Cyclic Variation in Heart Rate Score by Holter Electrocardiogram as Screening for Sleep-Disordered Breathing in Subjects With Heart Failure

Evaluation and Treatment of Central Sleep Apnea in Patients with Heart Failure

Sleep-disordered breathing (SDB) is a common comorbidity in patients with heart failure (HF). Prevalence of the most common subtypes of SDB, central sleep apnea (CSA) and obstructive sleep apnea (OSA), is increasing, which is concerning due to the association of SDB with increased mortality in patients with heart failure. Despite an increasing burden of CSA in HF, it is difficult to detect using current diagnostic tools and the treatment modalities are limited by variable efficacy and patient adherence. Though positive airway pressure therapies remain the cornerstone of OSA treatment, the management of CSA in the setting of HF continues to evolve. The association of the presence of CSA with worse prognosis in HF patients warrants the need for routine screening for signs and symptoms of CSA in this population. In this review, we examine the connection between CSA and HF, highlighting advancements in timely diagnostics, treatment modalities, and strategies to promote facilitation of compliance in this high-risk cohort.

The Nadir Oxygen-Specific Heart Rate Response in Sleep Apnea Links With the Occurrence of Acute Myocardial Infarction

Background

Little is known regarding the quantification of sleep apnea- and hypoxemia-elicited heart rate (HR) response and its prognostic significance of the cardiovascular risk. We sought to explore the impact of HR response and variability specific to obstructive sleep apnea (OSA) on the occurrence of a common cardiovascular event – acute myocardial infarction (AMI).

Methods

Consecutive patients with suspected OSA were enrolled and underwent nocturnal respiratory study and electrocardiography monitoring. The minimal oxygen saturation (minSpO2) was determined from the oxygen saturation curve under a subject-specific search window. Primary HR metrics such as maximal HR in response to minSpO2 and respiratory event-specific HR variability were computed from the synchronized recordings. Multivariate regression analyses were conducted to analyze the associations between individualized HR metrics and the occurrence of AMI.

Results

Of 2,748 patients recruited, 39% (n = 1,071) had moderate-to-severe OSA (respiratory event index, REI ≥ 15), and 11.4% (n = 313) patients had AMI. Patients with AMI experienced severe OSA, severe minSpO2, and greater HR reactions. Patients with minSpO2 <90% had an adjusted odds ratio (OR) of 1.48 [95% confidence interval (CI): 1.09–2.00, p = 0.012) for AMI. Notably, minSpO2-induced elevated mean HR response (HRmean > 73 bpm) was significantly associated with AMI (OR 1.72, 95% CI: 1.32–2.23, p < 0.001). Patients with both severe minSpO2 (<90%) and elevated HRmean carried an additive OR of 2.65 (95% CI: 1.74–4.05, p < 0.001) for the risk of AMI after adjustment for potential confounders. A large total power spectrum specific to respiratory events was correlated with an adjusted OR of 0.61 for AMI risk.

Conclusion

Patients with substantial HR reactions to OSA-induced oxygen nadir and restricted cardiac cycle shifting to respiratory events were likely at increased risk of developing AMI. Detection of nocturnal HR response to hypoxemia may help improve cardiovascular risk stratification.

Frequent cyclic variation of heart rate is associated with left ventricular diastolic dysfunction in patients without ischemia

BACKGROUND

Cyclic variation of heart rate (CVHR) associated with sleep-disordered breathing reflects cardiac autonomic responses to apneic/hypoxic stress. However, the association of CVHR with cardiac function is unclear.

METHODS

We investigated a total of 181 patients who underwent both 24-hour Holter electrocardiography (ECG) and quantitative gated single-photon emission computed tomography (SPECT) myocardial functional imaging, excluding patients with atrial fibrillation, myocardial infarction, structural heart disease, and implantable devices, from January 2017 to July 2018. The number of CVHR per hour (CVHR index) in sleeping-time Holter ECG was compared with the parameters of left ventricular (LV) systolic and diastolic functions assessed by cardiac SPECT functional imaging, peak filling rate (PFR), first-third mean filling rate (1/3 MFR), and time to peak filling rate (TTPF).

RESULTS

In all patients, the CVHR index was not associated with any parameters of cardiac functions. However, in a propensity score-matched subgroup of patients without ischemia (N = 39), the CVHR index was negatively correlated with PFR (r = -0.35, P < .05) and 1/3 MFR (r = -0.37, P < .05) but positively correlated with TTPF (r = 0.43, P < .01) and was not correlated with LV ejection fraction. Multivariate linear regression analysis revealed that high CVHR index was independently associated with LV diastolic dysfunction, even after adjusting for the relative wall thickness and LV mass index assessed by echocardiography.

CONCLUSION

These results indicate that the high frequency of CVHR in sleeping time is associated with LV diastolic dysfunction in nonischemic patients, irrespective of LV geometry.

