Atrial Fibrillation in Obstructive Sleep Apnea Patients: Mechanisms, Risk Factors, and Management Strategies

Sankaranarayanan, Rajagopal; Bansal, Ankit; Yanamaladoddi, Vishnu R; Sarvepalli, Sai S; Vemula, Shree L; Aramadaka, Saikumar; Mannam, Raam

doi:10.7759/cureus.36282

Cited by 3 publications

(2 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, this might help reduce and better control the symptoms of AF. 50 , 51 Despite this, more evidence is needed on how management strategies and screening processes can be incorporated. Such studies might provide specialists with valuable insights into how to build targeted and individualized treatment approaches for both patients with OSA and AF.…”

Section: Recommendationsmentioning

confidence: 99%

Prevalence of Obstructive Sleep Apnea Among Atrial Fibrillation Patients: A Cross-Sectional Study from Jordan

Ibdah,

Zaitoun,

Altawalbeh

et al. 2024

JMDH

View full text Add to dashboard Cite

Background Obstructive Sleep Apnea (OSA) is a common respiratory disorder that causes intermittent upper airway collapse during sleep and can lead to various acute cardiovascular complications. Atrial Fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with an increased risk of cardiovascular hospitalization and all-cause mortality. Our study aimed to investigate the prevalence of individuals with AF and those considered at high risk for OSA. Methods A cross-sectional study was conducted with a population comprising patients who had visited KAUH cardiology clinics between 2017–2019; subjects were categorized into AF patients and general cardiology patients. Patients were surveyed for OSA using the Berlin Questionnaire to assess the degree of OSA symptoms and to classify patients into high- or low-risk groups based on their responses. Results Of the 656 patients, 545 met our inclusion criteria, of whom 192 were diagnosed with AF. Comparable demographic characteristics were observed between the AF and non-AF groups, barring higher rates of obesity ( p =0.001) and smoking ( p =0.042) in the AF group. The prevalence of high-risk OSA was significantly higher in AF patients (68.2%) compared to non-AF patients (29.4%), with an adjusted odds ratio of 2.473 times (95% CI: 1.434 −4.266, p =0.001) greater for AF. The age, gender, and BMI categories did not differ significantly between the two groups. Binary logistic regression revealed significant associations between OSA and risk factors such as asthma (OR=4.408, 95% CI: 2.634–7.376, p=0.001). Conclusion These results serve to display a statistically significant increase in high-risk OSA in existing AF patients, irrespective of the presence of conventional OSA risk factors; this could imply a more immediate and direct relationship between both diseases and calls to include routine screening for OSA in patients diagnosed, newly or otherwise, with AF.

show abstract

Section: Recommendationsmentioning

confidence: 99%

Prevalence of Obstructive Sleep Apnea Among Atrial Fibrillation Patients: A Cross-Sectional Study from Jordan

Ibdah,

Zaitoun,

Altawalbeh

et al. 2024

JMDH

View full text Add to dashboard Cite

show abstract

“…Moreover, there is strong evidence of a relationship between AF and obstructive sleep apnea (OSA). Cardiovascular disease and OSA are closely linked with respect to their pathophysiology and epidemiology [2]. The atrial arrhythmogenic mechanism is based on imbalances of the autonomic nervous system, chronic intermittent hypoxia, inflammation, and swings in thoracic pressure during different phases of the acute apnoeic episodes [3].…”

Section: Introductionmentioning

confidence: 99%

From Sleep Patterns to Heart Rhythms: Predicting Atrial Fibrillation from Overnight Polysomnograms

Koscova,

Rad,

Nasiri

et al. 2024

Preprint

View full text Add to dashboard Cite

BackgroundAtrial fibrillation (AF) is often asymptomatic and thus under-observed. Given the high risks of stroke and heart failure among patients with AF, early prediction and effective management are crucial. Importantly, obstructive sleep apnea is highly prevalent among AF patients (60–90%); therefore, electrocardiogram (ECG) analysis from polysomnography (PSG), a standard diagnostic tool for subjects with suspected sleep apnea, presents a unique opportunity for the early prediction of AF. Our goal is to identify individuals at a high risk of developing AF in the future from a single-lead ECG recorded during standard PSGs.MethodsWe analyzed 18,782 single-lead ECG recordings from 13,609 subjects at Massachusetts General Hospital, identifying AF presence using ICD-9/10 codes in medical records. Our dataset comprises 15,913 recordings without a medical record for AF and 2,056 recordings from patients who were first diagnosed with AF between 1 day to 15 years after the PSG recording. The PSG data were partitioned into training, validation, and test cohorts. In the first phase, a signal quality index (SQI) was calculated in 30-second windows and those with SQI<0.95 were removed. From each remaining window, 150 hand-crafted features were extracted from time, frequency, time-frequency domains, and phase-space reconstructions of the ECG. A compilation of 12 statistical features summarized these window-specific features per recording, resulting in 1,800 features. We then updated a pre-trained deep neural network and data from the PhysioNet Challenge 2021 using transfer-learning to discriminate between recordings with and without AF using the same Challenge data. The model was applied to the PSG ECGs in 16-second windows to generate the probability of AF for each window. From the resultant probability sequence, 13 statistical features were extracted. Subsequently, we trained a shallow neural network to predict future AF using the extracted ECG and probability features.ResultsOn the test set, our model demonstrated a sensitivity of 0.67, specificity of 0.81, and precision of 0.3 for predicting AF. Further, survival analysis for AF outcomes, using the log-rank test, revealed a hazard ratio of 8.36 (p-value of 1.93 × 10−52).ConclusionsOur proposed ECG analysis method, utilizing overnight PSG data, shows promise in AF prediction despite a modest precision indicating the presence of false positive cases. This approach could potentially enable low-cost screening and proactive treatment for high-risk patients. Ongoing refinement, such as integrating additional physiological parameters could significantly reduce false positives, enhancing its clinical utility and accuracy.

show abstract

Obstructive Sleep Apnea Characterization: A Multimodal Cross-Recurrence-Based Approach for Investigating Atrial Fibrillation

Rinkevičius,

Lázaro,

Gil

et al. 2024

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

Atrial Fibrillation in Obstructive Sleep Apnea Patients: Mechanisms, Risk Factors, and Management Strategies

Cited by 3 publications

References 57 publications

Prevalence of Obstructive Sleep Apnea Among Atrial Fibrillation Patients: A Cross-Sectional Study from Jordan

Prevalence of Obstructive Sleep Apnea Among Atrial Fibrillation Patients: A Cross-Sectional Study from Jordan

From Sleep Patterns to Heart Rhythms: Predicting Atrial Fibrillation from Overnight Polysomnograms

Obstructive Sleep Apnea Characterization: A Multimodal Cross-Recurrence-Based Approach for Investigating Atrial Fibrillation

Contact Info

Product

Resources

About