION

Registry: Australian New Zealand Clinical Trials Registry; Name: Obesity Hypoventilation Syndrome and Neurocognitive Dysfunction; URL: https://anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367492; Identifier: ACTRN12615000122550.

Cardiopulmonary Sleep Spectrograms Open a Novel Window Into Sleep Biology-Implications for Health and Disease

The interactions of heart rate variability and respiratory rate and tidal volume fluctuations provide key information about normal and abnormal sleep. A set of metrics can be computed by analysis of coupling and coherence of these signals, cardiopulmonary coupling (CPC). There are several forms of CPC, which may provide information about normal sleep physiology, and pathological sleep states ranging from insomnia to sleep apnea and hypertension. As CPC may be computed from reduced or limited signals such as the electrocardiogram or photoplethysmogram (PPG) vs. full polysomnography, wide application including in wearable and non-contact devices is possible. When computed from PPG, which may be acquired from oximetry alone, an automated apnea hypopnea index derived from CPC-oximetry can be calculated. Sleep profiling using CPC demonstrates the impact of stable and unstable sleep on insomnia (exaggerated variability), hypertension (unstable sleep as risk factor), improved glucose handling (associated with stable sleep), drug effects (benzodiazepines increase sleep stability), sleep apnea phenotypes (obstructive vs. central sleep apnea), sleep fragmentations due to psychiatric disorders (increased unstable sleep in depression).

Metrics of sleep apnea severity: beyond the apnea-hypopnea index

Obstructive sleep apnea (OSA) is thought to affect almost 1 billion people worldwide. OSA has well established cardiovascular and neurocognitive sequelae, although the optimal metric to assess its severity and/or potential response to therapy remains unclear. The apnea-hypopnea index (AHI) is well established; thus, we review its history and predictive value in various different clinical contexts. Although the AHI is often criticized for its limitations, it remains the best studied metric of OSA severity, albeit imperfect. We further review the potential value of alternative metrics including hypoxic burden, arousal intensity, odds ratio product, and cardiopulmonary coupling. We conclude with possible future directions to capture clinically meaningful OSA endophenotypes including the use of genetics, blood biomarkers, machine/deep learning and wearable technologies. Further research in OSA should be directed towards providing diagnostic and prognostic information to make the OSA diagnosis more accessible and to improving prognostic information regarding OSA consequences, in order to guide patient care and to help in the design of future clinical trials.

Variation in Sleep Stability with Differences in Severity of Sleep-Disordered Breathing in Children

OBJECTIVES

The aim of this study was to analyze the association between obstructive sleep apnea (OSA) severity and various cardiopulmonary coupling (CPC) parameters in children with OSA.

STUDY DESIGN

Retrospective cross-sectional study.

METHODS

A cross-sectional study was conducted among 117 children (aged 7.96 ± 3.54 years, 86 male) who underwent both full-night polysomnography (PSG) and CPC for suspicion of sleep-disordered breathing (SDB). We analyzed the association between various CPC and PSG findings.

RESULTS

The apnea-hypopnea index (AHI) was negatively correlated with high frequency coupling (HFC, r = -0.374, P < .001) and very low frequency coupling (VLFC, r = -0.192, P = .038) and positively correlated with low frequency coupling (LFC, r = 0.503, P < .001), elevated low frequency coupling (e-LFC, r = 0.475, P < .001), and narrow and broad band e-LFC (e-LFC_NB and e-LFC_BB ; r = 0.221, P = .016 and r = 0.468, P < .001, respectively). The arousal index was negatively correlated with HFC (r = - 0.466, P < .001) and positively correlated with LFC, e-LFC, e-LFC_NB , and e-LFC_BB (r = 0.543, r = 0.460, r = 0.239, and r = 0.445, respectively; all P < .001). In addition, we also found a significant difference in various CPC values according to OSA severity.

CONCLUSION

CPC parameters accurately reflect sleep fragmentation and OSA severity in children. Thus, we can verify objective sleep quality using CPC analysis, which is a simple method of analyzing sleep stability in children with SDB.

LEVEL OF EVIDENCE

4 Laryngoscope, 131:435-439, 2021.

A Review of Current Tools Used for Evaluating the Severity of Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is a common and heterogeneous disease characterized by episodic collapse within the upper airways, which leads to reduced ventilation and adverse consequences, including hypoxia, hypercapnia, sleep fragmentation, and long-term effects such as cardiovascular comorbidities. The clinical diagnosis of OSA and its severity classification are often determined based on the apnea-hypopnea index (AHI), defining the number of apneic and hypopnea events per hour of sleep. However, the limitations of the AHI to assess disease severity have necessitated the exploration of other metrics for additional information to reflect the complexity of OSA. Novel parameters such as the hypoxic burden have the potential to better capture the main features of OSA by maximizing the information available from the polysomnogram. These emerging measures have described multidimensional qualities of sleep-disordered breathing events and breathing irregularity and will ultimately result in better management of OSA.

Significance of sleep stability using cardiopulmonary coupling in sleep disordered breathing

OBJECTIVES

The aim of this study was to assess the sleep quality and sleep stability according to the severity of obstructive sleep apnea (OSA) by using questionnaires and cardiopulmonary coupling (CPC) analysis.

METHODS

Two hundred and twenty-one adults were evaluated using subjective sleep questionnaires, CPC parameters, and respiratory parameters measured during full-night polysomnography. We measured the differences in the CPC parameters of each OSA group and the correlation between the apnea-hypopnea index (AHI) and CPC parameters.

RESULTS

AHI and CPC parameters were not associated with sleep questionnaires. AHI is negatively correlated with high frequency coupling (HFC, r = -0.725, P < 0.001) and very low frequency coupling (VLFC, r = -0.475, P < 0.001), but it is positively correlated with low frequency coupling (LFC, r = 0.786, P < 0.001) and narrow- and broadband-elevated low frequency coupling (e-LFC_NB and e-LFC_BB ; r = 0.522, P < 0.001 and r = 0.668, P < 0.001, respectively). We also found similar results regarding the correlation between the arousal index and CPC parameters. In addition, there were significant differences in HFC, LFC, VLFC, e-LFC_NB , and e-LFC_BB (all P < 0.001) between the severe OSA group and all other groups.

CONCLUSION

We found that CPC parameters significantly correlated with AHI. In addition, sleep stability-related parameters differ significantly based on OSA severity, and apneic parameters in the severe OSA group are significantly different from those in the other groups.

LEVEL OF EVIDENCE

4 Laryngoscope, 130: 2069-2075, 2020.