Nocturnal gastro-oesophageal reflux and respiratory symptoms are increased in sleep apnoea: comparison with the general population.

AIM: To assess respiratory symptoms and nocturnal gastro-oesophageal reflux (nGER) among untreated obstructive sleep apnoea (OSA) patients, compared with the general population. Also, if nGER associates differently with respiratory symptoms among OSA patients. METHODS: 2 study cohorts were included: 822 newly diagnosed subjects with moderate-severe OSA and 738 Icelandic general population study participants. All participants answered the same questionnaires. Those reporting nGER symptoms at least once per week were defined as 'with nGER'; those without nGER symptoms and without nGER medication were defined as 'no nGER'; and other participants were defined as having 'possible nGER'. Propensity score-based weights were used to minimise confounding and selection bias and facilitate causal interpretations. RESULTS: The prevalence of nGER among OSA patients was 14.1%, compared with 5.8% in the general population. This increased prevalence in OSA was not explained by differences in age, gender, body mass index, smoking, hypertension and diabetes (adjusted OR (95% CI)=3.79 (2.24 to 6.43)). OSA patients 'with nGER' and with 'possible nGER' reported more wheezing (44% and 44% vs 25%, respectively) and productive cough (47% and 42% vs 29%, respectively), compared with OSA patients with 'no nGER'. The same pattern was seen in the general population, although with a generally lower prevalence. The effect of nGER on respiratory symptoms was similar between the two cohorts. CONCLUSION: nGER was more often reported among untreated moderate-severe OSA patients than in the general population. Participants with nGER had more wheezing and productive cough, both among untreated OSA patients and in the general population.

The epidemiological characteristics of sleep disordered breathing in congestive heart failure: A prospective, single centre study in Southeast Asia.

BACKGROUND: The presence of sleep-disordered breathing (SDB) in congestive heart failure (CHF) is associated with poor prognosis and is underdiagnosed despite advances in CHF management. The prevalence of SDB in CHF remains understudied in South East Asia. METHODS: A prospective, observational single-centre study was conducted where 116 consecutive patients in a specialised heart failure clinic underwent level 1, attended polysomnography (PSG). RESULTS: The prevalence of SDB was 78% using the apnoea-hypopnea index (AHI), AHI ⩾ 5/h threshold, and 59% with the AHI ⩾ 15/h threshold. Obstructive sleep apnoea (OSA) was the predominant type of SDB and was associated with increased body mass index and neck circumference. STOP-BANG was predictive of SDB, especially in men. Central sleep apnoea (CSA) patients had worse sleep indexes and lower awake arterial carbon dioxide. SDB was also homogenously present in preserved ejection fraction (EF) CHF. CONCLUSION: Most of the CHF patients were found to have SDB with the utility of PSG. Local CHF guidelines should include sleep testing for all patients with CHF.The study is registered on ClinicalTrials.gov (NCT05332223) as 'The Epidemiological Characteristics of SDB in Patients with Reduced or Preserved EF CHF'.

Management of paediatric sleep-disordered breathing.

Paediatric sleep-disordered breathing is a common condition which varies in severity from snoring to obstructive sleep apnoea. Paediatric sleep-disordered breathing is usually diagnosed clinically, with investigations such as polysomnography reserved for more complex cases. Management can involve watching and waiting, medical or adjunct treatments and adenotonsillectomy. National working groups have sought to standardise the pathway for surgery and improve the management of surgical and anaesthetic complications. Current guidelines use age, weight and comorbidities to stratify risk for these surgical cases. This article summarises these recommendations and outlines the important factors that indicate cases that may be more suitable for management in secondary and tertiary units. Appropriate case selection will reduce pressure on tertiary units while maintaining training opportunities in district general hospitals.

Sleep-disordered breathing in heart failure.

