Screening for Obstructive Sleep Apnea in an Atrial Fibrillation Population: What's the Best Test?

How Would You Manage This Patient With Recent-Onset Atrial Fibrillation? Grand Rounds Discussion From Beth Israel Deaconess Medical Center

Atrial fibrillation (AF) is the most common arrhythmia. Risk factors for AF include obstructive sleep apnea, physical inactivity, obesity, cigarette use, and alcohol misuse. Atrial fibrillation substantially increases the risk for stroke and is associated with higher rates of mortality than for individuals without AF. Strategies to prevent these risk factors and to optimize those that already exist reduce the risk for subsequent AF. Physicians play an important role in proposing strategies to reduce the risk for AF among patients. Decision making regarding management of AF is often complex and requires consideration of symptoms, burden of AF (the percentage of time in AF), comorbid conditions that increase stroke risk, and the risk for bleeding. In particular, novel risk scoring systems to predict stroke risk, and consideration of factors beyond those in these tools, refine the ability to identify patients with AF who are most likely to benefit from anticoagulation to reduce stroke risk. Early use of catheter ablation of AF in selected patients improves symptoms and reduces the potential for progression from intermittent to persistent AF. A 2023 collaborative guideline from the American College of Cardiology, American Heart Association, American College of Chest Physicians, and the Heart Rhythm Society addressed multiple aspects of care of patients with AF. Here, a general cardiologist and a cardiac electrophysiologist discuss recommendations derived from this guideline and how to apply them to the care of a particular patient.

Recommended assessment and management of sleep disordered breathing in patients with atrial fibrillation, hypertension and heart failure: Taiwan Society of Cardiology/Taiwan Society of sleep Medicine/Taiwan Society of pulmonary and Critical Care Medicine joint consensus statement

Sleep disordered breathing (SDB) is highly prevalent and may be linked to cardiovascular disease in a bidirectional manner. The Taiwan Society of Cardiology, Taiwan Society of Sleep Medicine and Taiwan Society of Pulmonary and Critical Care Medicine established a task force of experts to evaluate the evidence regarding the assessment and management of SDB in patients with atrial fibrillation (AF), hypertension and heart failure with reduced ejection fraction (HFrEF). The GRADE process was used to assess the evidence associated with 15 formulated questions. The task force developed recommendations and determined strength (Strong, Weak) and direction (For, Against) based on the quality of evidence, balance of benefits and harms, patient values and preferences, and resource use. The resulting 11 recommendations are intended to guide clinicians in determining which the specific patient-care strategy should be utilized by clinicians based on the needs of individual patients.

Atrial fibrillation-specific refinement of the STOP-Bang sleep apnoea screening questionnaire: insights from the Virtual-SAFARI study

BACKGROUND

Sleep-disordered breathing (SDB) is prevalent in up to 50% of patients referred for atrial fibrillation (AF) catheter ablation (CA). Currently, it remains unclear how to improve pre-selection for SDB screening in patients with AF.

AIM

We aimed to (1) assess the accuracy of the STOP-Bang screening questionnaire for detection of SDB within an AF population referred for CA; (2) derive a refined, AF-specific SDB score to improve pre-selection.

METHODS

Consecutive AF patients referred for CA without a history of SDB and/or SDB screening were included. Patients were digitally referred to the previously implemented Virtual-SAFARI SDB screening and management pathway including a home sleep test. An apnoea-hypopnoea index (AHI) of  ≥ 15 was interpreted as moderate-to-severe SDB. Logistic regression analysis was used to assess characteristics associated with moderate-to-severe SDB to refine pre-selection for SDB screening.

RESULTS

Of 206 included patients, 51% were diagnosed with moderate-to-severe SDB. The STOP-Bang questionnaire performed poorly in detecting SDB, with an area under the receiver operating characteristic curve (AUROC) of 0.647 (95% Confidence-Interval (CI) 0.573-0.721). AF-specific refinement resulted in the BOSS-GAP score. Therein, BMI with cut-off point ≥ 27 kg/m2 and previous stroke or transient ischaemic attack (TIA) were added, while tiredness and neck circumference were removed. The BOSS-GAP score performed better with an AUROC of 0.738 (95% CI 0.672-0.805) in the overall population.

