Search Results (1,131)

Study Objectives: Our study aimed to investigate the association between asthma and obstructive sleep apnea (OSA) in American Indian communities, a historically underrepresented population in clinical research with a high prevalence of asthma and OSA risk factors like smoking and obesity. Methods: This cross-sectional study used data retrieved from the Strong Heart Study cohort. Participants who attended both the Asthma Sub-study and the Sleep Heart Health Study around the same time were compared for active asthma diagnosis, OSA diagnosis, and potential risk factors for asthma and OSA. The association between asthma and OSA was then evaluated. Results: Among the 2480 participants who attended the Strong Heart Study Phase III exam, 123 participated in both the Asthma Sub-study and the Sleep Heart Health Study. Of these, 13 were diagnosed with OSA, with 4 having moderate to severe OSA. There was no statistically significant difference in OSA prevalence between the active asthma group and the non-active asthma group (former asthma or no asthma) (9.6% vs. 12.5%, p = 0.63). Additionally, body mass index did not differ significantly between participants with both active asthma and OSA and those without active asthma, OSA, or both. OSA diagnosis was significantly associated with male sex (Odds Ratio [OR] 9.2 [1.85–45.87], p = 0.007) and body mass index (OR 1.1 [1.02–1.26], p = 0.016) but not with age or a diagnosis of active asthma. Conclusions: In this American Indian cohort, no significant difference in OSA prevalence was observed between participants with and without active asthma, contradicting previous studies. Further research is needed to explore the underlying reasons for this discrepancy. Full article

Background: Sleep disorders have been poorly described in congenital (CDM) and childhood (ChDM) myotonic dystrophy despite being highly burdensome. The aims of this study were to explore sleep disorders in a cohort of Italian CDM and ChDM and to assess their association with motor and respiratory function and disease-specific cognitive and behavioral assessments. Methods: This was an observational multicenter study. Reported sleep quality was assessed using the Pediatric Daytime Sleepiness Scale (PDSS) and Pediatric Sleep Questionnaire (PSQ). Sleep quality was correlated to motor function (6 min walk test, 6MWT and grip strength; pulmonary function (predicted Forced Vital Capacity%, FVC% pred.); executive function assessed by BRIEF-2; autism traits assessed by Autism Spectrum Screening Questionnaire (ASSQ) and Repetitive Behavior Scale-revised (RBS-R); Quality of life (PedsQL) and disease burden (Congenital Childhood Myotonic Dystrophy Health Index, CCMDHI). Results: Forty-six patients were included, 33 CDM and 13 ChDM, at a median age of 10.4 and 15.1 years. Daytime sleepiness and disrupted sleep were reported by 30% children, in both subgroups of CDM and ChDM. Daytime sleepiness correlated with autism traits in CDM (p < 0.05). Disrupted sleep correlated with poorer executive function (p = 0.04) and higher disease burden (p = 0.03). Conclusions: Sleep issues are a feature of both CDM and ChDM. They correlate with behavioral issues and impact on disease burden. Full article

Background/Objectives: Exploring and establishing a consensus on palatal surgery, the outcomes and follow-up after the palatal surgery, the complications of palatal surgery, and the post-operative management after palatal surgery for snoring and obstructive sleep apnea (OSA) among sleep surgeons is critical in the surgical management of patients with such conditions. Methods: Using the Delphi method, a set of statements was developed based on the literature and circulated among a panel of eight European experts. Responses included agreeing and disagreeing with each statement, and the comments were used to assess the level of consensus and to develop a revised version. The new version with the level of consensus and anonymized comments was sent to each panel member as the second round. This was repeated over a total of five rounds. Results: The final set included a total of 111 statements, 27 of which were stand-alone questions and 21 of which contained 84 sub-statements. Of the 34 statements regarding palatal surgery, consensus was achieved among all eight, seven, and six panelists for 50%, 35.3%, and 5.9% of the questions, respectively. Of the 43 statements regarding the outcomes and follow-up after the palatal surgery, consensus was achieved among all eight, seven, and six panelists for 53.5%, 23.3%, and 4.7% of the questions, respectively. Of the 24 statements regarding complications after the palatal surgery, consensus was achieved among all eight, seven, and six panelists for 91.7%, 0%, and 4.2% of the questions, respectively. Of the 10 statements regarding post-operative management after palatal surgery, consensus was achieved among all eight, seven, and six panelists for 10%, 30%, and 30% of the papers, respectively. Conclusions: This consensus provides an overview of the work of European sleep surgeons to develop a set of statements on palatal surgery for the treatment of snoring and OSA, the outcomes and follow-up, the complications, and the post-operative management of palatal surgery. We believe that this will be helpful in everyday practice. It also indicates key areas for further studies in sleep surgery. Full article