PURPOSE OF REVIEW: This review addresses the evolving intersection of sleep-disordered breathing (SDB) and heart failure, a topic of increasing clinical significance due to the high prevalence of SDB in heart failure patients and its impact on morbidity and mortality. It reflects recent advancements in diagnostic methodologies and therapeutic strategies. It emphasizes the need for heightened awareness among healthcare providers about the complex relationship between SDB and various forms of heart failure. RECENT FINDINGS: Recent studies underscore the high incidence of SDB in heart failure patients, varying with the cause of heart failure. Emerging diagnostic tools, including home sleep tests and advanced inpatient screening methods, have improved the early detection and accurate diagnosis of SDB. Novel treatment modalities, like hypoglossal and phrenic nerve stimulation, are promising, especially where conventional therapies are inadequate. The review also discusses the complexities of managing SDB in the context of different heart failure subtypes. SUMMARY: Findings from recent literature suggest that improved screening, diagnosis, and innovative treatment of SDB in heart failure patients can reduce morbidity, mortality, and healthcare costs. This review emphasizes the need for personalized treatment approaches tailored to individual patient profiles, highlighting the potential of new technologies and multidisciplinary strategies in clinical practice.

Algorithmic detection of sleep-disordered breathing using respiratory signals: a systematic review.

Background and Objective. Sleep-disordered breathing (SDB) poses health risks linked to hypertension, cardiovascular disease, and diabetes. However, the time-consuming and costly standard diagnostic method, polysomnography (PSG), limits its wide adoption and leads to underdiagnosis. To tackle this, cost-effective algorithms using single-lead signals (like respiratory, blood oxygen, and electrocardiogram) have emerged. Despite respiratory signals being preferred for SDB assessment, a lack of comprehensive reviews addressing their algorithmic scope and performance persists. This paper systematically reviews 2012-2022 literature, covering signal sources, processing, feature extraction, classification, and application, aiming to bridge this gap and provide future research references.Methods. This systematic review followed the registered PROSPERO protocol (CRD42022385130), initially screening 342 papers, with 32 studies meeting data extraction criteria.Results. Respiratory signal sources include nasal airflow (NAF), oronasal airflow (OAF), and respiratory movement-related signals such as thoracic respiratory effort (TRE) and abdominal respiratory effort (ARE). Classification techniques include threshold rule-based methods (8), machine learning models (13), and deep learning models (11). The NAF-based algorithm achieved the highest average accuracy at 94.11%, surpassing 78.19% for other signals. Hypopnea detection sensitivity with single-source respiratory signals remained modest, peaking at 73.34%. The TRE and ARE signals proved to be reliable in identifying different types of SDB because distinct respiratory disorders exhibited different patterns of chest and abdominal motion.Conclusions. Multiple detection algorithms have been widely applied for SDB detection, and their accuracy is closely related to factors such as signal source, signal processing, feature selection, and model selection.

Impaired post-sleep apnea autonomic arousals in patients with drug-resistant epilepsy.

OBJECTIVE: Sudden and unexpected deaths in epilepsy (SUDEP) pathophysiology may involve an interaction between respiratory dysfunction and sleep/wake state regulation. We investigated whether patients with epilepsy exhibit impaired sleep apnea-related arousals. METHODS: Patients with drug-resistant (N = 20) or drug-sensitive (N = 20) epilepsy and obstructive sleep apnea, as well as patients with sleep apnea but without epilepsy (controls, N = 20) were included. We explored (1) the respiratory arousal threshold based on nadir oxygen saturation, apnea-hypopnea index, and fraction of hypopnea among respiratory events; (2) the cardiac autonomic response to apnea/hypopnea quantified as percentages of changes from the baseline in RR intervals (RRI), high (HF) and low (LF) frequency powers, and LF/HF. RESULTS: The respiratory arousal threshold did not differ between groups. At arousal onset, RRI decreased (-9.42%) and LF power (179%) and LF/HF ratio (190%) increased. This was followed by an increase in HF power (118%), p < 0.05. The RRI decrease was lower in drug-resistant (-7.40%) than in drug-sensitive patients (-9.94%) and controls (-10.91%), p < 0.05. LF and HF power increases were higher in drug-resistant (188%/126%) than in drug-sensitive patients (172%/126%) and controls (177%/115%), p < 0.05. CONCLUSIONS: Cardiac reactivity following sleep apnea is impaired in drug-resistant epilepsy. SIGNIFICANCE: This autonomic dysfunction might contribute to SUDEP pathophysiology.

