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.

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.

Sleep-disordered breathing in children and adults with intellectual disability: mind the gap!

BACKGROUND: In adults and children with intellectual disability (ID), sleep -disordered breathing (SDB) is thought to be common. However, large epidemiological studies are lacking, and there are few studies on optimal methods of investigation and even fewer randomised, controlled intervention trials of treatment. METHOD: Peer-reviewed publications from various databases were examined in line with search terms relevant to ID and SDB spanning the years 200-2024. RESULTS: Findings suggest that, due to comorbid conditions, children and adults with ID may experience both an increased risk of SDB, as well as lower frequency of diagnosis. SDB can compromise the emotional, physical and mental health of individuals with ID. Appropriate treatment when tolerated leads to an improvement in health and well-being and several studies emphasized the importance of consistent follow-up of people with ID - something that is not universally occurring during childhood, in the transition to adulthood and during adulthood itself. As the most frequently occurring form of ID worldwide, we use Down syndrome as a specific example of how diagnosing and treating SDB can lead to improved outcomes. CONCLUSIONS: This review highlights the importance of identifying SDB in this heterogenous population, recognising the multi-faceted, deleterious consequences of untreated SDB in people with ID, and presents some strategies that can be harnessed to improve diagnosis and management. Until further ID-specific research is available, we urge flexibility in the approach to people with ID and SDB based in guidelines and standard practice developed for the typically developing population.

Definition of diaphragmatic sleep disordered breathing and clinical meaning in Duchenne muscular dystrophy.

BACKGROUND: Diaphragmatic sleep disordered breathing (dSDB) has been recently identified as sleep dysfunction secondary to diaphragmatic weakness in Duchenne muscular dystrophy (DMD). However, scoring criteria for the identification of dSDB are missing.This study aimed to define and validate dSDB scoring criteria and to evaluate whether dSDB severity correlates with respiratory progression in DMD. METHODS: Scoring criteria for diaphragmatic apnoea (dA) and hypopnoeas (dH) have been defined by the authors considering the pattern observed on cardiorespiratory polygraphy (CR) and the dSDB pathophysiology.10 sleep professionals (physiologists, consultants) blinded to each other were involved in a two-round Delphi survey to rate each item of the proposed dSDB criteria (Likert scale 1-5) and to recognise dSDB among other SDB. The scorers' accuracy was tested against the authors' panel.Finally, CR previously conducted in DMD in clinical setting were rescored and diaphragmatic Apnoea-Hypopnoea Index (dAHI) was derived. Pulmonary function (forced vital capacity per cent of predicted, FVC%pred), overnight oxygen saturation (SpO2) and transcutaneous carbon dioxide (tcCO2) were correlated with dAHI. RESULTS: After the second round of Delphi, raters deemed each item of dA and dH criteria as relevant as 4 or 5. The agreement with the panel in recognising dSDB was 81%, kappa 0.71, sensitivity 77% and specificity 85%.32 CRs from DMD patients were reviewed. dSDB was previously scored as obstructive. The dAHI negatively correlated with FVC%pred (r=-0.4; p<0.05). The total number of dA correlated with mean overnight tcCO2 (r 0.4; p<0.05). CONCLUSIONS: dSDB is a newly defined sleep disorder that correlates with DMD progression. A prospective study to evaluate dSDB as a respiratory measure for DMD in clinical and research settings is planned.

Outcomes of endovascular therapy for Stanford type B aortic dissection in patients with sleep apnea syndrome.

OBJECTIVE: This study aimed to determine the influences of varying severity of sleep apnea syndrome (SAS) on the outcomes after thoracic endovascular aorta repair (TEVAR) in patients with Stanford type B aortic dissection (TBAD). METHODS: This observational study focused on individuals with TBAD plus SAS who received TEVAR between January 2018 and December 2022. Patients were divided into groups according to the results of the portable sleep-breathing monitoring systems: mild SAS (MSAS) and moderate-to-severe SAS (MSSAS). Clinical profiles were collected and analyzed. RESULTS: A total of 121 cases with TBAD plus SAS who underwent TEVAR were enrolled in this study. Two groups were formed by stratifying these cases: MSAS (74 cases) and MSSAS (47 cases). The MSSAS cases were found to be older relative to MSAS cases (51.7 ± 8.3 years vs 57.1 ± 12.8 years; P = .012) and had a higher body mass index (BMI; 25.7 ± 2.3 kg/m2vs 27.0 ± 2.3 kg/m2; P = .038). The investigation did not find any appreciable differences between the MSAS and MSSAS groups in terms of complications (endoleak, P = .403; stent-induced new entry, P >.999; and stent displacement: P >.999). However, the MSSAS group exhibited a significantly higher overall mortality rate compared with the MSAS group (log-rank P = .027). The tendency continued when examining cases with Marfan syndrome combined with MSSAS, where the overall mortality rate was significantly greater compared with Marfan syndrome cases with MSAS (log-rank P = .037). The absence of a significant difference was noteworthy in the freedom from reintervention between the MSAS and MSSAS groups (log-rank P = .278). The overall mortality rate was significantly higher in MSSAS group even after adjusting for varying potential confounders in the multivariate cox regression analysis (hazard ratio [HR], 1.875; 95% confidence interval [CI], 1.238-2.586; P = .012). A markedly higher rate of distal stent dilation in the MSSAS group was also observed compared with the MSAS group (HR, 2.5 mm/year [95% CI, 2-3 mm/year] vs HR, 4 mm/year [95% CI, 2.0-5.5 mm/year]; P = .029). CONCLUSIONS: MSSAS is associated with a significantly higher risk of overall mortality and dilation rate of the distal stent after TEVAR for TBAD patients. Hence, aggressive efforts to reverse the severity of SAS in time in these individuals seem to be necessary.

Snoring-generated fluid droplets as a potential mechanistic link between sleep-disordered breathing and pneumonia.

The soft palate and back of the throat represent vulnerable early infection sites for SARS-CoV-2, influenza, streptococci, and many other pathogens. We demonstrate that snoring causes aerosolization of pharyngeal fluid that covers these surfaces, which previously has escaped detection because the inspired airstream carries the micron-sized droplets into the lung, inaccessible to traditional aerosol detectors. While many of these droplets will settle in the lower respiratory tract, a fraction of the respirable smallest droplets remains airborne and can be detected in exhaled breath. We distinguished these exhaled droplets from those generated by the underlying breathing activity by using a chemical tracer, thereby proving their existence. The direct transfer of pharyngeal fluids and their pathogens into the deep lung by snoring represents a plausible mechanistic link between the previously recognized association between sleep-disordered breathing and pneumonia incidence.

Treating sleep-disordered breathing of idiopathic pulmonary fibrosis patients with CPAP and nocturnal oxygen treatment. A pilot study : Sleep-disordered breathing treatment in IPF.

INTRODUCTION: Sleep-disordered breathing (SDB) is a major comorbidity in idiopathic pulmonary fibrosis (IPF) and is associated with a poor outcome. There is a lack of knowledge regarding the impact of SDB treatment on IPF. We assessed at one year: (1) the effect of CPAP and/or nocturnal oxygen therapy on IPF regarding lung function, blood mediators, and quality of life; (2) adherence to SDB treatment and SDB changes. METHODOLOGY: This is a prospective study of consecutive newly diagnosed IPF patients initiating anti-fibrotic treatment. Lung function, polysomnography, blood tests and quality of life questionnaires were performed at inclusion and after one year. Patients were classified as obstructive sleep apnoea (OSA), central sleep apnoea (CSA), and sleep-sustained hypoxemia (SSH). SDB therapy (CPAP and/or nocturnal oxygen therapy) was initiated if needed. RESULTS: Fifty patients were enrolled (36% had OSA, 22% CSA, and 12% SSH). CPAP was started in 54% of patients and nocturnal oxygen therapy in 16%. At one-year, polysomnography found improved parameters, though 17% of patients had to add nocturnal oxygen therapy or CPAP, while 33% presented SDB onset at this second polysomnography. CPAP compliance at one year was 6.74 h/night (SD 0.74). After one year, matrix metalloproteinase-1 decreased in OSA and CSA (p = 0.029; p = 0.027), C-reactive protein in OSA (p = 0.045), and surfactant protein D in CSA group (p = 0.074). There was no significant change in lung function. CONCLUSIONS: Treatment of SBD with CPAP and NOT can be well tolerated with a high compliance. IPF patients may exhibit SDB progression and require periodic re-assessment. Further studies to evaluate the impact of SDB treatment on lung function and serological mediators are needed.

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.

Multicentre sleep-stage scoring agreement in the Sleep Revolution project.

Determining sleep stages accurately is an important part of the diagnostic process for numerous sleep disorders. However, as the sleep stage scoring is done manually following visual scoring rules there can be considerable variation in the sleep staging between different scorers. Thus, this study aimed to comprehensively evaluate the inter-rater agreement in sleep staging. A total of 50 polysomnography recordings were manually scored by 10 independent scorers from seven different sleep centres. We used the 10 scorings to calculate a majority score by taking the sleep stage that was the most scored stage for each epoch. The overall agreement for sleep staging was κ = 0.71 and the mean agreement with the majority score was 0.86. The scorers were in perfect agreement in 48% of all scored epochs. The agreement was highest in rapid eye movement sleep (κ = 0.86) and lowest in N1 sleep (κ = 0.41). The agreement with the majority scoring varied between the scorers from 81% to 91%, with large variations between the scorers in sleep stage-specific agreements. Scorers from the same sleep centres had the highest pairwise agreements at κ = 0.79, κ = 0.85, and κ = 0.78, while the lowest pairwise agreement between the scorers was κ = 0.58. We also found a moderate negative correlation between sleep staging agreement and the apnea-hypopnea index, as well as the rate of sleep stage transitions. In conclusion, although the overall agreement was high, several areas of low agreement were also found, mainly between non-rapid eye movement stages.

A sleep stage estimation algorithm based on cardiorespiratory signals derived from a suprasternal pressure sensor.

Automatic estimation of sleep structure is an important aspect in moving sleep monitoring from clinical laboratories to people's homes. However, the transition to more portable systems should not happen at the expense of important physiological signals, such as respiration. Here, we propose the use of cardiorespiratory signals obtained by a suprasternal pressure (SSP) sensor to estimate sleep stages. The sensor is already used for diagnosis of sleep-disordered breathing (SDB) conditions, but besides respiratory effort it can detect cardiac vibrations transmitted through the trachea. We collected the SSP sensor signal in 100 adults (57 male) undergoing clinical polysomnography for suspected sleep disorders, including sleep apnea syndrome, insomnia, and movement disorders. Here, we separate respiratory effort and cardiac activity related signals, then input these into a neural network trained to estimate sleep stages. Using the original mixed signal the results show a moderate agreement with manual scoring, with a Cohen's kappa of 0.53 in Wake/N1-N2/N3/rapid eye movement sleep discrimination and 0.62 in Wake/Sleep. We demonstrate that decoupling the two signals and using the cardiac signal to estimate the instantaneous heart rate improves the process considerably, reaching an agreement of 0.63 and 0.71. Our proposed method achieves high accuracy, specificity, and sensitivity across different sleep staging tasks. We also compare the total sleep time calculated with our method against manual scoring, with an average error of -1.83 min but a relatively large confidence interval of ±55 min. Compact systems that employ the SSP sensor information-rich signal may enable new ways of clinical assessments, such as night-to-night variability in obstructive sleep apnea and other sleep disorders.

Follow-up of children with Down syndrome and sleep disordered breathing and the effects of treatment on actigraphically recorded sleep, quality of life, behaviour, and daytime functioning.