Hypoglossal nerve stimulation (HGNS) has emerged as an effective treatment for obstructive sleep apnea (OSA). Identifying baseline characteristics that prospectively could predict treatment outcomes even better is crucial for optimizing patient selection and improving therapeutic success in the future. A systematic review was conducted following PRISMA guidelines. Literature searches in Medline, Web of Science, and Cochrane databases identified studies assessing baseline characteristics associated with HGNS treatment outcomes. Inclusion criteria focused on studies with adult patients diagnosed with OSA, treated with HGNS, and assessed using full-night efficacy sleep studies. Risk of bias was evaluated using the NICE tool. Twenty-six studies met the inclusion criteria. Commonly reported baseline characteristics with predictive potential included BMI, site of collapse, and various pathophysiological endotypes. Most studies used the original Sher criteria to define treatment response, though variations were noted. Results suggested that lower BMI, absence of complete concentric collapse at the palatal level, and specific pathophysiological traits were associated with better HGNS outcomes. This review identified several baseline characteristics associated with HGNS outcomes, which may guide future patient selection. Importantly, patients were already preselected for HGNS. Standardizing response criteria is recommended to enhance the evaluation and effectiveness of HGNS therapy in OSA patients. Full article

Obstructive sleep apnea (OSA) is a prevalent pathology with current modalities of treatment including continuous positive airway pressure (CPAP), surgery, weight loss, hypoglossal nerve stimulation, and pharmacotherapy. While CPAP is the current standard treatment for OSA, lack of tolerance and side effects necessitate alternative modalities of treatment. Various pharmacologic agents exist with mechanisms that may target OSA. Early trials have demonstrated efficacy of noradrenergic-antimuscarinic combinations to stimulate the airway, promote pharyngeal muscle tone, and prevent airway collapse. These agents, which we discuss in detail, have demonstrated significant reductions in apnea-hypopnea index (AHI) and lowest oxygen saturations based on preliminary studies. Glucagon-like peptide 1 receptor agonists (GLP-1RA), which stimulate endogenous insulin, reducing glucagon release, and decreasing gastric emptying, have shown positive results for OSA patients through weight loss with reductions in AHI. In this narrative review article, we highlight the mechanisms, current data, and future potential for multiple drug classes, including respiratory stimulants and GLP-1RAs. Full article

Acromegaly is a rare endocrine syndrome characterized by unrestrained growth hormone (GH) secretion from a GH-secreting pituitary neuroendocrine tumor (PitNET). Data on sleep disorders are scanty and mainly linked to Obstructive Sleep Apnea Syndrome (OSAS). This study aimed to evaluate the prevalence of insomnia and sleep quality in a cohort of patients with a low risk of OSAS before and after therapies for acromegaly. A total of 27 naïve acromegalic patients (mean age 55.15 ± 10.53 years) were submitted to a psychometric sleep evaluation and compared to a matched control group of 24 Non-Functioning Pituitary micro-Adenoma patients (mean age 51.08 ± 11.02 years). A psychometric sleep evaluation was carried out 4 years later, after achieving acromegaly control in all patients. The role of different therapies for acromegaly (somatostatin analogues, pegvisomant, or adenomectomy) was evaluated. At the initial evaluation, most untreated acromegalic patients had a higher rate of impaired sleep quality and clinical insomnia than NFPA patients (p = 0.001 ES = 1.381, p = 0.001 ES = 1.654, respectively). Patients treated with somatostatin analogues or pituitary adenomectomy had an improvement in insomnia parameters (p = 0.046 ES = 0.777, p = 0.038 ES = 0.913, respectively). Conversely, in patients treated with pegvisomant, sleep quality and insomnia worsened (p = 0.028 ES = 1.002, p = 0.009 ES = 1.398, respectively). In summary, therapies for acromegaly seem to have divergent effects on perceived sleep disorders. Concerning sleep, somatostatin analogues and adenomectomy seem to have favorable effects on the psychometric parameters of sleep. Full article

