Study of the oscillatory breathing pattern in elderly patients

Cardiorespiratory Phase Synchronization (CRPS) is the manifestation of the non-linear coupling between the cardiac and the respiratory systems, different to the Respiratory Sinus Arrythmia (RSA). This takes place when the heartbeats occur at the same relative phase of the breathing, during a succession of respiratory cycles. In this study, we investigated the CRPS in 45 elderly patients admitted to the semi-critical unit of a hospital. The patients were classified according to their respiratory state as non-Periodic Breathing (nPB), Periodic Breathing (PB) and Cheyne-Stokes Respiration (CSR). The phase synchrogram between the electrocardiographic and respiratory signals was computed using the Hilbert transform technique. A continuous measure of the CRPS was obtained from the synchrogram, and was characterized by the average duration of synchronized epochs (A vgDurSync), the percentage of synchronized time (%Sync), the number of synchronized epochs (NumSync), and the frequency ratio between the cardiac and respiratory oscillators (FreqRat). These measures were studied using two different thresholds (0.1 and 0.05) for the amplitude of the synchronization and a minimum duration threshold of 10s. According to the results, the AvgDurSync and %Sync had a decreasing trend in patients with breathing periodicity. In addition, CSR patients presented the lowest values A vgDurSync and %Sync. Therefore, the CRPS method could be a useful tool for characterizing periodic respiratory patterns in elderly patients, which might be related to chronic heart failure. Clinical Relevance- This study analyzes the synchronization between cardiac and respiratory systems in elderly patients with a possible progressive decompensation in the cardiac function.

Cheyne-Stokes Respiration and Congestive Heart Failure: Are Oxygen Stores the Critical Factor?

To assess the impact of acute hypoxia-induced periodic breathing on respiratory efficiency in young healthy males. 20 healthy male (median (IQR) age: 24 (22 to 25) years; body mass index: 22.5 (20.9 to 24.9) kg/m2)-no sleep-disordered breathing in normoxia-underwent polysomnography in normobaric hypoxia simulating 3500 m altitude. Measurements included oesophageal pressure-time curve, airflow and exhaled fraction of carbon dioxide (CO2). Inspiratory pressure-time product (iPTP, index of respiratory muscle effort) was calculated from oesophageal pressure. Physiological dead-space volume, ventilation ([Formula: see text]Dest) and alveolar ventilation ([Formula: see text]Aest) were calculated from exhaled fractions of CO2 (Bohr equation). Within-subject comparisons of periodic (PB) and regular breathing (RB) periods were made using the Wilcoxon signed-rank test. In hypoxia, 17 participants had sufficient (>3 min) breathing periods of both periodic and non-periodic breathing for analysis. The median (IQR) Apnoea-Hypopnoea Index was 76.9 (62.8 to 122.7)/hour and participants spent 40.8% (27.3% to 64.6%) of sleep in periodic breathing (n=17). Tidal volume was greater in periodic breathing (n=17, median (IQR), PB vs RB; 0.815 (0.636 to 0.928) L vs 0.633 (0.517 to 0.673) L, p<0.001). Physiological dead space was similar in both breathing patterns (n=17, PB vs RB, 0.172 (0.160 to 0.196) L vs 0.184 (0.162 to 0.205) L, p=0.782). Respiratory rate was lower in periodic breathing (n=17; PB vs RB; 10.6 (9.8 to 13.6)/min vs 16.8 (15.4 to 17.7)/min, p<0.001). [Formula: see text]Dest was lower in periodic breathing (n=17; PB vs RB; 1.9 (1.7 to 2.2) L/min vs 3.0 (2.6 to 3.4) L/min, p<0.001), while [Formula: see text]Aest remained similar (n=17, PB vs RB; 6.9 (6.3 to 7.4) L/min vs 7.3 (6.8 to 7.7) L/min, p=0.102). iPTP/min was lower in PB (n=14, PB vs RB; 185.4 (154.3 to 203.8) cmH2O×s/min vs 221.5 (189.2 to 291.9) cmH2O×s/min, p=0.002). Periodic breathing in acute normobaric hypoxia reduces inspiratory effort without impairing alveolar ventilation, suggesting an adaptive mechanism to optimise respiratory efficiency in young healthy males.

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.

