Obstructive sleep apnea (OSA) is a highly prevalent and underdiagnosed sleep disorder with significant cardiovascular implications, namely in atrial fibrillation (AF) patients. Despite its clinical relevance, OSA prevalence among AF patients and the diagnostic strategies used remain heterogeneous across studies, complicating screening, and treatment pathways. Our aim was to synthesize recent evidence on OSA prevalence in AF populations and to critically evaluate the diagnostic methods and screening strategies employed in clinical studies, by conducting a systematic review using PubMed and Google Scholar to identify original clinical studies published between January-2019 and December-2024. Inclusion criteria targeted adult AF populations assessed for OSA or sleep-disordered breathing. The results were analyzed by two independent reviewers. Non-concordances were resolved by consensus. Data extracted included study characteristics, population profiles, diagnostic approaches, prevalence rates, symptom profiles, and clinical correlates. Thirty-eight studies were included, comprising predominantly observational studies. Prevalence estimates of OSA in AF populations ranged from 5% to 90%, with most studies reporting rates > 60%. A consistent burden of moderate-to-severe OSA was observed. Diagnostic methods varied widely, from polysomnography (PSG) and home sleep apnea testing to pacemaker-derived monitoring and questionnaires such as STOP-Bang and Epworth Sleepiness Scale (ESS). Underdiagnosis was attributed to minimal symptomatology, lack of physician awareness, and reliance on subjective tools. Several studies highlighted the limited sensitivity of standard screening instruments in AF populations and advocated for objective testing even in asymptomatic patients. Marked heterogeneity in study designs, diagnostic methods, and populations precluded quantitative synthesis and limited direct comparisons. Objective diagnostic testing, particularly PSG, is essential to improve OSA detection rates and guide individualized management. Integration of structured screening protocols into AF care—especially for high-risk patients—and interdisciplinary collaboration are critical.

With the purpose of establishing a consensus around clinical orientations for professionals involved in managing patients with sleep breathing disorders (SBD), an interdisciplinary group of scientific societies involved in this field discussed and reviewed all the published international guidelines from the American Dental Association, American Academy of Sleep Medicine, American Academy of Dental Sleep Medicine and the European counterparts. Treatment of SBD is multidisciplinary and should be made in concert with the patient, the sleep physician, and the qualified dentist to solve the individual, social, and economic burden of the disease,. This consensus document represents the current thinking of a team of Portuguese experts on managing patients with SBD based on the available evidence.

Introduction: Obstructive sleep apnea syndrome (OSAS) is highly prevalent in patients with atrial fibrillation (AF) and may influence rhythm control outcomes. Accurate diagnosis is essential but depends on the criteria used to define respiratory events. This study aimed to evaluate how the inclusion of EEG arousals in hypopnea scoring affects the diagnosis and severity classification of OSAS in patients with AF. Patients and methods: We conducted a prospective analysis of 88 consecutive patients with AF (paroxysmal or persistent) referred for sleep evaluation with ambulatory type II polysomnography (PSG). Hypopneas were scored according to two criteria: [1] ≥ 3% oxygen desaturation, and [2] ≥ 3% desaturation or EEG-defined arousal. Apnea–hypopnea index (AHI) and OSAS severity were compared across both definitions. Results: Participants had a mean age of 63 ± 9.7 years, were predominantly male (68%), and had a mean BMI of 30 ± 4.8 kg/m². OSAS was diagnosed in 100% of patients. Using the desaturation-only criterion, OSAS severity was classified as mild in 24.7%, moderate in 31.8%, and severe in 43.5% of patients. In contrast, scoring hypopneas based on desaturation or arousal led to reclassification: 5.7% mild, 17.0% moderate, and 77.3% severe. Thirty-one patients classified as severe OSAS were missed using desaturation-only scoring. The number of hypopneas detected was significantly higher when arousals were included (200.0 ± 105.6 vs. 81.9 ± 48.9; p < 0.001), with a moderate positive correlation between the two methods (r = 0.436). Conclusion: The use of arousal-inclusive criteria significantly increases OSAS detection and alters severity classification in patients with AF. Relying solely on oxygen desaturation may lead to underdiagnosis and misclassification, particularly in non-desaturating patients. Incorporating EEG arousals into hypopnea scoring provides a more accurate assessment of disease burden and may support more effective, individualized treatment strategies.

