Effects Of Sleep Apnea Research Articles

Pentraxin-3 marker as a predictor of cardiovascular events in obstructive sleep apnea; a cross-sectional study

Introduction A large population suffers from obstructive sleep apnea (OSA). Most of these individuals (almost 90%) are either unrecognized or untreated OSA. These patients are at increased risk of cardiovascular diseases. Aim The aim of this study was to assess the prognostic value of Pentraxin 3 (PTX3) in OSA compared with the clinically relevant biological markers (HbA1c, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), uric acid, and fibrinogen) and to evaluate the relationship between PTX3 levels and risk of cardiovascular diseases. Patients and methods This research used a cross-sectional design and enrolled 100 participants with suspected OSA. All the participants underwent a full clinical history, a STOP-Bang questionnaire, and a polysomnography (PSG) study, and the serum biomarkers were also analyzed. Results The apnea-hypopnea index (AHI) from PSG was used to measure the OSA severity, and the individuals were distributed into three groups accordingly. Our findings showed that there was a significant difference in Pentraxin 3 and fibrinogen levels between patients with mild or moderate OSA and those with severe OSA. Pentraxin 3, at a cutoff point of greater than 4.25 ng/ml, had the highest prognostic accuracy in OSA (96%), with an AUC of 0.96. Regression analysis revealed that Pentraxin 3, with a cutoff point greater than 4.25, and CRP were significant predictors of cardiovascular disease in individuals with OSA. Furthermore, Pentraxin 3 (with a cutoff point of greater than 4.25 ng/ml) and fibrinogen were significant predictors of pulmonary embolism in patients with OSA. Conclusion Serum PTX3 was higher in patients with severe OSA than in those with mild- to moderate OSA. Furthermore, we found that OSA severity as indicated by the AHI was significantly correlated with serum PTX3. PTX3 and CRP are useful markers of cardiovascular risk in OSA.

Multicenter comparative study of polysomnography outcomes in children with the monogenic disorder sickle cell disease.

Sleep-disordered breathing (SDB) is prevalent in children with sickle cell disease (SCD) and is associated with worse outcomes. This study aims to compare the outcomes of polysomnography (PSG) performed for pediatric patients with SCD at three United States centers. We included children with SCD aged 0-21 who underwent PSG at three American Academy of Sleep Medicine accredited centers: the University of Alabama at Birmingham (UAB), the University of Florida (UF), and Duke University Hospital (DUH), between 2012 and 2022. Descriptive statistics were used as appropriate to compare the baseline characters and PSG outcomes among the different centers. A total of 210 children with SCD from the three centers were included, with comparable sex, SCD genotypes, hemoglobin, hematocrit levels, and chronic transfusion. Children from the different centers exhibited variations in age (P < .001), BMI (P < .05), mean corpuscular volume (P < .05), and hydroxyurea use (P < .05) at the time of the PSG. Overall, the three centers showed significantly different PSG outcomes. Patients from UF had worse obstructive sleep apnea, oxygenation, and periodic leg movement events, together with lower hydroxyurea usage. While those from DUH showed higher hypoventilation and arousal indices. This multicenter study underscores variations in PSG outcomes among pediatric SCD patients at different centers in the Southeast United States. These findings emphasize the need for standardized approaches to screen for SDB, refer to PSG, and interpret the results in children with SCD. These conclusions may apply to other genetic disorders associated with an increased risk of sleep-disordered breathing.

Indoor air pollution impacts cardiovascular autonomic control during sleep and the inflammatory profile

