Search Results (13)

Background: Sleep-disordered breathing (SDB), particularly Cheyne–Stokes respiration (CSR), is highly prevalent among patients hospitalized with acute decompensated heart failure (ADHF) and is associated with worse clinical outcomes. Sodium-glucose cotransporter-2 inhibitors (SGLT2i) have demonstrated cardiorenal benefits in heart failure, but their effects on nocturnal respiratory parameters remain underexplored. Objectives: This study aims to evaluate the impact of SGLT2i therapy on key respiratory and cardiac indices including CSR burden, oxygenation, and right heart function in patients with ADHF and reduced left ventricular ejection fraction. Methods: In this single-center prospective cohort study, 60 patients with ADHF, LVEF < 40%, and a baseline apnea–hypopnea index (AHI) > 5 were assessed before and three months after the initiation of SGLT2i therapy. Sleep respiratory parameters were measured using home polygraphy (ApneaLink^TM), while cardiac and renal indices were evaluated by echocardiography, NT-proBNP, and the estimated glomerular filtration rate (eGFR). Structural and functional echocardiographic changes were analyzed both at baseline and following the 3-month treatment period. Patient-reported outcomes were assessed using the Epworth Sleepiness Scale (ESS) and Kansas City Cardiomyopathy Questionnaire (KCCQ). Results: After 3 months of SGLT2i therapy, significant improvements were observed in daytime sleepiness (ESS: −2.68 points; p < 0.001), CSR index (−5.63 events/h; p < 0.001), AHI (−3.07 events/h; p < 0.001), ODI (−6.11 events/h; p < 0.001), and mean nocturnal SpO₂ (+1.95%; p < 0.001). KCCQ scores increased by 9.16 points (p < 0.001), indicating improved quality of life. Cardiac assessments revealed reductions in NT-proBNP (−329.6 pg/mL; p < 0.001) and E/e′ ratio (−1.08; p < 0.001), with no significant change in LVEF or chamber dimensions. Right ventricular function improved, as evidenced by the increased TAPSE/sPAP ratio (+0.018; p < 0.001). Renal function remained stable, with a non-significant upward trend in eGFR. Conclusions: This exploratory study suggests that SGLT2 inhibitors may be associated with the attenuation of Cheyne–Stokes respiration and an improvement in right heart function in patients with ADHF, warranting further investigation in controlled trials. These findings highlight the potential of SGLT2is to address overlapping cardio-respiratory dysfunction in this high-risk population. Full article

Central sleep apnea (CSA) is a heterogeneous group of sleep-related breathing disorders characterized by intermittent absence of respiratory effort during sleep. CSA results from impaired neurological signaling from the respiratory centers to the respiratory muscles, leading to airflow cessation for at least 10 s. Major causes include heart failure, opioid use, central neurological disorders, and altitude exposure. This review outlines the pathophysiology of CSA, emphasizing ventilatory instability and brainstem dysfunction as key mechanisms. It details the classification of CSA subtypes, including Cheyne–Stokes respiration, high-altitude CSA, and drug-induced CSA. Clinical manifestations range from excessive daytime sleepiness to cardiovascular complications. Diagnostic approaches encompass polygraphy, polysomnography, and various laboratory tests to evaluate comorbidities. Treatment requires a multidisciplinary approach, addressing underlying conditions while utilizing positive airway pressure (PAP) therapy, adaptive servo-ventilation (ASV), supplemental oxygen, and pharmacological interventions. Newer modalities, such as phrenic nerve stimulation, offer promising outcomes for CSA management. This review underscores the necessity of an individualized, interdisciplinary strategy to improve patient outcomes in CSA. Full article