Transvenous Phrenic Nerve Stimulation is Safe and Effective in Patients with Heart Failure and Cheyne-stokes Respiration

Due to the increasing elderly population and the extensive number of comorbidities that affect them, studies are required to determine future increments in admission to emergency departments. Some of these studies could focus on the relation between chronic diseases and breathing pattern in elderly patients. Variations in the fractal properties of respiratory signals can be associated with several diseases. To determine the relationship between these variations and breathing patterns, and to quantify the fractal properties of respiratory flow signals, we estimated the Hurst exponent (H). Detrended fluctuation analysis (DFA) and discrete wavelet transform-based estimation (DWTE) methods were applied. The estimation methods were analyzed using simulated data series generated by fractional Gaussian noise. 43 elderly patients (19 patients with a non-periodic breathing pattern - nPB, and 24 patients with a periodic breathing pattern - PB) were studied. The results were evaluated according to the length of data and the number of averaged data series used to obtain a good estimation. The DWTE method estimated the respiratory flow signals better than the DFA method, and obtained Hurst values clustered by group. We found significant differences in the H exponent (p = 0.002) between PB and nPB patients, which showed different behavior in the fractal properties.

There are no standard therapies for the management of central sleep apnea (CSA). Either positive pressure therapy (PAP) or supplemental oxygen (O(2)) may stabilize respiration in CSA by reducing ventilatory chemoresponsiveness. Additionally, increasing opioid use and the presence of comorbid conditions in US veterans necessitates investigations into alternative titration protocols to treat CSA. The goal was to report on the effectiveness of titration with PAP, used alone or in conjunction with O(2), for the management of CSA associated with varying comorbidities and opioid use. This was a retrospective chart review over 3 years, performed at a VA sleep disorders center. The effects of CPAP, CPAP+O(2), and BPAP+O(2), used in a step-wise titration protocol, on consecutive patients diagnosed with CSA were studied. CSA was diagnosed in 162 patients. The protocol was effective in eliminating CSA (CAI ≤ 5/h) in 84% of patients. CPAP was effective in 48%, while CPAP+O(2) combination was effective in an additional 25%, and BPAP+O(2) in 11%. The remaining 16% were non-responders. Forty-seven patients (29%) were on prescribed opioid therapy for chronic pain, in whom CPAP, CPAP+O(2), or BPAP+O(2) eliminated CSA in 54%, 28%, and 10% cases, respectively. CPAP, CPAP+O(2), and BPAP+O(2) each produced significant declines in the AHI, CAI, and arousal index, and an increase in the SpO(2). The data demonstrate that using a titration protocol with CPAP and then PAP with O(2) effectively eliminates CSA in individuals with underlying comorbid conditions and prescription opioid use. Comparative studies with other therapeutic modalities are required.

A New Straw in the Genesis of Cheyne-Stokes Respiration

Breathing pattern as periodic breathing (PB) in chronic heart failure (CHF) is associated with poor prognosis and high mortality risk. This work investigates the significance of a number of time domain parameters for characterizing respiratory flow cycle morphology in patients with CHF. Thus, our primary goal is to detect PB pattern and identify patients at higher risk. In addition, differences in respiratory flow cycle morphology between CHF patients (with and without PB) and healthy subjects are studied. Differences between these parameters are assessed by investigating the following three classification issues: CHF patients with PB versus with non-periodic breathing (nPB), CHF patients (both PB and nPB) versus healthy subjects, and nPB patients versus healthy subjects. Twenty-six CHF patients (8/18 with PB/nPB) and 35 healthy subjects are studied. The results show that the maximal expiratory flow interval is shorter and with lower dispersion in CHF patients than in healthy subjects. The flow slopes are much steeper in CHF patients, especially for PB. Both inspiration and expiration durations are reduced in CHF patients, mostly for PB. Using the classification and regression tree technique, the most discriminant parameters are selected. For signals shorter than 1min, the time domain parameters produce better results than the spectral parameters, with accuracies for each classification of 82/78, 89/85, and 91/89%, respectively. It is concluded that morphologic analysis in the time domain is useful, especially when short signals are analyzed.

Clinical characteristics of hospitalized heart failure patients with daytime cheyne-stokes respiration

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.

