Periodic Breathing in Heart Failure

Obese Patients Experience More Severe CSA than Non-Obese Patients

Objective: To investigate whether central sleep apnea (CSA) is associated with an increased risk of obesity. Materials and methods: From 1 January 2000 to 31 December 2015, we screened 24,363 obese patients from the 2005 longitudinal health insurance database, which is part of the Taiwan National Health Insurance Research Database. From the same database, 97,452 non-obese patients were also screened out. Age, gender, and index dates were matched. Multiple logistic regression was used to analyze the previous exposure risk of obese and CSA patients. A p-value of <0.05 was considered significant. Results: Obese patients were more likely to be exposed to CSA than non-obese patients would (AOR = 2.234, 95% CI = 1.483–4.380, p < 0.001). In addition, the closeness of the exposure time to the index time is positively correlated with the severity of obesity and has a dose–response effect (CSA exposure < 1 year, AOR = 2.386; CSA exposure ≥ 1 year and <5 years, AOR = 1.725; CSA exposure time ≥ 5 years, AOR = 1.422). The CSA exposure time of obese patients was 1.693 times that of non-obese patients. Longer exposure time is associated with more severe obesity and has a dose-response effect (CSA exposure < 1 year, AOR = 1.420; CSA exposure ≥ 1 year and <5 years, AOR = 2.240; CSA ≥ 5 years, AOR = 2.863). Conclusions: In this case-control study, patients with CSA had a significantly increased risk of obesity. Long-term exposure to CSA and obesity is more likely and has a dose-response effect.

Analysis of Exercise-Induced Periodic Breathing Using an Autoregressive Model and the Hilbert-Huang Transform

Evaluation of exercise-induced periodic breathing (PB) in cardiopulmonary exercise testing (CPET) is one of important diagnostic evidences to judge the prognosis of chronic heart failure cases. In this study, we propose a method for the quantitative analysis of measured ventilation signals from an exercise test. We used an autoregressive (AR) model to filter the breath-by-breath measurements of ventilation from exercise tests. Then, the signals before reaching the most ventilation were decomposed into intrinsic mode functions (IMF) by using the Hilbert-Huang transform (HHT). An IMF represents a simple oscillatory pattern which catches a part of original ventilation signal in different frequency band. For each component of IMF, we computed the number of peaks as the feature of its oscillatory pattern denoted by Δ_i. In our experiment, 61 chronic heart failure patients with or without PB pattern were studied. The computed peaks of the third and fourth IMF components, Δ₃ and Δ₄, were statistically significant for the two groups (both p values < 0.02). In summary, our study shows a close link between the HHT analysis and level of intrinsic energy for pulmonary ventilation. The third and fourth IMF components are highly potential to indicate the prognosis of chronic heart failure.

Influence of Thoracic Fluid Compartments on Pulmonary Congestion in Chronic Heart Failure

INTRODUCTION

Pulmonary congestion is a common finding of heart failure (HF), but it remains unclear how pulmonary and heart blood volumes (V_p and V_h, respectively) and extravascular lung water (EVLW) change in stable HF and affect lung function.

METHODS

Fourteen patients with HF (age 68 ± 11 y, LVEF 33 ± 8%) and 12 control subjects (age 65 ± 9 y) were recruited. A pulmonary function test, thoracic computerized tomographic (CT) scan, and contrast perfusion scan were performed. From the thoracic scan, a histogram of CT attenuation of lung tissue was generated and skew, kurtosis, and full-width half-max (FWHM) calculated as surrogates of EVLW. Blood volumes were calculated from the transit time of the contrast through the great vessels of the heart.

RESULTS

Patients with HF had greater V_p and V_h (V_p 0.55 ± 0.21 L vs 0.41 ± 0.13 L; V_h 0.53 ± 0.33 L vs 0.40 ± 0.15 L) and EVLW (skew 3.2 ± 0.5 vs 3.7 ± 0.7; kurtosis 19.4 ± 6.6 vs 25.9 ± 9.4; FWHM 73 ± 13 HU vs 59 ± 9 HU). Spirometric measures were decreased in HF (percentage of predicted: forced vital capacity 86 ± 17% vs 104 ± 9%; forced expiratory volume in 1 second 83 ± 20% vs 105 ± 11%; maximal mid-expiratory flow 82 ± 42% vs 115 ± 43%). V_p was associated with decreased expiratory flows, and EVLW was associated with decreased lung volumes.