The present study explores the modifications of cardiovascular autonomic control (CAC) during wake and sleep time and the systemic inflammatory profile associated with exposure to indoor air pollution (IAP) in a cohort of healthy subjects.Twenty healthy volunteers were enrolled. Indoor levels of fine particulate matter (PM2.5), nitrogen dioxide (NO2) and volatile organic compounds (VOCs) were monitored using a portable detector for 7 days. Together, a 7-day monitoring was performed through a wireless patch that continuously recorded electrocardiogram, respiratory activity and actigraphy. Indexes of CAC during wake and sleep time were derived from the biosignals: heart rate and low-frequency to high-frequency ratio (LF/HF), index of sympathovagal balance with higher values corresponding to a predominance of the sympathetic branch. Cyclic variation of heart rate index (CVHRI events/hour) during sleep, a proxy for the evaluation of sleep apnea, was assessed for each night. After the monitoring, blood samples were collected to assess the inflammatory profile. Regression and correlation analyses were performed.A positive association between VOC exposure and the CVHRI (Δ% = +0.2% for 1 μg/m3 VOCs, p = 0.008) was found. The CVHRI was also positively associated with LF/HF during sleep, thus higher CVHRI values corresponded to a shift of the sympathovagal balance towards a sympathetic predominance (r = 0.52; p = 0.018). NO2 exposure was positively associated with both the pro-inflammatory biomarker TREM-1 and the anti-inflammatory biomarker IL-10 (Δ% = +1.2% and Δ% = +2.4%, for 1 μg/m3 NO2; p = 0.005 and p = 0.022, respectively).The study highlights a possible causal relationship between IAP exposure and higher risk of sleep apnea events, associated with impaired CAC during sleep, and a pro-inflammatory state counterbalanced by an increased anti-inflammatory response in healthy subjects. This process may be disrupted in vulnerable populations, leading to a harmful chronic pro-inflammatory profile. Thus, IAP may emerge as a critical and often neglected risk factor for the public health that can be addressed through targeted preventive interventions.

Supine or non-supine sleep apnea events: which can be treated better with Barbed Pharyngoplasty?

Among the treatment options for Obstructive Sleep Apnea (OSA), intrapharyngeal surgery has undergone significant changes and received solid scientific support. However, it is crucial to identify the best candidate. This study aims to present the results of the modified Alianza technique in our clinic to show the differences in the impact of surgery on supine and non-supine apnea levels in moderate-severe OSA patients. Adult patients affected by moderate-severe OSA (Apnea-Hypopnea Index (AHI) > 15), having circular palatal collapse, and candidates for modified Alianza Tecnique were retrospectively enrolled. Each subject performed polysomnography pre- and post-operatively, and the follow-up check was performed after at least six months. This study enrolled 24 patients who underwent the Modified Alianza technique for sleep apnea. We found significant reductions in both supine and non-supine AH) after surgery. Non-supine AHI showed a greater reduction (from 20.89 to 11.64 episodes/hour, p = 0.0001) than supine AHI (from 42.51 to 25.93, p = 0.0003). We subsequently divided the patients into two groups based on whether they were affected by positional OSA before surgery. There was a lower percentage decrease in non-supine AHI compared to supine AHI after surgery in patients who were positional before surgery, but this difference was not statistically significant. Conversely, in the non-positional patient group, there was a higher decrease in non-supine AHI compared to supine AHI, although this was not statistically significant. The Modified Alianza Tecnique leads to notable enhancement in AHI among patients with OSA. Non-supine apneas exhibit a more favorable response to the surgery than supine apneas.

Sleep apnea in schizophrenia: Estimating prevalence and impact on cognition

Undertreated medical illnesses can compound the disabling cognitive deficits of schizophrenia. Obstructive sleep apnea (OSA) impairs cognitive domains also affected by schizophrenia, is common, and is treatable. The effects of sleep apnea on cognition in schizophrenia, however, are not well understood. We estimated the prevalence of OSA in a previously characterized sample of 3942 Veterans with schizophrenia by self-report and with a predictive model to identify individuals at high risk for OSA. We then compared neuropsychological and functional capacity assessment results between those who reported OSA versus those who did not, and between those predicted to have OSA versus predicted to not have OSA. We expected that many Veterans not reporting sleep apnea would be predicted to have it, and that both reported and predicted sleep apnea would be associated with lower cognitive and functional performance. The reported prevalence of OSA in the sample was 14%, whereas 72% were predicted to be at high risk of OSA. Interestingly, participants who reported having OSA had better cognitive and functional capacity performance (p's < 0.001) compared to those who did not report OSA, particularly on speed of processing assessments (p < 0.001). Predicted OSA, by contrast, was associated with lower speed of processing, verbal learning and working memory test scores (p's < 0.001). One possible interpretation of these results is that people with higher cognitive capacity may be more likely to seek medical care, while those with cognitive impairments are at greater risk for having untreated co-occurring medical conditions that further compromise cognition.