The widespread implementation of artificial intelligence technologies provides an appealing alternative to traditional search engines for online patient healthcare education. This study assessed ChatGPT-3.5’s capabilities as a source of obstructive sleep apnea (OSA) information, using Google Search as a comparison. Ten frequently searched questions related to OSA were entered into Google Search and ChatGPT-3.5. The responses were assessed by two independent researchers using the Global Quality Score (GQS), Patient Education Materials Assessment Tool (PEMAT), DISCERN instrument, CLEAR tool, and readability scores (Flesch Reading Ease and Flesch–Kincaid Grade Level). ChatGPT-3.5 significantly outperformed Google Search in terms of GQS (5.00 vs. 2.50, p < 0.0001), DISCERN reliability (35.00 vs. 29.50, p = 0.001), and quality (11.50 vs. 7.00, p = 0.02). The CLEAR tool scores indicated that ChatGPT-3.5 provided excellent content (25.00 vs. 15.50, p < 0.001). PEMAT scores showed higher understandability (60–91% vs. 44–80%) and actionability for ChatGPT-3.5 (0–40% vs. 0%). Readability analysis revealed that Google Search responses were easier to read (FRE: 56.05 vs. 22.00; FKGL: 9.00 vs. 14.00, p < 0.0001). ChatGPT-3.5 delivers higher quality and more comprehensive OSA information compared to Google Search, although its responses are less readable. This suggests that while ChatGPT-3.5 can be a valuable tool for patient education, efforts to improve readability are necessary to ensure accessibility and utility for all patients. Healthcare providers should be aware of the strengths and weaknesses of various healthcare information resources and emphasize the importance of critically evaluating online health information, advising patients on its reliability and relevance. Full article

Background: Obstructive sleep apnea (OSA) is a common disorder associated with major cardiovascular and neurocognitive sequelae. Continuous positive airway pressure (CPAP) is the standard treatment for OSA. The aim of this study was to investigate the prevalence and associations of long-term CPAP adherence in newly diagnosed OSA patients. Methods: We enrolled patients who were diagnosed with OSA during the COVID-19 pandemic. Adherence was defined as CPAP use ≥4 h per night on ≥70% of nights over 30 consecutive days. Patient demographics were retrieved from medical records, and CPAP adherence at 6 months and 1 year after initiation was monitored. Results: Overall, 107 patients were included in the analysis. A number of 73 (68%) and 63 (59%) patients were adherent to CPAP treatment at 6 months and 12 months accordingly. Among the factors examined and analyzed (age, gender, BMI, Apnea–Hypopnea Index (AHI)), no significant correlation was found. Further analysis revealed the potential role of comorbidities. CPAP compliance at 6 months was shown to be associated with better CPAP adherence at 12 months. Conclusions: CPAP adherence at 6 months is correlated to long-term adherence to treatment. Therefore, early close follow-up is important. Further prospective studies are needed to identify other potential predictors. Full article

Background: Mandibular advancement devices (MADs) are considered a primary alternative treatment for adults with moderate to severe obstructive sleep apnea (OSA) who are unable to tolerate or do not respond to continuous positive airway pressure (CPAP) therapy, supported by substantial scientific evidence. While a range of designs and materials for MADs are commercially available, there is a lack of clear diagnostic guidelines to assist clinicians in selecting the most appropriate device based on a multidisciplinary evaluation of OSA patients. This narrative review seeks to outline the key characteristics of MADs that clinicians should evaluate during both the diagnostic and treatment phases for patients with OSA. Methods: An extensive search of academic databases was conducted to gather relevant studies that address therapeutic and diagnostic recommendations for the design and titration of MADs. The search was carried out across EMBASE, Scopus, PubMed, and Web of Science up to May 2024. From a total of 1445 identified citations, 1103 remained after duplicate removal. Based on the inclusion criteria, the full text of 202 articles was retrieved, and 70 studies were ultimately included in this review. The extracted data were organized to generate clinical insights, aimed at guiding orthodontists in optimizing diagnostic and decision-making processes for treating OSA patients with MADs. Results: The analysis led to the identification of key clinical questions that can assist orthodontists in enhancing their approach and choosing the appropriate appliance basing on the diagnosis and clinical dento-orofacial characteristics. Conclusions: Bibloc appliances could be preferred over mono-bloc devices due to the possibility of arranging the mandibular advancement according to the patient’s clinical condition and orofacial symptoms. Provisional devices could be used as screening tools to verify the patient’s adherence to the therapy. Regardless of the MAD design, type and programmed advancement, it must be under-lined that the rule of the orthodontist/dental specialist is secondary to the other sleep-medicine specialists (ORL, pulmonologist) and must be related to (1) a preliminary assessment of MAD usage (dental anatomical conditions), (2) testing a diagnostic MAD usable during a sleep examination (PSG or DISE), (3) final treatment with a definitive MAD. Full article