Apresenta-se um caso clínico de policitemia secundária à síndrome de apneia obstrutiva do sono (SAOS), sem patologia respiratória concomitante. Doente do sexo masculino, com SAOS ligeira (Índice de Apneia-Hipopneia = 14,8/h e dessaturação mínima de oxigénio de 82%), função nasal comprometida e policitemia (hemoglobina = 18 g/dL; hematócrito > 50%). Iniciou tratamento com ventilação em pressão positiva contínua (CPAP), que permitiu a resolução completa dos eventos respiratórios. Contudo, o doente referiu desconforto com o CPAP, pelo que se realizou endoscopia do sono induzido por fármacos com poligrafia em simultâneo. Procedeu-se à redução intersticial dos cornetos inferiores por radiofrequência e a fisioterapia, o que melhorou significativamente a adesão ao CPAP, sem desconforto adicional. Ao fim de dois meses, verificou-se redução da hemoglobina para 16,4 g/dL e do hematócrito para 48%, ambos estáveis. Este caso reforça a importância de uma abordagem multidisciplinar e individualizada na avaliação e tratamento da SAOS.

Purpose: Obstructive sleep apnea is a common yet underdiagnosed sleep disorder in patients with atrial fibrillation (AF). Although polysomnography (PSG) is the gold standard, its costs and complexity limit routine use. Home sleep apnea testing (HSAT) is a more accessible alternative, but it lacks electroencephalography (EEG), limiting direct assessment of sleep stages and arousals. This study evaluates the performance of DeepRESP, an automated scoring solution that infers sleep stages and arousals using respiratory inductance plethysmography (RIP) signals and scoring respiratory events, in a cohort of patients with AF. Methods: In this prospective cohort study, consecutive AF patients underwent ambulatory type II PSG. Automated respiratory event scoring, sleep staging and arousal identification were performed with DeepRESP using only HSAT signals. Performance metrics included epoch-level positive and negative percent agreement, complemented by concordance analyses for the apnea–hypopnea index (AHI), arousal index (ArI), and total sleep time (TST), using Bland–Altman analysis and intraclass correlation coefficients (ICC), with manually scored PSG as reference. Results: A total of 88 patients (67.0% male) were included. Women were older than men (66.7 ± 7.2 vs. 61.27 ± 10.3 years, p = 0.015). Epoch-level overall percent agreement (OPA) with manual scoring was 0.91 for Wake, 0.95 for rapid eye movement sleep (REM), and 0.87 for non-rapid eye movement sleep (NREM). Arousal detection OPA was 0.80. Strong agreement was observed for the apnea–hypopnea index (ICC = 0.92), TST (ICC = 0.82), and ArI (ICC = 0.83). Conclusion: In AF patients, DeepRESP reliably estimates sleep architecture, arousals, and sleep apnea severity from HSAT signal, offering a scalable, EEG-independent diagnostic approach. Brief summary: Current knowledge/study rationale: Obstructive sleep apnea is highly prevalent and often underdiagnosed in atrial fibrillation patients, yet standard home testing lacks the neurophysiological channels necessary to detect arousals and accurate sleep architecture. This study was conducted to validate a deep learning-based system capable of inferring these critical metrics exclusively from home sleep testing signals, improving diagnostics performance within this specific cardiovascular population. Study impact: The findings demonstrate that artificial intelligence-driven analysis of breathing patterns achieves high concordance with polysomnography for key diagnostic indices, including arousal index, total sleep time and apnea severity via the apnea–hypopnea index. This provides a scalable, precise diagnostic pathway that bypasses the confounding effects of arrhythmias and cardiac medications, facilitating improved management of sleep-disordered breathing in atrial fibrillation patients.