Background: Impairment in autonomic activity is a prognostic marker in patients with heart failure (HF), and its involvement has been suggested in cardiovascular complications of obstructive sleep apnea syndrome (OSAS) and Cheyne–Stokes respiration (CSR). This prospective observational study aims to investigate the implications of sleep-disordered breathing (SDB) on hemodynamic regulation and autonomic activity in chronic HF patients. Methods: Chronic HF patients, providing confirmation of reduced ejection fraction (≤35%), underwent polysomnography, real-time hemodynamic, heart rate variability (HRV), and baroreceptor reflex sensitivity (BRS) assessments using the Task Force Monitor. BRS was assessed using the sequencing method during resting conditions and stress testing. Results: Our study population (n = 58) was predominantly male (41 vs. 17), with a median age of 61 (±11) yrs and a median BMI of 30 (±5) kg/m². Patients diagnosed with CSR were 13.8% (8/58) and 50.0% (29/58) with OSAS. No differences in the real-time assessment of hemodynamic regulation, heart rate variability, or baroreceptor reflex function were found between patients with OSAS, CSR, and patients without SDB. A subgroup analysis of BRS and HRV in patients with severe SDB (AHI > 30/h) and without SDB (AHI < 5) revealed numerically reduced BRS and increased LF/HF-RRI values under resting conditions, as well as during mental testing in patients with severe SDB. Patients with moderate-to-severe SDB had a shorter overall survival, which was, however, dependent upon age. Conclusions: Chronic HF patients with severe SDB may exhibit lower baroreceptor function and impaired cardiovascular autonomic function in comparison with HF patients without SDB. Full article

Objectives: We aimed to investigate the occurrence of sleep-related breathing disorders (SRBDs) in patients with chronic thromboembolic pulmonary hypertension (CTEPH) and addressed the effect of pulmonary hemodynamics and SRBD indices on the severity of nocturnal hypoxemia (NH). Methods: An overnight polysomnography (PSG) was conducted in patients with CTEPH, who were eligible for pulmonary endarterectomy. Pulmonary hemodynamics (mean pulmonary arterial pressure (mPAP), pulmonary arterial wedge pressure (PAWP), pulmonary vascular resistance (PVR) measured with right heart catheterization (RHC)), PSG variables (apnea–hypopnea index (AHI)), lung function and carbon monoxide diffusion capacity (DLCO) values, as well as demographics and comorbidities were entered into a logistic regression model to address the determinants of severe NH (nocturnal oxyhemoglobin saturation (SpO₂) < 90% under >20% of total sleep time (TST)). SRBDs were defined as obstructive sleep apnea (OSA; as an AHI ≥ 15 events/h), central sleep apnea with Cheyne–Stokes respiration (CSA–CSR; CSR pattern ≥ 50% of TST), obesity hypoventilation syndrome (OHS), and isolated sleep-related hypoxemia (ISRH; SpO₂ < 88% under >5 min without OSA, CSA, or OHS). Results: In all, 50 consecutive patients (34 men and 16 women; mean age 54.0 (SD 15.1) years) were included. The average mPAP was 43.8 (SD 16.8) mmHg. SRBD was observed in 40 (80%) patients, of whom 27 had OSA, 2 CSA–CSR, and 11 ISRH. None had OHS. Severe NH was observed in 31 (62%) patients. Among the variables tested, age (odds ratio (OR) 1.08, 95% confidence interval [CI] 1.01–1.15; p = 0.031), mPAP (OR 1.11 [95% CI 1.02–1.12; p = 0.012]), and AHI (OR 1.17 [95% CI 1.02–1.35; p = 0.031]) were independent determinants of severe NH. Conclusions: Severe NH is highly prevalent in patients with CTEPH. Early screening for SRBDs and intervention with nocturnal supplemental oxygen and/or positive airway pressure as well as pulmonary endarterectomy may reduce adverse outcomes in patients with CTEPH. Full article