MFS-DBF: A trustworthy multichannel feature sieve and decision boundary formulation system for Obstructive Sleep Apnea detection

The fine identification of sleep apnea events is instrumental in Obstructive Sleep Apnea (OSA) diagnosis. The development of sleep apnea event detection algorithms based on polysomnography is becoming a research hotspot in medical signal processing. In this paper, we propose an Inverse-Projection based Visualization System (IPVS) for sleep apnea event detection algorithms. The IPVS consists of a feature dimensionality reduction module and a feature reconstruction module. First, features of blood oxygen saturation and nasal airflow are extracted and used as input data for event analysis. Then, visual analysis is conducted on the feature distribution for apnea events. Next, dimensionality reduction and reconstruction methods are combined to achieve the dynamic visualization of sleep apnea event feature sets and the visual analysis of classifier decision boundaries. Moreover, the decision-making consistency is explored for various sleep apnea event detection classifiers, which provides researchers and users with an intuitive understanding of the detection algorithm. We applied the IPVS to an OSA detection algorithm with an accuracy of 84% and a diagnostic accuracy of 92% on a publicly available dataset. The experimental results show that the consistency between our visualization results and prior medical knowledge provides strong evidence for the practicality of the proposed system. For clinical practice, the IPVS can guide users to focus on samples with higher uncertainty presented by the OSA detection algorithm, reducing the workload and improving the efficiency of clinical diagnosis, which in turn increases the value of trust.

Effect of CPAP Treatment on Cardiovascular Outcomes

IntroductionRandomized controlled trials (RCT) have not demonstrated a role for continuous positive airway pressure (CPAP) on the secondary prevention of major cardiovascular events in obstructive sleep apnea (OSA) patients. However, participants in RCTs are substantially different from real-world patients. Therefore, we aimed to assess the effect of CPAP treatment on major cardiovascular events in real-world OSA patients. MethodsPopulation-based longitudinal observational study including all OSA patients with an active CPAP prescription at the beginning of 2011 in Catalonia, Spain, that terminated CPAP treatment during 2011 and did not have CPAP prescriptions between 2012-2015; and propensity-score-matched OSA patients that continued CPAP treatment until the end of 2015 or death. Adjusted hazard ratios were used to assess the association between CPAP treatment and overall and cardiovascular mortality, cardiovascular hospitalizations, or major adverse cardiovascular events (MACEs). Results3638 CPAP terminators and 10,914 propensity-score-matched continuators were included (median age 67 [57–77] years, 71.4% male). During a median follow-up of 47.9 months CPAP continuators showed a lower risk of cardiovascular death than terminators (hazard ratio [HR]: 0.61; 95% confidence interval [CI]: 0.50–0.75) after adjusting by age, sex and key comorbidities. Similar results were found for cardiovascular hospitalizations (HR: 0.87; 95% CI: 0.76–0.99) and MACEs (HR: 0.84; 95% CI: 0.75–0.95). ConclusionCPAP treatment continuation could be associated with a significantly lower risk of major cardiovascular events in real-world OSA patients. This result highlights the importance of including real-world patients in studies on OSA.

Cardiovascular disease and sleep apnea in patients with type 2 diabetes mellitus

The article provides information on concomitant conditions of type 2 diabetes mellitus such as cardiovascular diseases, sleep apnea syndrome. Questions about the types of sleep breathing disorders and factors predisposing to this condition are discussed. Data on the prevalence of sleep apnea in the world are demonstrated. The main approaches to the diagnosis of this condition are shown. Differences in therapy approaches for obstructive sleep apnea and central sleep apnea have been demonstrated. The issues of the relationship between obstructive sleep apnea, type 2 diabetes mellitus and cardiovascular diseases are considered. The data of clinical studies evaluating the relationship of sleep apnea and cardiovascular diseases are presented. Their mutually aggravating influence is shown. Information is provided on the effect of sleep apnea on the main clinical outcomes according to clinical research data. The relationship between sleep apnea and heart failure is discussed. The main issues of the inflammatory profile of sleep apnea are discussed, which are closely related to cardiovascular diseases and type 2 diabetes and include: macrophage recruitment and inflammatory biomarkers such as C-reactive protein, chemokines and cytokines such as interleukin 6, tumor necrosis factor α, nuclear kappa factor B, as well as adhesion molecules such as selectins, intracellular adhesion molecule-1 (ICAM-1) and intracellular vascular adhesion molecule (VCAM-1), imbalance between prooxidant and antioxidant factors. These factors contribute to the development of microvascular and macrovascular complications in type 2 diabetes mellitus. Special attention is paid in the publication to the discussion of the main factors predisposing to the development of sleep apnea in heart failure in patients with type 2 diabetes mellitus. The data on the main diagnostic tests for patients with heart failure and type 2 diabetes are presented. Information is provided on the use of drugs from the group of sodiumglucose cotransporter type 2 inhibitors in patients with type 2 diabetes and sleep apnea syndrome. Reasonable recommendations on the use of these drugs are presented, taking into account the modern strategy for the treatment of type 2 diabetes.

An audio-semantic multimodal model for automatic obstructive sleep Apnea-Hypopnea Syndrome classification via multi-feature analysis of snoring sounds.

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a common sleep-related breathing disorder that significantly impacts the daily lives of patients. Currently, the diagnosis of OSAHS relies on various physiological signal monitoring devices, requiring a comprehensive Polysomnography (PSG). However, this invasive diagnostic method faces challenges such as data fluctuation and high costs. To address these challenges, we propose a novel data-driven Audio-Semantic Multi-Modal model for OSAHS severity classification (i.e., ASMM-OSA) based on patient snoring sound characteristics. In light of the correlation between the acoustic attributes of a patient's snoring patterns and their episodes of breathing disorders, we utilize the patient's sleep audio recordings as an initial screening modality. We analyze the audio features of snoring sounds during the night for subjects suspected of having OSAHS. Audio features were augmented via PubMedBERT to enrich their diversity and detail and subsequently classified for OSAHS severity using XGBoost based on the number of sleep apnea events. Experimental results using the OSAHS dataset from a collaborative university hospital demonstrate that our ASMM-OSA audio-semantic multimodal model achieves a diagnostic level in automatically identifying sleep apnea events and classifying the four-class severity (normal, mild, moderate, and severe) of OSAHS. Our proposed model promises new perspectives for non-invasive OSAHS diagnosis, potentially reducing costs and enhancing patient quality of life.

Application of multifractal analysis for recognition of sleep apnea using electroencephalogram

Sleep apnea is a common sleep disorder that causes an increased risk of mortality and affects quality of life. Identifying cases of sleep apnea is vital to promptly treating the condition. The goal of the work is to develop a software package for automatically detecting episodes of obstructive sleep apnea syndrome. The paper presents an approach based on the study of multifractal properties of the electroencephalogram (EEG) signal for detecting sleep apnea events. The results of tests conducted using the PhysioNet CAP Sleep database indicate excellent quality of detection of sleep apnea against the background of other diseases: using the Hurst index – sensitivity (90.00%) and specificity (91.50%), using the multifractal detrended fluctuation method analysis (by spectrum width) - sensitivity (90.00%) and specificity (90.50%).The use of a sleep apnea episode recognition system ensures timely detection of the disease at an early stage and thereby improves the effectiveness of the treatment of this disease.

Association of comorbid obstructive sleep apnea and insomnia with risk of major adverse cardiovascular events in sleep medicine center patients

ObjectivesTo investigate the association between comorbid obstructive sleep apnea and insomnia and major adverse cardiovascular events, including myocardial infarction, unstable angina, congestive heart failure, and stroke, in adults with suspected sleep disorders who underwent sleep apnea testing. MethodsWe conducted a retrospective analysis of electronic medical records data from patients with clinical encounters at sleep medicine centers to identify patients with comorbid obstructive sleep apnea and insomnia, obstructive sleep apnea only, insomnia only, and patients without a diagnosis of obstructive sleep apnea or insomnia (i.e., controls). Obstructive sleep apnea, insomnia, comorbidities, and new-onset major adverse cardiovascular events were ascertained by ICD-9-CM and ICD-10-CM codes. Multivariable adjusted Cox proportional regression models evaluated the risk of major adverse cardiovascular events over a 10-year follow-up period. ResultsA total of 3951 patients, 226 controls, 2107 with obstructive sleep apnea only, 276 with insomnia only, and 1342 with comorbid obstructive sleep apnea and insomnia, were included in the analysis. Compared to controls, comorbid obstructive sleep apnea and insomnia were associated with a significantly higher risk of developing major adverse cardiovascular events (hazard ratio 3.60, 95 CI%: 2.33-5.91) in unadjusted analyses. The relationship between comorbid obstructive sleep apnea and insomnia and major adverse cardiovascular events remained after adjustment for demographic and behavioral factors, but not after further adjustment for comorbidities. The greatest risk of major adverse cardiovascular events was found among younger adults with comorbid obstructive sleep apnea and insomnia. Obstructive sleep apnea only was associated with greater risk of major adverse cardiovascular events in unadjusted analyses only (hazard ratio 2.77, 95% CI: 1.80-4.54). Insomnia only was not significantly associated with increased risk of major adverse cardiovascular events. ConclusionsComorbid obstructive sleep apnea and insomnia may be a high-risk group for major adverse cardiovascular events, particularly younger adults. Further research is needed to better understand the association between comorbid obstructive sleep apnea and insomnia and major adverse cardiovascular events risk.

Real world assessment of reduction of obstructive sleep apnea events by continuous positive airway pressure using a continuous large U.S. sample by home under-mattress devices

Real world assessment of reduction of obstructive sleep apnea events by continuous positive airway pressure using a continuous large U.S. sample by home under-mattress devices

Enhancing both sleep stage classification and obstructive sleep apnea event detection tasks with a unified sound-based multi-task model

Enhancing both sleep stage classification and obstructive sleep apnea event detection tasks with a unified sound-based multi-task model

All Obstructive Sleep Apnea Events Are Not Created Equal: The Relationship between Event-related Hypoxemia and Physiologic Response.

Rationale: Obstructive sleep apnea (OSA) severity is typically assessed by the apnea-hypopnea index (AHI), a frequency-based metric that allocates equal weight to all respiratory events. However, more severe events may have a greater physiologic impact. Objectives: The purpose of this study was to determine whether the degree of event-related hypoxemia would be associated with the postevent physiologic response. Methods: Patients with OSA (AHI, ⩾5/h) from the multicenter Canadian Sleep and Circadian Network cohort were studied. Using mixed-effect linear regression, we examined associations between event-related hypoxic burden (HBev) assessed by the area under the event-related oxygen saturation recording with heart rate changes (ΔHRev), vasoconstriction (vasoconstriction burden [VCBev] assessed with photoplethysmography), and electroencephalographic responses (power ratio before and after events). Results: Polysomnographic recordings from 658 patients (median [interquartile range] age, 55.00 [45.00, 64.00] yr; AHI, 27.15 [14.90, 64.05] events/h; 42% female) were included in the analyses. HBev was associated with an increase in all physiologic responses after controlling for age, sex, body mass index, sleep stage, total sleep time, and study centers; for example, 1 standard deviation increase in HBev was associated with 0.21 [95% confidence interval, 0.2, 0.22], 0.08 [0.08, 0.09], and 0.22 [0.21, 0.23] standard deviation increases in ΔHRev, VCBev, and β-power ratio, respectively. Conclusions: Increased event-related hypoxic burden was associated with greater responses across a broad range of physiologic signals. Future metrics that incorporate information about the variability of these physiologic responses may have promise in providing a more nuanced assessment of OSA severity.

Automatic prediction of obstructive sleep apnea event using deep learning algorithm based on ECG and thoracic movement signals

Background Obstructive sleep apnea (OSA) is a sleeping disorder that can cause multiple complications. Aims/Objective Our aim is to build an automatic deep learning model for OSA event detection using combined signals from the electrocardiogram (ECG) and thoracic movement signals. Materials and methods We retrospectively obtained 420 cases of PSG data and extracted the signals of ECG, as well as the thoracic movement signal. A deep learning algorithm named ResNeSt34 was used to construct the model using ECG with or without thoracic movement signal. The model performance was assessed by parameters such as accuracy, precision, recall, F1-score, receiver operating characteristic (ROC), and area under the ROC curve (AUC). Results The model using combined signals of ECG and thoracic movement signal performed much better than the model using ECG alone. The former had accuracy, precision, recall, F1-score, and AUC values of 89.0%, 88.8%, 89.0%, 88.2%, and 92.9%, respectively, while the latter had values of 84.1%, 83.1%, 84.1%, 83.3%, and 82.8%, respectively. Conclusions and significance The automatic OSA event detection model using combined signals of ECG and thoracic movement signal with the ResNeSt34 algorithm is reliable and can be used for OSA screening.

Sleep bruxism and associated physiological events in children with obstructive sleep apnea: a polysomnographic study.

The aim of this study was to evaluate the physiological events associated with sleep bruxism (Sleep Bruxism [SB]; presence of mandibular movement activity) and the control window (4 minutes prior to SB event, where no mandibular movement activity was detected) in a polysomnography study in children with mild sleep apnea. Polysomnography data from children aged 4 to 9 years old diagnosed with mild sleep apnea were analyzed by 2 trained examiners. The mandibular movement activity (bruxism event; SB) was classified into phasic and tonic. The control window was selected 4 minutes prior to the SB event. All physiological events were recorded in both bruxism and control windows, including sleep phase (N1, N2, N3, and rapid eye movement), arousal, leg movements, tachycardia, bradycardia, oxygen desaturation, and number of obstructive and central sleep apnea events. The moment in which those phenomena occurred when associated with SB was also analyzed (before/after). Data were analyzed using 95% confidence intervals (α = 5%). A total of 661 mandibular movements were analyzed and classified as tonic (n = 372) or phasic (n = 289). The mean apnea-hypopnea index was 1.99 (SD = 1.27) events/h. The frequency of leg movements, microarousal, and tachycardia was increased in SB events when compared with the control window (P < .05). There was an increase in bradycardia frequency in the control window when compared with SB (in both tonic and phasic events). The frequency of obstructive and central apnea during SB was lower when compared with the other physiological phenomena. There is a difference in the physiological parameters evaluated in children with mild sleep apnea when comparing the 2 windows (SB and control). Sleep bruxism is associated with other physiological phenomena, such as leg movements, tachycardia, and microarousal. The use of a control window (where no mandibular activity was detected) was representative since it did not show activation of the sympathetic nervous system. Bonacina CF, Soster LMSFA, Bueno C, etal. Sleep bruxism and associated physiological events in children with obstructive sleep apnea: a polysomnographic study. J Clin Sleep Med. 2024;20(4):565-573.

Heart Rate Variability-Based Obstructive Sleep Apnea Events Classification Using Microwave Doppler Radar

Heart Rate Variability-Based Obstructive Sleep Apnea Events Classification Using Microwave Doppler Radar

Accurate apnea and hypopnea localization in PSG with Multi-scale object detection via Dual-modal Feature Learning

Localization of sleep apnea and hypopnea (SAH) events has routinely relied on expert visual inspection of polysomnography (PSG) recordings, which is a tedious task demanding a high level of professional skills. Automated detection methods have achieved remarkable success, especially with the recent advances in machine learning and deep learning technologies. However, a significant challenge still remains in methods towards clinical practices: How to accurately discriminate SAH events in PSG, with the onset and duration of each?This study develops an object detection framework for accurately identifying the position of SAH segments with varied durations (namely SAH-MOD) in three phases: (1) Dual-modal Feature Learning (DFL, dual-branch 1-D convolutional layers followed by Concatenate Block). Deep features are efficiently learned and then fused from two different types of signals related to respiratory, i.e., nasal airflow and abdominal movement; (2) Feature Map Generation (FMG, cascade 1-D convolutional layers). Feature maps are generated with multi-scale hierarchical features in different depths of the network, catering for the needs of object (SAH event) detection; Default anchors associated with the scales and receptive fields are tiled onto the corresponding detection feature maps; and (3) Multi-scale Object Detection (MOD). A variety of instances of prediction are then made on all available detection layers with post-processing to accurately capture each SAH event.Experiments have been performed on the dataset of stroke unit recordings for the detection of Obstructive Sleep Apnea Syndrome (OSASUD-dataset) with SAH-MOD against the state-of-the-art counterparts, and results indicate that: (1) SAH-MOD performs the best with a Recall of 81.0% and an F1-score of 71.1%; and (2) it has significant advantages in localizing the onset and duration of each SAH event, with 91.9% of the IoU values between predicted and labeled events falling between 0.6 and 1.0. Ablation experiments show that the introduction of dual-modal feature learning and hierarchical feature maps improves recall performance by 6.9% and 4.1%, respectively.