Assessment of autonomic function by long-term heart rate variability: beyond the classical framework of LF and HF measurements

In the assessment of autonomic function by heart rate variability (HRV), the framework that the power of high-frequency component or its surrogate indices reflects parasympathetic activity, while the power of low-frequency component or LF/HF reflects sympathetic activity has been used as the theoretical basis for the interpretation of HRV. Although this classical framework has contributed greatly to the widespread use of HRV for the assessment of autonomic function, it was obtained from studies of short-term HRV (typically 5‑10 min) under tightly controlled conditions. If it is applied to long-term HRV (typically 24 h) under free-running conditions in daily life, erroneous conclusions could be drawn. Also, long-term HRV could contain untapped useful information that is not revealed in the classical framework. In this review, we discuss the limitations of the classical framework and present studies that extracted autonomic function indicators and other useful biomedical information from long-term HRV using novel approaches beyond the classical framework. Those methods include non-Gaussianity index, HRV sleep index, heart rate turbulence, and the frequency and amplitude of cyclic variation of heart rate.

Utility of cyclic variation of heart rate score as a screening tool for sleep-disordered breathing in patients with heart failure

STUDY OBJECTIVES

Patients with sleep-disordered breathing have cyclic variation of heart rate (CVHR) in response to respiratory events. However, limited data are available regarding the utility of CVHR as a screening tool for sleep-disordered breathing in patients with mixed heart failure (HF) and those without HF.

METHODS

We enrolled consecutive patients with and without HF who underwent full polysomnographies with simultaneous Holter electrocardiogram monitoring. We determined the temporal position of the individual dips comprising the CVHR score using time-domain methods.

RESULTS

The data of 101 patients, including 70 with and 31 without HF, were analyzed. The CVHR score was significantly correlated with the apnea-hypopnea index (r = .667, P < .001) and limits of agreement between the apnea-hypopnea index and CVHR score were -21.8 to 35.2. The receiver operating characteristic analysis demonstrated that the CVHR score (best cut-off of 23.5 events/h) identified severe sleep-disordered breathing with a sensitivity of 83.3%, specificity of 79.5%, and the area under the curve of 0.856. In addition, there was no interaction between the presence or absence of HF and the apnea-hypopnea index-CVHR score relationship (P = .323).

CONCLUSIONS

The CVHR score, determined by Holter electrocardiogram monitoring, is a useful tool for evaluating sleep-disordered breathing even in patients with mixed HF and patients without HF.

CITATION

Yatsu S, Kasai T, Naito R, et al. Utility of cyclic variation of heart rate score as a screening tool for sleep-disordered breathing in patients with heart failure. J Clin Sleep Med. 2021;17(11):2187-2196.

Night-to-night variability of sleep apnea detected by cyclic variation of heart rate during long-term continuous ECG monitoring

Background

Sleep apnea is common in patients with cardiovascular disease and is a factor that worsens prognosis. Holter 24‐h ECG screening for sleep apnea is beneficial in the care of these patients, but due to high night‐to‐night variability of sleep apnea, it can lead to misdiagnosis and misclassification of disease severity.

Methods

To investigate the long‐term dynamic behavior of sleep apnea, seven‐day ECGs recorded with a patch ECG recorder in 120 patients were analyzed for the cyclic variation of heart rate (CVHR) during sleep periods as determined by a built‐in three‐axis accelerometer.

Results

The frequency of CVHR (Fcv) showed considerable night‐to‐night variability (coefficient of variance, 66 ± 35%), which was consistent with the night‐to‐night variability in apnea‐hypopnea index and oxygen desaturation index reported in earlier studies. In patients with presumed moderate‐to‐severe sleep apnea (Fcv > 15 cph at least one night), it was missed on 62% of nights, and on at least one night in 88% of patients. The CV of Fcv was negatively correlated with the average of Fcv, suggesting that patients with mild sleep apnea show greater night‐to‐night variability and would benefit from long‐term assessment. The average Fcv was higher in the supine position, but the night‐to‐night variability was not explained by the night‐to‐night variability of time spent in the supine position.

Conclusions

CVHR analysis of long‐term ambulatory ECG recordings is useful for improving the reliability of screening for sleep apnea without placing an extra burden on patients with cardiovascular disease and their care.

Sleep Disordered Breathing and Cardiovascular Diseases

Sleep disordered breathing (SDB), which causes sleep deprivation, intermittent hypoxia, and negative intrathoracic pressure swings, can be accompanied by other harmful pathophysiologies relating to cardiovascular diseases (CVD), including sudden death, atrial fibrillation, stroke, and coronary artery disease leading to heart failure. Continuous positive airway pressure (CPAP) therapy for SDB has been reported to provide favorable effects such as lowered systemic blood pressure and improved endothelial function. However, in recent randomized controlled trials, CPAP has failed to demonstrate its beneficial prognostic impact on the primary or secondary setting of CVD. In this review article, we describe the characteristics of SDB complicated with CVD, the prognostic impacts of SDB in CVD, and the beneficial effects of CPAP on CVD.

Heart failure and sleep disordered breathing

Sleep-disordered breathing (SDB) is frequently observed in patients with heart failure (HF), and complex pathologic conditions exist between both conditions. In this review article, we describe the characteristics of SDB complicated with HF, the prognostic impact of SDB in HF patients, and the favorable effects of positive airway pressure in HF patients with SDB.

A REVIEW OF ECG-BASED DIAGNOSIS SUPPORT SYSTEMS FOR OBSTRUCTIVE SLEEP APNEA

Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy.