Assessing sleep-related breathing disorders among newly diagnosed rheumatoid and psoriatic arthritis patients: a cross-sectional study.

OBJECTIVES: This cross-sectional study aimed to determine the prevalence and risk factors for sleep-related breathing disorders (SRBD) in newly diagnosed, untreated rheumatoid arthritis (RA) and psoriatic arthritis (PsA) patients, and to develop a screening algorithm for early detection. METHODS: We evaluated newly diagnosed RA or PsA patients using the Epworth Sleepiness Scale (ESS) questionnaire, cardiorespiratory polygraphy (RPG), and clinical and laboratory assessments. Sleep apnea syndrome (SAS) was diagnosed based on pathological RPG findings excessive daytime sleepiness, defined as ESS score above 10. RESULTS: The study included 39 patients (22 RA, 17 PsA) and 23 controls. In RPG, SRBD was identified in 38.5% of arthritis patients compared to 39.1% of controls (p = 1.00), with male gender (p = .004) and age (p < .001) identified as risk factors. Excessive daytime sleepiness was noted in 36.4% of RA patients, 17.6% of PsA patients, and 21.7% of controls. Of the 24 patients diagnosed with SRBD, 41.6% met the criteria for SAS. SAS prevalence was 31.8% among RA patients, 0% in PsA patients, and 13% in controls. A significant association was observed between excessive daytime sleepiness and SRBD (p = .036). CONCLUSION: Our findings reveal a high prevalence of SRBD in newly diagnosed, untreated RA and PsA patients in ESS and RPG, with excessive daytime sleepiness being a reliable predictor of SRBD. Patients with RA exhibited a higher predisposition to SAS. We therefore suggest incorporating ESS and RPG as screening tools in RA or PsA for early detection and management of SRBD.

Artificial intelligence augmented home sleep apnea testing device study (AISAP study).

STUDY OBJECTIVE: This study aimed to prospectively validate the performance of an artificially augmented home sleep apnea testing device (WVU-device) and its patented technology. METHODOLOGY: The WVU-device, utilizing patent pending (US 20210001122A) technology and an algorithm derived from cardio-pulmonary physiological parameters, comorbidities, and anthropological information was prospectively compared with a commercially available and Center for Medicare and Medicaid Services (CMS) approved home sleep apnea testing (HSAT) device. The WVU-device and the HSAT device were applied on separate hands of the patient during a single night study. The oxygen desaturation index (ODI) obtained from the WVU-device was compared to the respiratory event index (REI) derived from the HSAT device. RESULTS: A total of 78 consecutive patients were included in the prospective study. Of the 78 patients, 38 (48%) were women and 9 (12%) had a Fitzpatrick score of 3 or higher. The ODI obtained from the WVU-device corelated well with the HSAT device, and no significant bias was observed in the Bland-Altman curve. The accuracy for ODI > = 5 and REI > = 5 was 87%, for ODI> = 15 and REI > = 15 was 89% and for ODI> = 30 and REI of > = 30 was 95%. The sensitivity and specificity for these ODI /REI cut-offs were 0.92 and 0.78, 0.91 and 0.86, and 0.94 and 0.95, respectively. CONCLUSION: The WVU-device demonstrated good accuracy in predicting REI when compared to an approved HSAT device, even in patients with darker skin tones.

A multi-task learning model using RR intervals and respiratory effort to assess sleep disordered breathing.

BACKGROUND: Sleep-disordered breathing (SDB) affects a significant portion of the population. As such, there is a need for accessible and affordable assessment methods for diagnosis but also case-finding and long-term follow-up. Research has focused on exploiting cardiac and respiratory signals to extract proxy measures for sleep combined with SDB event detection. We introduce a novel multi-task model combining cardiac activity and respiratory effort to perform sleep-wake classification and SDB event detection in order to automatically estimate the apnea-hypopnea index (AHI) as severity indicator. METHODS: The proposed multi-task model utilized both convolutional and recurrent neural networks and was formed by a shared part for common feature extraction, a task-specific part for sleep-wake classification, and a task-specific part for SDB event detection. The model was trained with RR intervals derived from electrocardiogram and respiratory effort signals. To assess performance, overnight polysomnography (PSG) recordings from 198 patients with varying degree of SDB were included, with manually annotated sleep stages and SDB events. RESULTS: We achieved a Cohen's kappa of 0.70 in the sleep-wake classification task, corresponding to a Spearman's correlation coefficient (R) of 0.830 between the estimated total sleep time (TST) and the TST obtained from PSG-based sleep scoring. Combining the sleep-wake classification and SDB detection results of the multi-task model, we obtained an R of 0.891 between the estimated and the reference AHI. For severity classification of SBD groups based on AHI, a Cohen's kappa of 0.58 was achieved. The multi-task model performed better than a single-task model proposed in a previous study for AHI estimation, in particular for patients with a lower sleep efficiency (R of 0.861 with the multi-task model and R of 0.746 with single-task model with subjects having sleep efficiency < 60%). CONCLUSION: Assisted with automatic sleep-wake classification, our multi-task model demonstrated proficiency in estimating AHI and assessing SDB severity based on AHI in a fully automatic manner using RR intervals and respiratory effort. This shows the potential for improving SDB screening with unobtrusive sensors also for subjects with low sleep efficiency without adding additional sensors for sleep-wake detection.

Association between sleep-disordered breathing and post-stroke fatigue in patients with ischemic stroke.

OBJECTIVES: Post-stroke fatigue (PSF) is common and often disabling. Sleep-disordered breathing (SDB) is highly prevalent among stroke survivors and can cause fatigue. We explored the relationship between SDB and PSF over time. MATERIALS AND METHODS: Ischemic stroke (IS) patients within the BASIC project were offered SDB screening with a well-validated cardiopulmonary sleep apnea test at 0, 3-, 6-, and 12-months post-stroke. The primary exposure was the respiratory event index (REI; sum of apneas plus hypopneas per hour). The primary outcome was PSF, measured by the SF-36 vitality scale. Associations between REI and PSF were evaluated using linear regression including time-by-REI interactions, allowing the effect of REI to vary over time. RESULTS: Of the 411 IS patients who completed at least one outcome interview, 44 % were female, 61 % Mexican American (MA), 26 % non-Hispanic white, with a mean age of 64 (SD 10). Averaged across timepoints, REI was not associated with PSF. In a time-varying model, higher REI was associated with greater PSF at 3-months (ß = 1.75, CI = 0.08, 3.43), but not at 6- or 12-months. Across timepoints, female sex, depressive symptoms, and comorbidity burden were associated with greater PSF, whereas MA ethnicity was associated with less PSF. CONCLUSIONS: Higher REI was associated with modestly greater PSF in the early post-stroke period, but no association was observed at 6 months and beyond. SDB may be a modest modifiable risk factor for early PSF, but its treatment is unlikely to have a substantial impact on long-term PSF. MA ethnicity seems to be protective against PSF.

Persistent and symptomatic periodic breathing beyond the neonatal period in full-term infants: A case series.

INTRODUCTION: Periodic breathing (PB) is considered physiological in the neonatal period and usually disappears in the first months of life. There are few data available on persistent PB after the neonatal period. The objective of this study was to characterize infants born at term with persistent PB after the age of 1 month through polysomnography (PSG) performed during symptoms. METHODS: This retrospective case series included infants born at term between 2012 and 2021, without an underlying disease, who presented with symptoms of persistent PB during a PSG. Persistent PB was defined as more than 1 % of total sleep time (TST) of PB after 1 month of life, and PB was defined as a succession of at least three episodes of central apnea lasting more than 3 s and separated by less than 20 s of normal breathing. RESULTS: A total of 10 infants born at term were included. They underwent PSG for brief resolved unexplained events, desaturation, pauses in breathing, cyanosis, and/or signs of respiratory distress. The percentage of TST spent with PB was 18.1 % before 3 months of age (n = 7), and 4.7 % between 3 and 6 months of age (n = 10). During the first PSG, ≥3 % of desaturation events were observed in 77-100 % of the PB episodes. At the first PSG, nine of the 10 infants had an obstructive apnea-hypopnea index of >10/h and five of 10 infants had a central apnea index of >5/h. Gastroesophageal reflux (GER) was suspected in eight infants. All infants showed improvement in the initial symptoms during the first year of life. CONCLUSION: This study presents cases of persistent and symptomatic PB after 1 month of life in infants born at term. The interesting finding was the presence of obstructive sleep apnea syndrome and/or central apnea syndrome in the majority of children, along with GER.

Variations in the sleep-related breathing disorder index on polysomnography between men with HIV and controls: a matched case-control study.

BACKGROUND: Both sleep-related breathing disorders (SRBDs) and HIV infection can interfere with normal sleep architecture, and also cause physical and psychological distress. We aimed to understand the differences in the obstructive patterns, sleep architecture, physical and psychological distress when compared between people living with HIV (PLWH) and matched the severity of SRBDs controls. METHODS: A comparative study using matched case-control design was conducted. Men with HIV infection (case group) were enrolled from 2016 to 2019. A control group with HIV seronegative men were matched for SRBDs severity, and were selected from sleep medicine center database for comparison. RESULTS: The mean age of the 108 men (including 54 cases and 54 matched controls) was 33.75 years. Central-apnea index (CI) was higher in the case group rather than matched controls (mean CI, 0.34 vs. 0.17, p = 0.049). PLWH had a lower mean percentage of stage 3 sleep (10.26% vs. 13.94%, p = 0.034) and a higher percentage of rapid eye movement sleep (20.59% vs. 17.85%, p = 0.011) compared to matched controls. Nocturnal enuresis and sleepiness causing traffic accidents were more frequent complaint in PLWH compared to controls. CONCLUSIONS: Early detected SRBDs and subtypes in PLWH to begin treatment for the underlying cause could reduce the risk of sleepiness-related traffic accidents.

Assessment of Sleep Disorders in Patients with CVID.

Inborn errors of immunity have been associated with reduced health-related quality of life and increased fatigue. Sleep disorders, which have been shown to contribute to fatigue and other health concerns, are prevalent in the general population, but there are limited studies evaluating these conditions in patients with common variable immunodeficiency (CVID). Our aim was to evaluate the prevalence of fatigue, sleep disturbances, and sleep-disordered breathing in adults with CVID. Patients completed 4 validated, self-administered questionnaires and a 1-night disposable home sleep apnea test. Our results demonstrated increased median Patient-Reported Outcomes Measurement Information System fatigue scores of 58.7 in patients with CVID in addition to clinically significant fatigue as measured by Fatigue Severity Scale score (median, 5.2) and overall poor sleep quality based on global Pittsburgh Sleep Quality Index score (median, 9.0). For CVID patients who completed the home sleep apnea test, 76.9% met criteria for sleep-disordered breathing with an Apnea-Hypopnea Index score of 5 or greater. The results of our study indicate that patients with CVID may have increased rates of undiagnosed sleep disorders that may contribute to increased fatigue and reduced health-related quality of life.

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'.

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.

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.

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.

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.

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.

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.

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.

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.

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.