Malnutrition frequently coexists with heart failure (HF), leading to series of negative consequences. Cheyne–Stokes respiration (CSR) is predominantly detected in patients with HF. However, the effect of CSR and malnutrition on the long-term prognosis of patients with acute decompensated HF (ADHF) remains unclear. We enrolled 162 patients with ADHF (median age, 62 years; 78.4% men). The presence of CSR was assessed using polysomnography and the controlling nutritional status score was assessed to evaluate the nutritional status. Patients were divided into four groups based on CSR and malnutrition. The primary outcome was all-cause mortality. In total, 44% of patients had CSR and 67% of patients had malnutrition. The all-cause mortality rate was 26 (16%) during the 35.9 months median follow-up period. CSR with malnutrition was associated with lower survival rates (log-rank p < 0.001). Age, hemoglobin, albumin, lymphocyte count, total cholesterol, triglyceride, low-density lipoprotein cholesterol, creatinine, estimated glomerular filtration rate, B-type natriuretic peptide, administration of loop diuretics, apnea-hypopnea index and central apnea-hypopnea index were significantly different among all groups (p < 0.05). CSR with malnutrition was independently associated with all-cause mortality. In conclusion, CSR with malnutrition is associated with a high risk of all-cause mortality in patients with ADHF. Full article

With the development of science and technology, transparent, non-invasive general computing is gradually applied to disease diagnosis and medical detection. Universal software radio peripherals (USRP) enable non-contact awareness based on radio frequency signals. Cheyne-Stokes respiration has been reported as a common symptom in patients with heart failure. Compared with the disadvantages of traditional detection equipment, a microwave sensing method based on channel state information (CSI) is proposed to qualitatively detect the normal breathing and Cheyne-Stokes breathing of patients with heart failure in a non-contact manner. Firstly, USRP is used to collect subjects’ respiratory signals in real time. Then the CSI waveform is filtered, smoothed and normalized, and the relevant features are defined and extracted from the signal. Finally, the machine learning classification algorithm is used to establish a recognition model to detect the Cheyne-Stokes respiration of patients with heart failure. The results show that the system accuracy of support vector machine (SVM) is 97%, which can assist medical workers to identify Cheyne-Stokes respiration symptoms of patients with heart failure. Full article

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20–30% of COVID patients require hospitalization, while almost 5–12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing abnormalities data. Subsequently, based on this data, a large dataset of simulated breathing abnormalities is generated using the curve fitting technique for developing a machine learning (ML) classification model. The advantages of generating simulated breathing abnormalities data are two-fold; it will help counter the daunting and time-consuming task of real-time data collection and improve the ML model accuracy. Several ML algorithms are exploited to classify eight breathing abnormalities: eupnea, bradypnea, tachypnea, Biot, sighing, Kussmaul, Cheyne–Stokes, and central sleep apnea (CSA). The performance of ML algorithms is evaluated based on accuracy, prediction speed, and training time for real-time breathing data and simulated breathing data. The results show that the proposed platform for real-time data classifies breathing patterns with a maximum accuracy of 97.5%, whereas by introducing simulated breathing data, the accuracy increases up to 99.3%. This work has a notable medical impact, as the introduced method mitigates the challenge of data collection to build a realistic model of a large dataset during the pandemic. Full article

Today’s rapid growth of elderly populations and aging problems coupled with the prevalence of obstructive sleep apnea (OSA) and other health related issues have affected many aspects of society. This has led to high demands for a more robust healthcare monitoring, diagnosing and treatments facilities. In particular to Sleep Medicine, sleep has a key role to play in both physical and mental health. The quality and duration of sleep have a direct and significant impact on people’s learning, memory, metabolism, weight, safety, mood, cardio-vascular health, diseases, and immune system function. The gold-standard for OSA diagnosis is the overnight sleep monitoring system using polysomnography (PSG). However, despite the quality and reliability of the PSG system, it is not well suited for long-term continuous usage due to limited mobility as well as causing possible irritation, distress, and discomfort to patients during the monitoring process. These limitations have led to stronger demands for non-contact sleep monitoring systems. The aim of this paper is to provide a comprehensive review of the current state of non-contact Doppler radar sleep monitoring technology and provide an outline of current challenges and make recommendations on future research directions to practically realize and commercialize the technology for everyday usage. Full article