Obstructive Sleep Apnea (OSA) is a disorder characterized by repeated upper airway collapse during sleep, leading to apneas and/or hypopneas, with associated symptoms like intermittent hypoxia and sleep fragmentation. One of the agents contributing to OSA occurrence and development seems to be serotonin (5-HT). Currently, the research focuses on establishing and interlinking OSA pathogenesis and the severity of the disease on the molecular neurotransmitter omnipresent in the human body—serotonin, its pathway, products, receptors, drugs affecting the levels of serotonin, or genetic predisposition. The 5-HT system is associated with numerous physiological processes such as digestion, circulation, sleep, respiration, and muscle tone—all of which are considered factors promoting and influencing the course of OSA because of correlations with comorbid conditions. Comorbidities include obesity, physiological and behavioral disorders as well as cardiovascular diseases. Additionally, both serotonin imbalance and OSA are connected with psychiatric comorbidities, such as depression, anxiety, or cognitive dysfunction. Pharmacological agents that target 5-HT receptors have shown varying degrees of efficacy in reducing the Apnea-Hypopnea Index and improving OSA symptoms. The potential role of the 5-HT signaling pathway in modulating OSA provides a promising avenue for new therapeutic interventions that could accompany the primary treatment of OSA—continuous positive airway pressure. Thus, this review aims to elucidate the complex role of 5-HT and its regulatory mechanisms in OSA pathophysiology, evaluating its potential as a therapeutic target. We also summarize the relationship between 5-HT signaling and various physiological functions, as well as its correlations with comorbid conditions. Full article

Obstructive sleep apnea (OSA) is one of the common sleep disorders related to breathing. It is important to identify an optimal set of questions among the existing questionnaires, using a data-driven approach, that can prescreen OSA with high sensitivity and specificity. The current study proposes reliable models that are based on machine learning techniques to predict the severity of OSA. A total of 66 participants consisted of 45 males and 21 females (average age = 52.4 years old; standard deviation ± 14.6). Participants were asked to fill out the questionnaire items. If the value of the Respiratory Disturbance Index (RDI) was more than 30, the participant was diagnosed with severe OSA. Several different modeling techniques were applied, including deep neural networks with a scaled principal component analysis (DNN-PCA), random forest (RF), Adaptive Boosting Classifier (ABC), Decision Tree Classifier (DTC), K-nearest neighbors classifier (KNC), and support vector machine classifier (SVMC). Among the participants, 27 participants were diagnosed with severe OSA (RDI > 30). The area under the receiver operating characteristic curve (AUROC) was used to evaluate the developed models. As a result, the AUROC values of DNN-PCA, RF, ABC, DTC, KNC, and SVMC models were 0.95, 0.62, 0.53, 0.53, 0.51, and 0.78, respectively. The highest AUROC value was found in the DNN-PCA model with a sensitivity of 0.95, a specificity of 0.75, a positive predictivity of 0.95, an F1 score of 0.95, and an accuracy of 0.95. The DNN-PCA model outperforms the existing screening questionnaires, scores, and other models. Full article

Obesity is often viewed as a result of patient failure to adhere to healthy dietary intake and physical activity; however, this belief undermines the complexity of obesity as a disease. Rates of obesity have doubled for adults and quadrupled for adolescents since the 1990s. Without effective interventions to help combat this disease, patients with obesity are at increased risk for developing type 2 diabetes, heart attack, stroke, liver disease, obstructive sleep apnea, and more. Patients often go through several barriers before they are offered pharmacotherapy or bariatric surgery, even though evidence supports the use of these interventions earlier. This partially stems from the cultural barriers associated with using these therapies, but it is also related to healthcare provider bias and limited knowledge of these therapies. Finally, even when patients are offered treatment for obesity, they often run into insurance barriers that keep them from treatment. There needs to be a cultural shift to accept obesity as a disease and improve access to effective treatments sooner to help decrease the risk of health complications associated with obesity. Full article