Children with Down syndrome are at increased risk of obstructive sleep disordered breathing, which has deleterious effects on daytime functioning. We aimed to examine the effects of treatment of sleep disordered breathing on sleep quality and daytime functioning in children with Down syndrome, and hypothesised that these would be improved. Thirty-four children completed a baseline study and a follow-up 2 years later. Measures at both time points included 7 days of actigraphy and parents completed a number of questionnaires assessing sleep, behaviour, daytime functioning, and quality of life. All children had overnight polysomnography at baseline; 15 children (44%) were treated. At baseline the treated group had more severe sleep disordered breathing compared with the untreated group: obstructive apneoa-hypopnoea index 29.3 ± 38.2 events/h versus 3.3 ± 5.2 events/h (p < 0.01). Actigraphy showed no significant differences in total sleep time, sleep efficiency, sleep schedules from baseline to follow up in either group. The sleep disturbance (p < 0.01) and total problems (p < 0.05) scales on the OSA-18 and the sleep disordered breathing subscale on the Paediatric Sleep Problem Survey Instrument (p < 0.01) improved in the treated children. There were no changes in any measure in the untreated children. Treatment of sleep disordered breathing improves symptoms, sleep disturbance and quality of life in children with Down syndrome, but has no demonstrable impact on actigraphic sleep measures or daytime behaviour or function. In contrast, children who were not treated, despite having less severe disease at baseline, had increased sleep disruption and no change in quality of life.

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.

Software defined radio frequency sensing framework for intelligent monitoring of sleep apnea syndrome.

Healthy sleep is vital to all functions in the body. It improves physical and mental health, strengthens resistance against diseases, and develops strong immunity against metabolism and chronic diseases. However, a sleep disorder can cause the inability to sleep well. Sleep apnea syndrome is a critical breathing disorder that occurs during sleeping when breathing stops suddenly and starts when awake, causing sleep disturbance. If it is not treated timely, it can produce loud snoring and drowsiness or causes more acute health problems such as high blood pressure or heart attack. The accepted standard for diagnosing sleep apnea syndrome is full-night polysomnography. However, its limitations include a high cost and inconvenience. This article aims to develop an intelligent monitoring framework for detecting breathing events based on Software Defined Radio Frequency (SDRF) sensing and verify its feasibility for diagnosing sleep apnea syndrome. We extract the wireless channel state information (WCSI) for breathing motion using channel frequency response (CFR) recorded in time at every instant at the receiver. The proposed approach simplifies the receiver structure with the added functionality of communication and sensing together. Initially, simulations are conducted to test the feasibility of the SDRF sensing design for the simulated wireless channel. Then, a real-time experimental setup is developed in a lab environment to address the challenges of the wireless channel. We conducted 100 experiments to collect the dataset of 25 subjects for four breathing patterns. SDRF sensing system accurately detected breathing events during sleep without subject contact. The developed intelligent framework uses machine learning classifiers to classify sleep apnea syndrome and other breathing patterns with an acceptable accuracy of 95.9%. The developed framework aims to build a non-invasive sensing system to diagnose patients conveniently suffering from sleep apnea syndrome. Furthermore, this framework can easily be further extended for E-health applications.

EfficientNet-based machine learning architecture for sleep apnea identification in clinical single-lead ECG signal data sets.

OBJECTIVE: Our objective was to create a machine learning architecture capable of identifying obstructive sleep apnea (OSA) patterns in single-lead electrocardiography (ECG) signals, exhibiting exceptional performance when utilized in clinical data sets. METHODS: We conducted our research using a data set consisting of 1656 patients, representing a diverse demographic, from the sleep center of China Medical University Hospital. To detect apnea ECG segments and extract apnea features, we utilized the EfficientNet and some of its layers, respectively. Furthermore, we compared various training and data preprocessing techniques to enhance the model's prediction, such as setting class and sample weights or employing overlapping and regular slicing. Finally, we tested our approach against other literature on the Apnea-ECG database. RESULTS: Our research found that the EfficientNet model achieved the best apnea segment detection using overlapping slicing and sample-weight settings, with an AUC of 0.917 and an accuracy of 0.855. For patient screening with AHI > 30, we combined the trained model with XGBoost, leading to an AUC of 0.975 and an accuracy of 0.928. Additional tests using PhysioNet data showed that our model is comparable in performance to existing models regarding its ability to screen OSA levels. CONCLUSIONS: Our suggested architecture, coupled with training and preprocessing techniques, showed admirable performance with a diverse demographic dataset, bringing us closer to practical implementation in OSA diagnosis. Trial registration The data for this study were collected retrospectively from the China Medical University Hospital in Taiwan with approval from the institutional review board CMUH109-REC3-018.

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.

Validation of a sleep-disordered breathing screening questionnaire during pregnancy and comparison between mothers and bedpartners prediction of risk.

BACKGROUND: Sleep Disorder Breathing (SDB) in pregnant patients ranges from 3 to 27% and varies depending on gestational age and method used to diagnose. SDB increases the risk of advanced pregnancy complications such as gestational diabetes mellitus, pregnancy-induced hypertension, and preeclampsia. Screening and diagnosis of SDB during pregnancy remains a challenge, with existing screening tools underperforming during pregnancy. This study aimed to validate a previously developed model for predicting SDB during late pregnancy and compare the predictive value of bedpartner responses. METHODS: Ninety-six women in the third trimester of pregnancy underwent polysomnography and completed the Berlin Questionnaire (BQ), with 81 bedpartners completing the BQ about their pregnant partner. A subset of BQ items (snoring volume and tiredness upon awakening) along with BMI > 32 kg/m2 was utilised to calculate the Wilson Optimized Model (WOM), which demonstrated strong predictive properties in development. RESULTS: SDB (RDI/hr ≥ 5) was detected in 43.8% of women. BQ identified 72% of pregnant mothers as high risk for SDB (Sensitivity = 83%, Specificity = 37%), compared to 29% of mothers identified by the WOM (Sensitivity = 45%, Specificity = 83%). At RDI of ≥ 15, the WOM correctly classified more women according to SDB risk than the BQ (76.0% vs. 41.7% cases correct, X2(1) = 23.42, p < .001), with no difference at RDI ≥ 5. Bedpartners were more likely to report high risk for SDB on the WOM than pregnant women themselves (38.3% vs. 28.4%), however predictive ability was not improved by bedpartner input (RDI ≥ 5 bedpartner AUC = 0.69 v mother AUC = 0.73). CONCLUSION: BQ largely overestimates the prevalence of SDB in pregnancy compared to the WOM which underestimates. Utilising bedpartner responses didn't improve screening for SDB in late pregnancy. More work is needed to develop a pregnancy-specific tool for quick and accurate screening for SDB.

Measurement of peripheral arterial tone to detect sleep-disordered breathing in patients with heart failure.

PURPOSE: Sleep-disordered breathing is highly prevalent in patients with heart failure and is related to increased mortality and morbidity. The gold standard for sleep diagnostic is polysomnography in a sleep laboratory. Measurement of peripheral arterial tone with a wrist-worn diagnostic device is a promising method to detect sleep-disordered breathing without major technical effort. METHODS: We prospectively enrolled patients with heart failure with reduced ejection fraction for measurement of the peripheral arterial tone and polysomnography simultaneously during one night in the sleep laboratory. Raw data of polysomnography was analyzed blindly by sleep core lab personnel and compared with automatic algorithm-based sleep results of measurement of the peripheral arterial tone. RESULTS: A total of 25 patients provided comparable sleep results. All patients had sleep-disordered breathing and were identified by measurement of the peripheral arterial tone. The comparison of apnea-hypopnea index between peripheral arterial tone 38.8 ± 17.4/h and polysomnography 44.5 ± 17.9/h revealed a bias of - 5.7 ± 9.8/h with limits of agreement of ± 19.2/h in Bland-Altman analysis but showed high and significant Pearson correlation (r = 0.848, p < 0.001). CONCLUSION: The findings suggest that measurement of the peripheral arterial tone may be useful to identify sleep-disordered breathing in patients with heart failure with reduced ejection fraction.

Validation of the Sleepiz One + as a radar-based sensor for contactless diagnosis of sleep apnea.