Evaluation of risk factors for sleep-disordered breathing in dogs.

BACKGROUND: Brachycephalic dogs display sleep-disordered breathing (SDB). The risk factors for SDB remain unknown. OBJECTIVES: To identify risk factors for SDB. We hypothesized that brachycephaly, increasing severity of brachycephalic obstructive airway syndrome (BOAS), excess weight, and aging predispose to SDB. ANIMALS: Sixty-three privately owned pet dogs were prospectively recruited: 28 brachycephalic and 35 normocephalic (mesaticephalic or dolicocephalic) dogs. METHODS: Prospective observational cross-sectional study with convenience sampling. Recording with the neckband was done over 1 night at each dog's home. The primary outcome measure was the obstructive respiratory event index (OREI). Body condition score (BCS) was assessed, and BOAS severity was graded for brachycephalic dogs. RESULTS: Brachycephaly was a significant risk factor for high OREI value (ratio of the geometric means 5.6, 95% confidence interval [CI] 3.2-9.9; P < .001) but aging was not (1.1, 95% CI 1.0-1.2; P = .2). Excess weight, defined as a BCS of over 5/9, (3.5, 95% CI 1.8-6.7; P < .001) was a significant risk factor. In brachycephalic dogs, BOAS-positive class (moderate or severe BOAS signs) was a significant risk factor (2.5, 95% CI 1.1-5.6; P = .03). CONCLUSIONS AND CLINICAL IMPORTANCE: Brachycephaly decreases welfare in a multitude of ways, including disrupting sleep. Brachycephaly, increasing severity of BOAS and excess weight are risk factors for obstructive SDB.

Detection of cardiac abnormalities from 12-lead ecg using complex wavelet sub-band features.

AIM OF THE STUDY: This research endeavours to optimize cardiac anomaly detection by introducing a method focused on selecting the most effective Daubechis wavelet families. The principal aim is to differentiate between cardiac states that are normal and abnormal by utilizing longer electrocardiogram (ECG) signal events based on the Apnea ECG dataset. Apnea ECG is often used to detect sleep apnea, a sleep disorder characterized by repeated interruptions in breathing during sleep. By using machine learning methods, such as Principal Component Analysis (PCA) and different classifiers, the goal is to improve the precision of cardiac irregularity identification. Used method. To extract important statistical and sub-band information from lengthy ECG signal episodes, the study uses a novel method that combines discrete wavelet transform with Principal Component Analysis (PCA) for dimension reduction. The methodology focuses on successfully categorizing ECG signals by utilizing several classifiers, including multilayer perceptron (MLP) neural network, Ensemble Subspace K-Nearest Neighbour(KNN), and Ensemble Bagged Trees, together with varied Daubechis wavelet families (db2, db3, db4, db5, db6). Brief Description of Results. The results emphasize the importance of the chosen Daubechis wavelet family, db5, and its superiority in ECG representation. The method distinguishes normal and abnormal ECG signals well on the Physionet Apnea ECG database. The Neural Network-based method accurately recognizes 100% of healthy signals and 97.8% of problematic ones with 98.6% accuracy. FINDINGS: The Ensemble Subspace K-Nearest Neighbour (KNN) and Ensemble Bagged Trees methods got 87.1% accuracy and 0.89 and 0.87 AOC curve values on this dataset, showing that the method works. Precision values of 0.96, 0.86, and 0.86 for MLP Neural Network, KNN Subspace, and Ensemble Bagged Trees confirm their robustness. These findings suggest wavelet families and machine learning can improve cardiac abnormality detection and categorization.