Obstructive sleep apnea (OSA) often goes unrecognized despite common symptoms, such as excessive daytime sleepiness, fatigue, and impaired quality of life (QoL). Continuous positive airway pressure (CPAP) is the gold standard treatment for OSA, but optimal daily usage and time needed for observable effects remain unclear. This study aimed to investigate the short-term effects of CPAP on daytime sleepiness and QoL in patients with severe OSA. Medical records were collected from 87 patients with severe OSA who initiated CPAP therapy. Also, validated questionnaires were used before and after one month of CPAP to analyze QoL—the Calgary Sleep Apnea Quality of Life Index (SAQLI), the Cues to CPAP Use Questionnaire (CCUQ), and daytime sleepiness—the Epworth Sleepiness Scale (ESS). Multiple regression analysis was conducted to identify predictors of CPAP usage. Of the total participants aged 55.6 ± 12.5, 77% were males, and 62% were CPAP adherent. Reductions in daytime sleepiness (ESS) were noted, as well as improvements in both overall QoL (SAQLI) and specifically in the domains of daily functioning, social interactions, emotional well-being, and symptom perception. Important cues for CPAP usage recognized by patients were physicians’ instructions and physicians’ concern regarding their patients’ condition. Furthermore, multiple regression revealed higher SAQLI scores and lower ESS scores as positive predictors of CPAP usage, along with lower AHI after one month of CPAP being associated with sufficient adherence. Full article

Analysis of tracheal breathing sounds (TBS) is a significant area of study in medical diagnostics and monitoring for respiratory diseases and obstructive sleep apnea (OSA). Recorded at the suprasternal notch, TBS can provide detailed insights into the respiratory system’s functioning and health. This method has been particularly useful for non-invasive assessments and is used in various clinical settings, such as OSA, asthma, respiratory infectious diseases, lung function, and detection during either wakefulness or sleep. One of the challenges and limitations of TBS recording is the background noise, including speech sound, movement, and even non-tracheal breathing sounds propagating in the air. The breathing sounds captured from the nose or mouth can interfere with the tracheal breathing sounds, making it difficult to isolate the sounds necessary for accurate diagnostics. In this study, two surface microphones are proposed to accurately record TBS acquired solely from the trachea. The frequency response of each microphone is compared with a reference microphone. Additionally, this study evaluates the tracheal and lung breathing sounds of six participants recorded using the proposed microphones versus a commercial omnidirectional microphone, both in environments with and without background white noise. The proposed microphones demonstrated reduced susceptibility to background noise particularly in the frequency ranges (1800–2199) Hz and (2200–2599) Hz with maximum deviation of 2 dB and 2.1 dB, respectively, compared to 9 dB observed in the commercial microphone. The findings of this study have potential implications for improving the accuracy and reliability of respiratory diagnostics in clinical practice. Full article

For patients suffering from obstructive sleep apnea and sleep-related breathing disorders, snoring is quite common, and it greatly interferes with the quality of life for them and for the people surrounding them. For diagnosing obstructive sleep apnea, snoring is used as a screening parameter, so the exact detection and classification of snoring sounds are quite important. Therefore, automated and very high precision snoring analysis and classification algorithms are required. In this work, initially the features are extracted from six different domains, such as time domain, frequency domain, Discrete Wavelet Transform (DWT) domain, sparse domain, eigen value domain, and cepstral domain. The extracted features are then selected using three efficient feature selection techniques, such as Golden Eagle Optimization (GEO), Salp Swarm Algorithm (SSA), and Refined SSA. The selected features are finally classified with the help of eight traditional machine learning classifiers and two proposed classifiers, such as the Firefly Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (FA-WELM-Adaboost) and the Capuchin Search Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (CSA-WELM-Adaboost). The analysis is performed on the MPSSC Interspeech dataset, and the best results are obtained when the DWT features with the refined SSA feature selection technique and FA-WELM-Adaboost hybrid classifier are utilized, reporting an Unweighted Average Recall (UAR) of 74.23%. The second-best results are obtained when DWT features are selected with the GEO feature selection technique and a CSA-WELM-Adaboost hybrid classifier is utilized, reporting an UAR of 73.86%. Full article