Sleep apnea in patients with exacerbated heart failure and overweight

Abstract Background Exacerbated heart failure is a condition with a high frequency of hospitalizations and mortality, especially in overweight patients. Screening and treatment for sleep apnea can be helpful in these patients. Purpose To determine the frequency and the phenotypic characteristics and of sleep apnea in patients with overweight and exacerbated heart failure. To assess systolic and diastolic function in patients with obstructive and central sleep apnea. To follow patients for one year and to assess hospitalization and mortality. Methods We conducted a single-centre, prospective cohort study form 120 consecutive patients hospitalized for exacerbation heart failure in the cardiology department. 70 patients meet inclusion criteria – Apnea-Hypopnea index (AHI) &amp;gt; 5, Epworth Sleepiness Scale (ESS) &amp;gt; 6, NTproBNP&amp;gt;900 pg/ml, and Body mass index(BMI) &amp;gt;25. All patients receiving optimal medical treatment. The follow-up period was 1 year. The primary endpoint was death for any reason. Sleep apnea screening was performed with ApneaLinkTM. Echocardiographic assessment of left ventricular ejection fraction (LVEF) and the E/e‘m ratio. Results From 70 with sleep apnea 73% (n=51) has obstructive sleep apnea (OSA) and 27% (n=19) has central sleep apnea (CSA). Detected significant changes in LVEF between the OSA group and CSA group (EF% 49.7±8.5vs43.3±9.6.4;p=0.008). Statistically significant changes there was about E/e’m ratio (E/e’m-17.01±3.7 vs 19.3±2.73; p=0.015) and BMI (BMI-38.2±6.5 vs 32.2±3.6; p&amp;lt;0.001). We found a moderate reverse correlation between the LVEF and the number of central sleep apnea events (r=-0,34;p=0,003). Simple linear regression was used to test if the left ventricular ejection fraction significantly predicted the number of central apnea events. The overall regression was statistically significant (R2 = 0.120, F(1,68) = 9.26, p = .003). It was found that the left ventricular ejection fraction significantly predicted the number of central apnea events (β = -0.06, p = .003). Patients from both groups were followed up regarding first hospitalization for heart failure and mortality over a period of 12 months. First hospitalization in patients with CSA occurs significantly sooner than in patients with OSA. The average number of months without hospitalization for HF in patients with CSA was 6.7 months versus 9.7 months in patients with OSA (Log Rank (Mental-Cox) p=0.012). The OSA group had a median survival of 10.5 months versus 9.3 months in the CSA group (Log Rank (Mental-Cox) p=0.026). Conclusion Sleep apnea is a common comorbidity in patients with exacerbated heart failure and overweight. OSA occurs to a greater extent than CSA. Patients with reduced systolic function are at higher risk of central sleep apneas events. Low LVEF% can be used as a prognostic factor regarding the occurrence of central sleep apnea events. Controlling sleep apnea can reduce patient readmissions and mortality.

Predicting obstructive sleep apnoea and perioperative respiratory adverse events in children: role of upper airway collapsibility measurements

BackgroundObstructive sleep apnoea (OSA) and perioperative respiratory adverse events are significant risks for anaesthesia in children undergoing adenotonsillectomy. Upper airway collapse is a crucial feature of OSA that contributes to respiratory adverse events. A measure of upper airway collapsibility to identify undiagnosed OSA can help guide perioperative management. We investigated the utility of pharyngeal closing pressure (PCLOSE) for predicting OSA and respiratory adverse events. MethodsChildren scheduled for elective adenotonsillectomy underwent in-laboratory polysomnography 2–12 weeks before surgery. PCLOSE measurements were obtained while the child was anaesthetised and breathing spontaneously just before surgery. Logistic regression was used to assess the predictive performance of PCLOSE for detecting OSA and perioperative respiratory adverse events after adjusting for potential covariates. ResultsIn 52 children (age, mean [standard deviation] 5.7 [1.8] yr; 20 [38%] females), airway collapse during PCLOSE was observed in 42 (81%). Of these, 19 of 42 (45%) patients did not have OSA, 15 (36%) had mild OSA, and eight (19%) had moderate-to-severe OSA. All 10 children with no evidence of airway collapse during the PCLOSE measurements did not have OSA. PCLOSE predicted moderate-to-severe OSA (odds ratio [OR] 1.71; 95% confidence interval [CI]: 1.2–2.8; P=0.011). All children with moderate-to-severe OSA could be identified at a PCLOSE threshold of –4.0 cm H2O (100% sensitivity), and most with no or mild OSA were ruled out (64.7% specificity; receiver operating characteristic/area under the curve=0.857). However, there was no significant association between respiratory adverse events and PCLOSE (OR 1.0; 95% CI: 0.8–1.1; P=0.641). ConclusionsMeasurement of PCLOSE after induction of anaesthesia can reliably identify moderate or severe OSA but not perioperative respiratory adverse events in children before adenotonsillectomy. Clinical trial registrationANZCTR ACTRN 12617001503314.