Quantitative detection of sleep apnea in adults using inertial measurement unit embedded in wristwatch wearable devices.

Sleep apnea (SA) is associated with risk of cardiovascular disease, cognitive decline, and accidents due to sleepiness, yet the majority (over 80%) of patients remain undiagnosed. Inertial measurement units (IMUs) are built into modern wearable devices and are capable of long-term continuous measurement with low power consumption. We examined if SA can be detected by an IMU embedded in a wristwatch device. In 122 adults who underwent polysomnography (PSG) examinations, triaxial acceleration and triaxial gyro signals from the IMU were recorded during the PSG. Subjects were divided into a training group and a test groups (both n = 61). In the training group, an algorithm was developed to extract signals in the respiratory frequency band (0.13-0.70 Hz) and detect respiratory events as transient (10-90 s) decreases in amplitude. The respiratory event frequency estimated by the algorithm correlated with the apnea-hypopnea index (AHI) of the PSG with r = 0.84 in the test group. With the cutoff values determined in the training group, moderate-to-severe SA (AHI ≥ 15) was identified with 85% accuracy and severe SA (AHI ≥ 30) with 89% accuracy in the test group. SA can be quantitatively detected by the IMU embedded in wristwatch wearable devices in adults with suspected SA.

Sleep Disordered Breathing and Risk for ADHD: Review of Supportive Evidence and Proposed Underlying Mechanisms.

BACKGROUND: Accumulating evidence suggests that sleep disordered breathing (SDB) is under-recognized in youth and adults with ADHD. SDB may contribute to exacerbating pre-existing ADHD symptoms and may play a role in the development of cognitive deficits that may mimic ADHD symptoms. METHOD: We conducted a focused review of publications on cross-prevalence, overlapping clinical and neurobiological characteristics and possible mechanisms linking SDB and ADHD. RESULTS: Exiting studies suggest that co-occurrence of SDB and ADHD is as high as 50%, with frequent overlap of clinical symptoms such as distractibility and inattention. Mechanisms linking these conditions may include hypoxia during sleep, sleep fragmentation and activation of inflammation, all of which may affect brain structure and physiology to produce disturbances in attention. CONCLUSIONS: The relationship between SDB and ADHD symptoms appear well-supported and suggests that more research is needed to better optimize procedures for SDB assessment in youth being evaluated and/or treated for ADHD.

Secondhand Smoke Exposure Measured in Urinary Cotinine Levels and Severity of Pediatric Sleep Apnea.

Importance: Exposure to secondhand smoke has been associated with numerous health problems in children, including obstructive sleep apnea. Secondhand smoke exposure may be a risk factor for increased pediatric sleep apnea severity. Objectives: To assess the association of secondhand smoke exposure (SHSe), quantified by urinary cotinine levels, with severity of obstructive sleep apnea (OSA) in children. Design, Setting, and Participants: This was a prospective cohort trial including pediatric patients from 3 to 16 years of age with sleep-disordered breathing who underwent a polysomnogram at a tertiary-level children's hospital in the US in either March 2014 to October 2016 or March 2020 to March 2021. Urine specimens were analyzed for cotinine, an important metabolite of nicotine. Each child's caregiver completed a validated SHSe questionnaire. Data were analyzed from February to June 2023. Exposure: OSA and secondhand smoke. Main Outcome and Measures: SHSe and severity of pediatric OSA, quantified by urinary cotinine levels and obstructive apnea hypopnea index (AHI) scores. Secondary outcomes were association of urinary cotinine levels with nadir oxygen saturation, sleep-related quality of life measured by the OSA-18 questionnaire, and caregiver-reported smoking habits (collected through a questionnaire). Results: The study included 116 patients with a median (IQR) age of 6 (5-9) years, among whom 51 (45%) had obesity. The median (IQR) AHI was 3.0 (1.2-8.0), with 28 children (30.0%) having severe disease (AHI >10). Thirty-four children (29.0%) were found to have a positive result for urine cotinine screening, with a mean (SD) level of 11.7 (9.4) ng/mL. The percentage of children with SHSe was less than anticipated. There was no association identified between urinary cotinine levels and either AHI (ρ = -0.04; 95% CI, -0.22 to 0.15) or nadir oxygen saturation (ρ = -0.07; 95% CI, -0.26 to 0.11). Furthermore, SHSe was not associated with the presence of severe OSA (odds ratio, 0.70; 95% CI, 0.26 to 1.90). Children whose caregivers reported indoor SHSe were more likely to have a detectable urinary cotinine level (odds ratio, 20.3; 95% CI, 6.67 to 61.8). Conclusions and Relevance: This cohort study did not identify any clinically meaningful association between SHSe, quantified by urinary cotinine level, and pediatric OSA severity. Future research with a larger number of children with SHSe is needed to confirm these findings and determine whether SHSe affects OSA treatment outcomes in children.