PURPOSE: The cardiorespiratory polysomnography (PSG) is an expensive and limited resource. The Sleepiz One + is a novel radar-based contactless monitoring device that can be used e.g. for longitudinal detection of nocturnal respiratory events. The present study aimed to compare the performance of the Sleepiz One + device to the PSG regarding the accuracy of apnea-hypopnea index (AHI). METHODS: From January to December 2021, a total of 141 adult volunteers who were either suspected of having sleep apnea or who were healthy sleepers took part in a sleep study. This examination served to validate the Sleepiz One + device in the presence and absence of additional SpO2 information. The AHI determined by the Sleepiz One + monitor was estimated automatically and compared with the AHI derived from manual PSG scoring. RESULTS: The correlation between the Sleepiz-AHI and the PSG-AHI with and without additional SpO2 measurement was rp = 0.94 and rp = 0,87, respectively. In general, the Bland-Altman plots showed good agreement between the two methods of AHI measurement, though their deviations became larger with increasing sleep-disordered breathing. Sensitivity and specificity for recordings without additional SpO2 was 85% and 88%, respectively. Adding a SpO2 sensor increased the sensitivity to 88% and the specificity to 98%. CONCLUSION: The Sleepiz One + device is a valid diagnostic tool for patients with moderate to severe OSA. It can also be easily used in the home environment and is therefore beneficial for e.g. immobile and infectious patients. TRIAL REGISTRATION NUMBER AND DATE OF REGISTRATION FOR PROSPECTIVELY REGISTERED TRIALS: This study was registered on clinicaltrials.gov (NCT04670848) on 2020-12-09.

Sleep-disordered breathing diagnosis: a comprehensive audit of home sleep testing in real clinical settings.

PURPOSE: This study aimed to investigate the indications and outcomes of Home Sleep Testing (HST) for patients with suspected obstructive sleep apnea (OSA), aligning with guidelines set forth by the American Academy of Sleep Medicine and the European Sleep Research Society. Specifically, we aimed to audit whether validated type 3 polygraphy could effectively ensure patient care while optimizing resource utilization. METHODS: A retrospective analysis was conducted on data from patients undergoing type 3 polygraphy for suspected OSA in a tertiary referral hospital between January 2022 and December 2022. Demographic, clinical, and management data were collected. The efficacy of HST in guiding management plans was evaluated, with outcomes categorized as effective or ineffective based on subsequent need for in-laboratory polysomnography. RESULTS: While 85% of patients received a reliable diagnosis, 44.4% of them still required subsequent polysomnography, primarily due to adherence to funding regulations, rather than clinical need for further testing. Factors impacting the efficacy of HST included patient age, severity of apnea, and referral by a certified sleep specialist physician. CONCLUSION: Our study highlighted the potential of type 3 polygraphy, as a valuable tool for diagnosing OSA in an outpatient setting. However, having the result interpreted by a certified sleep specialist doctor was not enough. To streamline the care pathway, the referral for polygraphy had also to be made by a trained specialist. Challenges related to funding regulations, patient demographics and physician training stress the need for optimized diagnostic pathways to improve patient care and resource utilization.

Piezoelectric rubber sheet sensor: a promising tool for home sleep apnea testing.

PURPOSE: This study aimed to develop an unobtrusive method for home sleep apnea testing (HSAT) utilizing micromotion signals obtained by a piezoelectric rubber sheet sensor. METHODS: Algorithms were designated to extract respiratory and ballistocardiogram components from micromotion signals and to detect respiratory events as the characteristic separation of the fast envelope of the respiration component from the slow envelope. In 78 adults with diagnosed or suspected sleep apnea, micromotion signal was recorded with a piezoelectric rubber sheet sensor placed beneath the bedsheet during polysomnography. In a half of the subjects, the algorithms were optimized to calculate respiratory event index (REI), estimating apnea-hypopnea index (AHI). In the other half of subjects, the performance of REI in classifying sleep apnea severity was evaluated. Additionally, the predictive value of the frequency of cyclic variation in heart rate (Fcv) obtained from the ballistocardiogram was assessed. RESULTS: In the training group, the optimized REI showed a strong correlation with the AHI (r = 0.93). Using the optimal cutoff of REI ≥ 14/h, subjects with an AHI ≥ 15 were identified with 77.8% sensitivity and 90.5% specificity. When applying this REI to the test group, it correlated closely with the AHI (r = 0.92) and identified subjects with an AHI ≥ 15 with 87.5% sensitivity and 91.3% specificity. While Fcv showed a modest correlation with AHI (r = 0.46 and 0.66 in the training and test groups), it lacked independent predictive power for AHI. CONCLUSION: The analysis of respiratory component of micromotion using piezoelectric rubber sheet sensors presents a promising approach for HSAT, providing a practical and effective means of estimating sleep apnea severity.