An airflow in the first four generations of the tracheobronchial tree was simulated by the 1D model of incompressible fluid flow through the network of the elastic tubes coupled with 0D models of lumped alveolar components, which aggregates parts of the alveolar volume and smaller airways, extended with convective transport model throughout the lung and alveolar components which were combined with the model of oxygen and carbon dioxide transport between the alveolar volume and the averaged blood compartment during pathological respiratory conditions. The novel features of this work are 1D reconstruction of the tracheobronchial tree structure on the basis of 3D segmentation of the computed tomography (CT) data; 1D−0D coupling of the models of 1D bronchial tube and 0D alveolar components; and the alveolar gas exchange model. The results of our simulations include mechanical ventilation, breathing patterns of severely ill patients with the cluster (Biot) and periodic (Cheyne-Stokes) respirations and bronchial asthma attack. The suitability of the proposed mathematical model was validated. Carbon dioxide elimination efficiency was analyzed in all these cases. In the future, these results might be integrated into research and practical studies aimed to design cyberbiological systems for remote real-time monitoring, classification, prediction of breathing patterns and alveolar gas exchange for patients with breathing problems. Full article

The measurement of human respiratory signals is crucial in cyberbiological systems. A disordered breathing pattern can be the first symptom of different physiological, mechanical, or psychological dysfunctions. Therefore, a real-time monitoring of the respiration patterns, as well as respiration rate is a critical need in medical applications. There are several methods for respiration rate measurement. However, despite their accuracy, these methods are expensive and could not be integrated in a body sensor network. In this work, we present a real-time cloud-based platform for both monitoring the respiration rate and breath pattern classification, remotely. The proposed system is designed particularly for patients with breathing problems (e.g., respiratory complications after surgery) or sleep disorders. Our system includes calibrated accelerometer sensor, Bluetooth Low Energy (BLE) and cloud-computing model. We also suggest a procedure to improve the accuracy of respiration rate for patients at rest positions. The overall error in the respiration rate calculation is obtained 0.53% considering SPR-BTA spirometer as the reference. Five types of respiration disorders, Bradapnea, Tachypnea, Cheyn-stokes, Kaussmal, and Biot’s breathing are classified based on hierarchical Support Vector Machine (SVM) with seven different features. We have evaluated the performance of the proposed classification while it is individualized to every subject (case 1) as well as considering all subjects (case 2). Since the selection of kernel function is a key factor to decide SVM’s performance, in this paper three different kernel functions are evaluated. The experiments are conducted with 11 subjects and the average accuracy of 94.52% for case 1 and the accuracy of 81.29% for case 2 are achieved based on Radial Basis Function (RBF). Finally, a performance evaluation has been done for normal and impaired subjects considering sensitivity, specificity and G-mean parameters of different kernel functions. Full article

We studied 44-year old man with heart failure (ejection fraction −25%). Obesity, arterial hypertension, snoring and excessive daytime sleepiness suggested concomitant obstructive sleep apnoea. Limited polysomnography with Polymesam revealed typical Cheyne-Stokes respiration with mainly central apnoeas (RDI = 48/hour). We did not fi nd any obstructive episodes during sleep study. Patient responded to CPAP therapy and apnoea hypo-pnoe index decreased to 12/hour on 8 mbar pressure. Full article

Sleep-related breathing disorders cause significant morbidity through excessive hypersomnolence, cognitive impairment, unrefreshing sleep and other symptoms. Potential consequences include accidents related to sleepiness and cardiovascular diseases. In the most common obstructive sleep apnoea syndrome, apnoeas and hypopnoeas are related to intermittent upper airways collapse. In central sleep apnoea and Cheyne-Stokes respiration associated with congestive heart failure, the waxing and waning of ventilation is caused by an unstable respiratory motor output. Chronic sleep-related hypoventilation may occur in extreme obesity, in neuro-muscular disorders that affect respiratory muscles and in patients with chest-wall deformities and lung diseases.The diagnosis of sleep-related breathing disorders is suggested by typical symptoms and confirmed by a sleep study. Treatment options include various forms of positive pressure ventilation applied by a nasal or face mask, removable oral appliances that increase the upper airway lumen by advancing the mandible, and surgery in selected cases. Full article