Impact of Comorbid Sleep-Disordered Breathing and Atrial Fibrillation on the Long-Term Outcome After Ischemic Stroke.

BACKGROUND: Sleep-disordered breathing (SDB) and atrial fibrillation (AF) are highly prevalent in patients with stroke and are recognized as independent risk factors for stroke. Little is known about the impact of comorbid SDB and AF on long-term outcomes after stroke. METHODS: In this prospective cohort study, 353 patients with acute ischemic stroke or transient ischemic attacks were analyzed. Patients were screened for SDB by respiratory polygraphy during acute hospitalization. Screening for AF was performed using a 7-day ECG up to 3× in the first 6 months. Follow-up visits were scheduled at 1, 3, 12, 24, and 36 months poststroke. Cox regression models adjusted for various factors (age, sex, body mass index, hypertension, diabetes, dyslipidemia, and heart failure) were used to assess the impact of comorbid SDB and AF on subsequent death or cerebro-cardiovascular events. RESULTS: Among 353 patients (299 ischemic stroke and 54 transient ischemic attacks), median age, 67 (interquartile range, 57-74) years with 63% males. Moderate-to-severe SDB (apnea-hypopnea index score, ≥15/h) was present in 118 (33.4%) patients. Among the 56 (15.9%) patients with AF, 28 had comorbid moderate-to-severe SDB and AF. Over 36 months, there were 12 deaths and 67 recurrent cerebro-cardiovascular events. Patients with comorbid moderate-to-severe SDB and AF had a higher risk of subsequent death or cerebro-cardiovascular events compared with those with only moderate-to-severe SDB without AF (hazard ratio, 2.49 [95% CI, 1.18-5.24]) and to those without moderate-to-severe SDB or AF (hazard ratio, 2.25 [95% CI, 1.12-4.50]). However, no significant difference was found between the comorbid moderate-to-severe SDB and AF group and the group with only AF without moderate-to-severe SDB (hazard ratio, 1.64 [95% CI, 0.62-4.36]). CONCLUSIONS: Comorbid moderate-to-severe SDB and AF significantly increase the risk of long-term mortality or recurrent cerebro-cardiovascular events after acute ischemic stroke. Considering both conditions as cumulative and modifiable cerebro-cardiovascular risk factors is of interest for the management of acute stroke. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02559739.

Deep learning of sleep apnea-hypopnea events for accurate classification of obstructive sleep apnea and determination of clinical severity.