Aging population is a major concern that is reflected in the increase of chronic diseases. Heart Failure (HF) is one of the most common chronic diseases of elderly people that is punctuated with acute episodes, which result in hospitalization. The periodic modulation of the amplitude of the breathing pattern is proved to be one of the multiple symptoms of an acute episode, and thus, the features extracted from its characterization contribute in the improvement of the first diagnosis of the clinical practice. The main objective of this study is to evaluate if the features extracted from the breathing pattern along with common clinical variables are reliable enough to detect Periodic Breathing (PB). A dataset of 44 elderly patients containing clinical information and a short record of electrocardiogram and respiratory flow signal was used to train two machine learning classification methods: Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). All the available clinical parameters within the dataset along with the parameters characterizing the respiratory pattern were used to classify the observations into two groups. SVM classification was optimized and performed using a = -8 and C = 10.04 giving an accuracy of 88.2 % sensitivity of 90 % and specificity of 85.7 % Similar results were achieved with LDA classifying with an accuracy of 82.4 %, a sensitivity of 81.8% and specificity of 83.3 % PB has been accurately detected using both classifiers.

Patients with acute heart failure (AHF) often experience dyspnea, and monitoring and quantifying their breathing patterns can provide reference information for disease and prognosis assessment. In this study, 39 AHF patients and 24 healthy subjects were included. Nighttime chest-abdominal respiratory signals were collected using wearable devices, and the differences in nocturnal breathing patterns between the two groups were quantitatively analyzed. Compared with the healthy group, the AHF group showed a higher mean breathing rate (BR_mean) [(21.03 ± 3.84) beat/min vs. (15.95 ± 3.08) beat/min, P < 0.001], and larger R_RSBI_cv [70.96% (54.34%-104.28)% vs. 58.48% (45.34%-65.95)%, P = 0.005], greater AB_ratio_cv [(22.52 ± 7.14)% vs. (17.10 ± 6.83)%, P = 0.004], and smaller SampEn (0.67 ± 0.37 vs. 1.01 ± 0.29, P < 0.001). Additionally, the mean inspiratory time (TI_mean) and expiration time (TE_mean) were shorter, TI_cv and TE_cv were greater. Furthermore, the LBI_cv was greater, while SD1 and SD2 on the Poincare plot were larger in the AHF group, all of which showed statistically significant differences. Logistic regression calibration revealed that the TI_mean reduction was a risk factor for AHF. The BR_ mean demonstrated the strongest ability to distinguish between the two groups, with an area under the curve (AUC) of 0.846. Parameters such as breathing period, amplitude, coordination, and nonlinear parameters effectively quantify abnormal breathing patterns in AHF patients. Specifically, the reduction in TI_mean serves as a risk factor for AHF, while the BR_mean distinguishes between the two groups. These findings have the potential to provide new information for the assessment of AHF patients.

Impact of Sleeping Angle on the Upper Airway and Pathogenesis of Cheyne Stokes Respiration

It has been hypothesized that the periodic breathing (PB) pattern often observed in chronic heart failure (CHF) patients (pts) originates from the instability of the feedback loop controlling ventilation, due to the prolonged circulation time of these pts. To test this hypothesis, we studied the relationship between the instantaneous tidal volume (ITV) and O/sub 2/ saturation at the ear (SpO/sub 2/) in 24 CHF pts during sustained episodes of PB and 13 healthy subjects during voluntary PB. Some basic modeling assumptions about the respiratory control system were made and, following the hypothesis, a set of expected results formulated. The relationship between SpO/sub 2/ and ITV was analyzed by bivariate spectral analysis and the phase shift and time delay at the PB frequency estimated. The ITV oscillation in CHF patients always mirrored and anticipated the contemporary oscillation of SpO/sub 2/ with a phase shift of -145/spl plusmn/19/spl deg/ and a delay of -3.3 s. The oscillation of SpO/sub 2/ was also a delayed version of the ITV, with a lung-to-ear circulation time (LECT) of 27.9/spl plusmn/6 s. The SpO/sub 2/ oscillation of healthy subjects was simply a delayed version of the ITV due to LECT. The phase shift was 123/spl plusmn/37/spl deg/, with a corresponding LECT of 15.5/spl plusmn/5 s. Provided the ventilatory open loop gain of CHF patients with PB is /spl ges/1, the results of this study are in good agreement with theoretical expectation and support the instability hypothesis of PB in these pts.