CONCLUSIONS

Congestion in stable patients with HF includes expanded V_p and V_h and increased EVLW associated with reductions in lung volumes and expiratory flows.

Leptin deficiency promotes central sleep apnea in patients with heart failure

BACKGROUND

Leptin-deficient animals hyperventilate. Leptin expression by adipocytes is attenuated by atrial natriuretic peptide (ANP). Increased circulating natriuretic peptides (NPs) are associated with an increased risk of central sleep apnea (CSA). This study tested whether serum leptin concentration is inversely correlated to NP concentration and decreased in patients with heart failure (HF) and CSA.

METHODS

Subjects with HF (N = 29) were studied by measuring leptin, NPs, CO2 chemosensitivity (Δminute ventilation [V.e]/Δpartial pressure of end-tidal CO2 [Petco2]), and ventilatory efficiency (V.e/CO2 output [V.co2]) and were classified as CSA or no sleep-disordered breathing by polysomnography. CSA was defined as a central apnea-hypopnea index ≥ 15. The Student t test, Mann-Whitney U test, and logistic regression were used for analysis, and data were summarized as mean ± SD; P &lt; .05 was considered significant.

RESULTS

Subjects with CSA had higher ANP and brain natriuretic peptide (BNP) concentrations (P &lt; .05), ΔV.e/ΔPetco2 (2.39 ± 1.03 L/min/mm Hg vs 1.54 ± 0.35 L/min/mm Hg, P = .01), and V.e/V.co2 (43 ± 9 vs 34 ± 7, P &lt; .01) and lower leptin concentrations (8 ± 10.7 ng/mL vs 17.1 ± 8.8 ng/mL, P &lt; .01). Logistic regression analysis (adjusted for age, sex, and BMI) demonstrated leptin (OR = 0.07; 95% CI, 0.01-0.71; P = .04) and BNP (OR = 4.45; 95% CI, 1.1-17.9; P = .05) to be independently associated with CSA.

CONCLUSIONS

In patients with HF and CSA, leptin concentration is low and is inversely related to NP concentration. Counterregulatory interactions of leptin and NP may be important in ventilatory control in HF.

Clinical usefulness of response profiles to rapidly incremental cardiopulmonary exercise testing

The advent of microprocessed "metabolic carts" and rapidly incremental protocols greatly expanded the clinical applications of cardiopulmonary exercise testing (CPET). The response normalcy to CPET is more commonly appreciated at discrete time points, for example, at the estimated lactate threshold and at peak exercise. Analysis of the response profiles of cardiopulmonary responses at submaximal exercise and recovery, however, might show abnormal physiologic functioning which would not be otherwise unraveled. Although this approach has long been advocated as a key element of the investigational strategy, it remains largely neglected in practice. The purpose of this paper, therefore, is to highlight the usefulness of selected submaximal metabolic, ventilatory, and cardiovascular variables in different clinical scenarios and patient populations. Special care is taken to physiologically justify their use to answer pertinent clinical questions and to the technical aspects that should be observed to improve responses' reproducibility and reliability. The most recent evidence in favor of (and against) these variables for diagnosis, impairment evaluation, and prognosis in systemic diseases is also critically discussed.

Study of the oscillatory breathing pattern in elderly patients

Some of the most common clinical problems in elderly patients are related to diseases of the cardiac and respiratory systems. Elderly patients often have altered breathing patterns, such as periodic breathing (PB) and Cheyne-Stokes respiration (CSR), which may coincide with chronic heart failure. In this study, we used the envelope of the respiratory flow signal to characterize respiratory patterns in elderly patients. To study different breathing patterns in the same patient, the signals were segmented into windows of 5 min. In oscillatory breathing patterns, frequency and time-frequency parameters that characterize the discriminant band were evaluated to identify periodic and non-periodic breathing (PB and nPB). In order to evaluate the accuracy of this characterization, we used a feature selection process, followed by linear discriminant analysis. 22 elderly patients (7 patients with PB and 15 with nPB pattern) were studied. The following classification problems were analyzed: patients with either PB (with and without apnea) or nPB patterns, and patients with CSR versus PB, CSR versus nPB and PB versus nPB patterns. The results showed 81.8% accuracy in the comparisons of nPB and PB patients, using the power of the modulation peak. For the segmented signal, the power of the modulation peak, the frequency variability and the interquartile ranges provided the best results with 84.8% accuracy, for classifying nPB and PB patients.