BACKGROUND: /Objective: Automatic apnea/hypopnea events classification, crucial for clinical applications, often faces challenges, particularly in hypopnea detection. This study aimed to evaluate the efficiency of a combined approach using nasal respiration flow (RF), peripheral oxygen saturation (SpO2), and ECG signals during polysomnography (PSG) for improved sleep apnea/hypopnea detection and obstructive sleep apnea (OSA) severity screening. METHODS: An Xception network was trained using main features from RF, SpO2, and ECG signals obtained during PSG. In addition, we incorporated demographic data for enhanced performance. The detection of apnea/hypopnea events was based on RF and SpO2 feature sets, while the screening and severity categorization of OSA utilized predicted apnea/hypopnea events in conjunction with demographic data. RESULTS: Using RF and SpO2 feature sets, our model achieved an accuracy of 94 % in detecting apnea/hypopnea events. For OSA screening, an exceptional accuracy of 99 % and an AUC of 0.99 were achieved. OSA severity categorization yielded an accuracy of 93 % and an AUC of 0.91, with no misclassification between normal and mild OSA versus moderate and severe OSA. However, classification errors predominantly arose in cases with hypopnea-prevalent participants. CONCLUSIONS: The proposed method offers a robust automatic detection system for apnea/hypopnea events, requiring fewer sensors than traditional PSG, and demonstrates exceptional performance. Additionally, the classification algorithms for OSA screening and severity categorization exhibit significant discriminatory capacity.

The role of overnight pulse oximetry in risk stratification of paediatric sleep disordered breathing in a district general hospital.

OBJECTIVES: Adenoidectomy and tonsillectomy are common surgical interventions in paediatric patients with sleep disordered breathing. Post operative respiratory complications are a recognised risk, and pre-operative risk stratification of patients is important to enable safe delivery of peri-operative care. Due to easy accessibility, overnight pulse oximetry is commonly used for this purpose. However, its limitations have been widely reported and recent national guidance recommends limiting use to patients with significant risk factors. We reviewed the use of overnight pulse oximetry in our local unit to determine if local guidelines should be adapted in line with these national recommendations. METHODS: Retrospective analysis of all paediatric patients referred for overnight pulse oximetry over an eighteen month period between August 2020 to February 2022. Data collection included patient age, weight, and relevant co-morbidities. The McGill score was used to risk stratify patients and determine the need for an overnight bed. This was then correlated with the occurrence of post operative respiratory complications. RESULTS: 200 patients were referred for overnight pulse oximetry, with a mean age of 7.5 years. 7.5 % (15/200) had significant comorbidities. 64 % (128/200) of patients were subsequently listed for surgery. 20.3 % (26/128) were deemed at risk of post-operative complications due to McGill scores of 3 and 4 and planned for overnight ward observation. Of these, 15 % (4/26) were subsequently discharged the same day. None of our patients developed major respiratory complications in the post operative period. CONCLUSION: Our findings support national recommendations that overnight pulse oximetry is not an accurate predictor of post-operative respiratory complications and resulted in unnecessary inpatient stays. Following discussion with the trust paediatric anaesthetist lead, our local guidance was tightened, limiting overnight pulse oximetry to children with relevant identifiable co-morbidities.

Sleep apnea detection from single-lead electrocardiogram signals using effective deep-shallow fusion network.

Objective.Explore a network architecture that can efficiently perform single-lead electrocardiogram (ECG) sleep apnea (SA) detection by utilizing the beneficial information of extended ECG segments and reducing the impact of their noisy information.Approach.We propose an effective deep-shallow fusion network (EDSFnet). The deeper residual network is used to extract high-level features with stronger semantics and less noise from the original ECG segments. The shallower convolutional neural network is used to extract lower-level features with higher resolution containing more detailed neighborhood information from the extended ECG segments. These two types of features are then fused using Effective Channel Attention, implementing automatic weight assignment to take advantage of their complementary nature.Main results.The performance of EDSFnet is evaluated on the Apnea-ECG dataset and the FAH-ECG dataset. In the Apnea-ECG dataset with 35 subjects as the training set and 35 subjects as the test set, the accuracy of EDSFnet was 92.6% and 100% for per-segment and per-recording test, respectively. In the FAH-ECG dataset with 348 subjects as the training set and 88 subjects as the test set, the accuracy of EDSFnet was 89.0% and 93.2% for per-segment and per-recording test, respectively. EDSFnet has achieved state-of-the-art results in both experiments using the publicly available Apnea-ECG dataset and subject-independent experiments using the FAH-ECG clinical dataset.Significance.The success of EDSFnet in handling SA detection underlines its robustness and adaptability. By achieving superior results across different datasets, EDSFnet offers promise in advancing the cost-effective and efficient detection of SA through single-lead ECG, reducing the burden on patients and healthcare systems alike.

