Objectives: Obstructive sleep apnea (OSA) is a common cause of atrial fibrillation (AF). The prevalence rate of OSA in AF is highest at 80%. There is limited data if who will develop AF in OSA patients. This study aimed to evaluate the prevalence of AF in patients with OSA and find clinical factors predictive of AF in patients with OSA.
Material And Methods: This was a cross-sectional study. We enrolled consecutive patients diagnosed with obstructive sleep apnea diagnosed by polysomnography. The primary outcome was persistent AF identified by electrocardiogram. Prevalence and predictors of AF in patients with OSA were analyzed.
Results: During the study period, there were 199 patients with OSA enrolled in the study. Of those, 31 patients (15.57%) had AF. There were five factors in the final model predictive for AF in OSA patients. Among those factors, three factors were independently associated with AF in OSA including age, tiredness, and glomerular filtration rate. The latter two factors were protective factors, while age was a predictor for AF with an adjusted odds ratio (95% confidence interval) of 1.052 (1.004, 1.103).
Conclusion: The prevalence of AF in patients with OSA was 15.57%. Elderly patients with renal deterioration are at risk of AF but AF risk was decreasing in patients with tiredness.
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http://dx.doi.org/10.5935/1984-0063.20220077 | DOI Listing |
Front Comput Neurosci
December 2024
School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, China.
Background: Automatic sleep staging is essential for assessing sleep quality and diagnosing sleep disorders. While previous research has achieved high classification performance, most current sleep staging networks have only been validated in healthy populations, ignoring the impact of Obstructive Sleep Apnea (OSA) on sleep stage classification. In addition, it remains challenging to effectively improve the fine-grained detection of polysomnography (PSG) and capture multi-scale transitions between sleep stages.
View Article and Find Full Text PDFJ Rhinol
November 2024
Department of Otorhinolaryngology-Head and Neck Surgery, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Background And Objectives: Obstructive sleep apnea (OSA) is a prevalent sleep disorder characterized by recurrent upper airway obstruction, leading to disrupted sleep and various health complications. Positional OSA (POSA) refers to patients whose OSA severity is significantly influenced by body position, especially when lying supine. This study aimed to evaluate the polysomnographic characteristics of POSA and non-positional OSA (non-POSA) and to assess their clinical implications.
View Article and Find Full Text PDFPulm Ther
January 2025
MSc Program in Sleep Medicine, Medical School, Democritus University of Thrace, Alexandroupolis, Greece.
The coexistence of chronic obstructive pulmonary disease (COPD) and obstructive sleep apnea (OSA) in the same patient is referred to as overlap syndrome (OS). Patients with OS suffer more frequently from cardiovascular disease (CVD) and carry a higher risk of COPD-related exacerbations than patients with COPD alone, especially when OSA is left untreated. Based on recent evidence, triple therapy, namely inhaled corticosteroid/long-acting muscarinic antagonist/long-acting beta-agonist (ICS-LABA-LAMA), is a treatment strategy in COPD patients with a history of exacerbations and/or CVD comorbidity.
View Article and Find Full Text PDFBr J Oral Maxillofac Surg
November 2024
Department of Otolaryngology-Head and Neck Surgery, Medical University of South Carolina, Charleston, SC 29425, USA. Electronic address:
The aim of this paper was to systematically review and compare the Apnoea-Hypopnoea Index (AHI), Lowest O Saturation (LSAT), Oxygen Desaturation Index (ODI), Epworth Sleep Scale (ESS), and Body Mass Index (BMI) between dentofacial (skeletal) classes I, II, and III before and after maxillomandibular advancement (MMA) for Obstructive Sleep Apnoea (OSA). The PubMed, Scopus, and CINAHL databases were searched from inception to 23 November 2022. Two reviewers screened for articles that reported occlusion/malocclusion class type as I, II, or III, and reported preoperative and postoperative AHI, LSAT, ODI, ESS, and/or BMI.
View Article and Find Full Text PDFRev Cardiovasc Med
December 2024
University Center for Research & Development, Chandigarh University, 140413 Mohali, India.
Background: Obstructive sleep apnea (OSA) is a severe condition associated with numerous cardiovascular complications, including heart failure. The complex biological and morphological relationship between OSA and atherosclerotic cardiovascular disease (ASCVD) poses challenges in predicting adverse cardiovascular outcomes. While artificial intelligence (AI) has shown potential for predicting cardiovascular disease (CVD) and stroke risks in other conditions, there is a lack of detailed, bias-free, and compressed AI models for ASCVD and stroke risk stratification in OSA patients.
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