Correntropy-based nonlinearity test applied to patients with chronic heart failure

In this study we propose the correntropy function as a discriminative measure for detecting nonlinearities in the respiratory pattern of chronic heart failure (CHF) patients with periodic or nonperiodic breathing pattern (PB or nPB, respectively). The complexity seems to be reduced in CHF patients with higher risk level. Correntropy reflects information on both, statistical distribution and temporal structure of the underlying dataset. It is a suitable measure due to its capability to preserve nonlinear information. The null hypothesis considered is that the analyzed data is generated by a Gaussian linear stochastic process. Correntropy is used in a statistical test to reject the null hypothesis through surrogate data methods. Various parameters, derived from the correntropy and correntropy spectral density (CSD) to characterize the respiratory pattern, presented no significant differences when extracted from the iteratively refined amplitude adjusted Fourier transform (IAAFT) surrogate data. The ratio between the powers in the modulation and respiratory frequency bands R was significantly different in nPB patients, but not in PB patients, which reflects a higher presence of nonlinearities in nPB patients than in PB patients.

Breathing pattern characterization in chronic heart failure patients using the respiratory flow signal

This study proposes a method for the characterization of respiratory patterns in chronic heart failure (CHF) patients with periodic breathing (PB) and nonperiodic breathing (nPB), using the flow signal. Autoregressive modeling of the envelope of the respiratory flow signal is the starting point for the pattern characterization. Spectral parameters extracted from the discriminant frequency band (DB) are used to characterize the respiratory patterns. For each classification problem, the most discriminant parameter subset is selected using the leave-one-out cross-validation technique. The power in the right DB provides an accuracy of 84.6% when classifying PB vs. nPB patterns in CHF patients, whereas the power of the DB provides an accuracy of 85.5% when classifying the whole group of CHF patients vs. healthy subjects, and 85.2% when classifying nPB patients vs. healthy subjects.

Time-varying respiratory pattern characterization in chronic heart failure patients and healthy subjects

Patients with chronic heart failure (CHF) with periodic breathing (PB) and Cheyne-Stokes respiration (CSR) tend to exhibit higher mortality and poor prognosis. This study proposes the characterization of respiratory patterns in CHF patients and healthy subjects using the envelope of the respiratory flow signal, and autoregressive (AR) time-frequency analysis. In time-varying respiratory patterns, the statistical distribution of the AR coefficients, pole locations, and the spectral parameters that characterize the discriminant band are evaluated to identify typical breathing patterns. In order to evaluate the accuracy of this characterization, a feature selection process followed by linear discriminant analysis is applied. 26 CHF patients (8 patients with PB pattern and 18 with non-periodic breathing pattern (nPB)) are studied. The results show an accuracy of 83.9% with the mean of the main pole magnitude and the mean of the total power, when classifying CHF patients versus healthy subjects, and 83.3% for nPB versus healthy subjects. The best result when classifying CHF patients into PB and nPB was an accuracy of 88.9%, using the coefficient of variation of the first AR coefficient and the mean of the total power.

Correntropy-based spectral characterization of respiratory patterns in patients with chronic heart failure

A correntropy-based technique is proposed for the characterization and classification of respiratory flow signals in chronic heart failure (CHF) patients with periodic or nonperiodic breathing (PB or nPB, respectively) and healthy subjects. The correntropy is a recently introduced, generalized correlation measure whose properties lend themselves to the definition of a correntropy-based spectral density (CSD). Using this technique, both respiratory and modulation frequencies can be reliably detected at their original positions in the spectrum without prior demodulation of the flow signal. Single-parameter classification of respiratory patterns is investigated for three different parameters extracted from the respiratory and modulation frequency bands of the CSD, and one parameter defined by the correntropy mean. The results show that the ratio between the powers in the modulation and respiratory frequency bands provides the best result when classifying CHF patients with either PB or nPB, yielding an accuracy of 88.9%. The correntropy mean offers excellent performance when classifying CHF patients versus healthy subjects, yielding an accuracy of 95.2% and discriminating nPB patients from healthy subjects with an accuracy of 94.4%.