Sleep apnea in end-stage renal disease patients: Impact on cardiovascular and neurological outcomes.

INTRODUCTION: Sleep apnea (SA) is an important comorbidity in end-stage renal disease (ESRD) patients. The association between SA and cardiac and neurological disease is known. This study investigates the relationship between SA and cardiovascular and cerebrovascular outcomes in the ESRD population. METHODS: In a retrospective cohort study, the United States Renal Data System was queried to identify ESRD patients aged 18-100 years in whom hemodialysis had been initiated between 2005 and 2013. Diagnoses of SA and clinical comorbidities were determined from International Classification of Disease-9 codes. Demographic variables were obtained from Centers for Medicare and Medicaid Services Form-2728. Logistic regression was used to examine the association of SA with myocardial infarction (MI) or with stroke, controlling for demographic and clinical variables. RESULTS: Of 858,131 subjects meeting the inclusion criteria, 587 had central SA, and 22,724 had obstructive SA. The SA cohort was younger, more likely to be male and Caucasian compared to the non-SA cohort. Patients with SA also had more tobacco and alcohol use, hypertension, heart failure, and diabetes. Central SA (aRR = 1.69, 95% CI = 1.28-2.23) and obstructive SA (aRR = 1.15, 95% CI = 1.09-1.21) were associated with an increased risk of stroke but not MI. CONCLUSION: In the ESRD population, a diagnosis of central SA or obstructive SA increased the risk of stroke, but not MI. Early identification and treatment of SA in the ESRD population may help reduce the risk of stroke in these patients.

Sleep disordered breathing assessment in patient with slowly progressive neuromuscular disease.

BACKGROUND: Sleep-disordered breathing (SDB) is common in patients with neuromuscular diseases (NMD). Focusing on hypercapnia may lead to the neglect of other SDB such as obstructive and/or central sleep apnea syndrome (SAS). Our objectives were to assess the risk of inappropriate SDB management according to different screening strategies and to evaluate the prevalence and determinants of isolated and overlapping sleep apnea in patients with slowly progressive NMD. METHODS: This monocentric, cross-sectional, retrospective study analyzed medical records of adult NMD patients referred to a sleep department. Diagnostic strategies, including respiratory polygraphy (RP), nocturnal transcutaneous capnography (tcCO2), and blood gases (BG), were assessed for their performance in diagnosing SDB. Demographics and pulmonary function test results were compared between patients with or without SDB to identify predictors. RESULTS: Among the 149 patients who underwent a full diagnostic panel (RP + tcCO2 + BG), 109 were diagnosed with SDB. Of these, 33% had isolated SAS, and central apneas were predominant. Using single diagnostic strategies would lead to inappropriate SDB management in two thirds of patients. A combination of 2 diagnostic tools resulted respectively in 21.1, 22.9 and 42.2 % of inappropriate SDB management for RP + tcCO2, RP + BG and tcCO2 + BG. CONCLUSION: The significant prevalence of sleep apnea syndrome in patients with slowly progressive NMD highlights the need for increased awareness among clinicians. Improved diagnostics involve a systematic approach addressing both sleep apnea and diurnal and nocturnal alveolar hypoventilation to avoid inappropriate management and limit the consequences of SDB.