CONCLUSION

AF-specific refinement of the STOP-Bang questionnaire moderately improved detection of SDB in AF patients referred for CA. Whether questionnaires bring benefits for pre-selection of SDB compared to structural screening in patients with AF requires further studies.

TRIAL REGISTRATION NUMBER

ISOLATION was registered NCT04342312, 13-04-2020.

Vagal milieu or electrophysiologic substrate? The link between atrial fibrillation and obstructive sleep apnea

Atrial fibrillation is the most common cardiac arrhythmia with its prevalence expected to increase to 12.1 million people in the United States by 2030. Chronic underlying conditions that affect the heart and lungs predispose patients to develop atrial fibrillation. Obstructive sleep apnea is strongly associated with atrial fibrillation. Several pathophysiological mechanisms have been proposed to elucidate this relationship which includes electrophysiological substrate modification and the contribution of the autonomic nervous system. In this comprehensive review, we highlight important relationships and plausible causality between obstructive sleep apnea and atrial fibrillation which will improve our understanding in the evaluation, management, and prevention of atrial fibrillation. This is the most updated comprehensive review of the relationship between obstructive sleep apnea and atrial fibrillation.

Diagnosis of Obstructive Sleep Apnea in Patients with Associated Comorbidity

Obstructive sleep apnea (OSA) is a heterogeneous disease with many physiological implications. OSA is associated with a great diversity of diseases, with which it shares common and very often bidirectional pathophysiological mechanisms, leading to significantly negative implications on morbidity and mortality. In these patients, underdiagnosis of OSA is high. Concerning cardiorespiratory comorbidities, several studies have assessed the usefulness of simplified screening tests for OSA in patients with hypertension, COPD, heart failure, atrial fibrillation, stroke, morbid obesity, and in hospitalized elders.The key question is whether there is any benefit in the screening for the existence of OSA in patients with comorbidities. In this regard, there are few studies evaluating the performance of the various diagnostic procedures in patients at high risk for OSA. The purpose of this chapter is to review the existing literature about diagnosis in those diseases with a high risk for OSA, with special reference to artificial intelligence-related methods.

Cardiorespiratory Polygraphy for Detection of Obstructive Sleep Apnea in Patients With Atrial Fibrillation

Background: Obstructive sleep apnea (OSA) is a modifiable risk factor of atrial fibrillation (AF) but is underdiagnosed in these patients due to absence of good OSA screening pathways. Polysomnography (PSG) is the gold standard for diagnosing OSA but too resource-intensive as a screening tool. We explored whether cardiorespiratory polygraphy (PG) devices using an automated algorithm for Apnea-Hypopnea Index (AHI) determination can meet the requirements of a good screening tool in AF patients.

Methods: This prospective study validated the performance of three PGs [ApneaLink Air (ALA), SOMNOtouch RESP (STR) and SpiderSAS (SpS)] in consecutive AF patients who were referred for PSG evaluation. Patients wore one of the three PGs simultaneously with PSG, and a different PG during each of three consecutive nights at home. Severity of OSA was classified according to the AHI during PSG (<5 = no OSA, 5–14 = mild, 15–30 = moderate, >30 = severe).

Results: Of the 100 included AF patients, PSG diagnosed at least moderate in 69% and severe OSA in 33%. Successful PG execution at home was obtained in 79.1, 80.2 and 86.8% of patients with the ALA, STR and SpS, respectively. For the detection of clinically relevant OSA (AHI ≥ 15), an area under the curve of 0.802, 0.772 and 0.803 was calculated for the ALA, STR and SpS, respectively.

Conclusions: This study indicates that home-worn PGs with an automated AHI algorithm can be used as OSA screening tools in AF patients. Based on an appropriate AHI cut-off value for each PG, the device can guide referral for definite PSG diagnosis.