Characterization of periodic and non-periodic breathing pattern in chronic heart failure patients

Periodic breathing (PB) has a high prevalence in chronic heart failure (CHF) patients with mild to moderate symptoms and poor ventricular function. This work proposes the analysis and characterization of the respiratory pattern to identify periodic breathing pattern (PB) and non-periodic breathing pattern (nPB) through the respiratory flow signal. The respiratory pattern analysis is based on the extraction and the study of the flow envelope signal. The flow envelope signal is modelled by an autoregressive model (AR) whose coefficients would characterize the respiratory pattern of each group. The goodness of the characterization is evaluated through a linear and non linear classifier applied to the AR coefficients. An adaptive feature selection is used before the linear and non linear classification, employing leave-one-out cross validation technique. With linear classification the percentage of well classified patients (8 PB and 18 nPB patients) is 84.6% using the statistically significant coefficients whereas with non linear classification, the percentage of well classified patients increase to more than 92% applying the best subset of coefficients extracted by a forward selection algorithm.

Correntropy-based analysis of respiratory patterns in patients with chronic heart failure

A correntropy-based technique is proposed for the analysis and characterization of respiratory flow signals in chronic heart failure (CHF) patients with both periodic and nonperiodic breathing (PB and nPB), and healthy subjects. Correntropy is a novel similarity measure which provides information on temporal structure and statistical distribution simultaneously. Its properties lend itself to the definition of the correntropy spectral density (CSD). An interesting result from CSD-based spectral analysis is that both the respiratory frequency and modulation frequency can be detected at their original positions in the spectrum without prior demodulation of the flow signal. The respiratory pattern is characterized by a number of spectral parameters extracted from the respiratory and modulation frequency bands. The results show that the power of the modulation frequency band offers excellent performance when classifying CHF patients versus healthy subjects, with an accuracy of 95.3%, and nPB patients versus healthy subjects with 90.7%. The ratio between the power in the modulation and respiration frequency bands provides the best results classifying CHF patients into PB and nPB, with an accuracy of 88.9%.

Exercise oscillatory ventilation: instability of breathing control associated with advanced heart failure

BACKGROUND

Instability of breathing control due to heart failure (HF) manifests as exercise oscillatory ventilation (EOV). Prior descriptions of patients with EOV have not been controlled and have been limited to subjects with left ventricular ejection fraction (LVEF) of <or= 0.40. The aim of this study was to compare clinical characteristics including ventilatory responses of subjects with EOV to those of control subjects with HF matched for LVEF.

METHODS

Subjects (n = 47) were retrospectively identified from 1,340 consecutive patients referred for cardiopulmonary exercise testing. Study inclusion required EOV without consideration of LVEF while control subjects (n = 47) were composed of HF patients with no EOV matched for LVEF. Characteristics for each group were summarized and compared.

RESULTS

For EOV subjects, the mean LVEF was 0.37 (range, 0.11 to 0.70), and 19 subjects (41%) had an LVEF of >or= 0.40. Compared to control subjects, EOV subjects had increased left atrial dimension, mitral E-wave velocity, and right heart pressures as well as decreased exercise tidal volume response, functional capacity, rest and exercise end-tidal carbon dioxide, and increased ventilatory equivalent for carbon dioxide and dead space ventilation (all p < 0.05). Multivariate analysis demonstrated atrial fibrillation (odds ratio, 6.7; p = 0.006), digitalis therapy (odds ratio, 0.27; p = 0.02), New York Heart Association class (odds ratio, 3.5; p = 0.0006), rest end-tidal carbon dioxide (odds ratio, 0.87; p = 0.005), and peak heart rate (odds ratio, 0.98; p = 0.02) were independently associated with EOV.

CONCLUSIONS

Patients with EOV have clinical characteristics and exercise ventilatory responses consistent with more advanced HF than patients with comparable LV systolic function; EOV may occur in HF patients with an LVEF of >or= 0.40.