Impact of Portable Sleep Testing

Data-driven sleep structure deciphering based on cardiorespiratory signals

BACKGROUND AND OBJECTIVE

Cardiorespiratory signals provide a novel perspective for understanding sleep structure through the physiological mechanism of cardiopulmonary coupling. This mechanism divides the coupling spectrum into high-frequency (HF) and low-frequency (LF) bands, indicating that signal segments of 4-8 min are optimal for analysis. However, the lack of labels tailored to these signals has led to reliance on the American Academy of Sleep Medicine (AASM) definitions, which are primarily designed for electroencephalogram (EEG) and electrooculogram (EOG) data. This study aims to address the challenge of transitioning from AASM-defined labels to cardiorespiratory-oriented ones and to evaluate the feasibility of using these signals for accurate sleep structure recognition.

METHODS

To align with the physiological characteristics of cardiorespiratory signals, AASM labels were modified by excluding the N2 stage due to its overlap of stable and unstable non-rapid eye movement (NREM) phases, which introduces ambiguity. The modified dataset focused on the wake, N1, deep sleep (N3), and rapid eye movement (REM) stages. A physiologically-inspired deep-learning model (PIDM) was developed to extract features from cardiorespiratory time series and classify sleep stages. Post-analysis assessed the physiological validity of the model's N2 predictions by evaluating the HF-to-LF ratio and respiratory variability.

RESULTS

The pipeline, combining the modified labeling scheme with the PIDM model, achieved balanced accuracy scores of 0.83, 0.86, and 0.78 for wake, deep sleep, and REM stages, respectively in the normal group; and 0.92, 0.95, and 0.90 in the mild and moderate sleep apnea groups. Post-analysis revealed that most N2 samples were attributed to stable NREM sleep, characterized by higher HF-to-LF ratios and lower respiratory variability, aligning with physiological understanding.

CONCLUSIONS

This study highlights the physiological relevance of cardiorespiratory signals for sleep structure recognition. By addressing the uncertainty in N2 classification through exclusion and redefinition, the proposed pipeline effectively distinguished wake, deep sleep, and REM stages. These findings demonstrate the potential of cardiorespiratory signals as a robust, practical, and EEG-independent tool for sleep analysis, particularly in home healthcare settings.

Performance of consumer wrist-worn sleep tracking devices compared to polysomnography: a meta-analysis

STUDY OBJECTIVES

The use of sleep tracking devices is increasing as people become more aware of the importance of sleep and interested in monitoring their patterns. With many devices on the market, we conducted a meta-analysis comparing sleep scoring data from consumer wrist-worn sleep tracking devices with polysomnography to validate the accuracy of these devices.

METHODS

We retrieved studies from the databases of SCOPUS, EMBASE, Cochrane Library, PubMed, Web of Science, and KoreaMed and OVID Medline up to March 2024. We compared personal data about participants and information on objective sleep parameters.

RESULTS

From 24 studies, data of 798 patient using Fitbit, Jawbone, myCadian watch, WHOOP strap, Garmin, Basis B1, Zulu Watch, Huami Arc, E4 wristband, Fatigue Science Readiband, Apple Watch, or Xiaomi Mi Band 5 were analyzed. There were significant differences in total sleep time (mean difference, -16.854; 95% confidence interval, [-26.332; -7.375]), sleep efficiency (mean difference, -4.691; 95% confidence interval, [-7.079; -2.302]), sleep latency (mean difference, 2.574; 95% confidence interval, [0.606; 4.542]), and wake after sleep onset (mean difference, 13.255; 95% confidence interval, [4.522; 21.988]) between all consumer sleep tracking devices and polysomnography. In subgroup analysis, there was no significant difference in wake after sleep onset between Fitbit and polysomnography. There was also no significant difference in sleep latency between other devices and polysomnography. Fitbit measured sleep latency longer than other devices, and other devices measured wake after sleep onset longer. Based on Begg and Egger's test, there was no publication bias in total sleep time and sleep efficiency.

CONCLUSIONS

Wrist-worn sleep tracking devices, although popular, are not as reliable as polysomnography in measuring key sleep parameters such as total sleep time, sleep efficiency, and sleep latency. Physicians and consumers should be aware of their limitations and interpret results carefully, though they can still be useful for tracking general sleep patterns. Further improvements and clinical studies are needed to enhance their accuracy.

CITATION

Lee YJ, Lee JY, Cho JH, Kang YJ, Choi JH. Performance of consumer wrist-worn sleep tracking devices compared to polysomnography: a meta-analysis. J Clin Sleep Med. 2025;21(3):573-582.

Assessment of obstructive sleep apnoea in children: What are the challenges we face?

There is an increasing demand for the assessment of sleep-disordered breathing in children of all ages to prevent the deleterious neurocognitive and behaviour consequences of the under-diagnosis and under-treatment of obstructive sleep apnoea [OSA]. OSA can be considered in three broad categories based on predominating contributory features: OSA type 1 [enlarged tonsils and adenoids], type II [Obesity] and type III [craniofacial abnormalities, syndromal, storage diseases and neuromuscular conditions]. The reality is that sleep questionnaires or calculations of body mass index in isolation are poorly predictive of OSA in individuals. Globally, the access to testing in tertiary referral centres is comprehensively overwhelmed by the demand and financial cost. This has prompted the need for better awareness and focussed history taking, matched with simpler tools with acceptable accuracy used in the setting of likely OSA. Consequently, we present key indications for polysomnography and present scalable, existing alternatives for assessment of OSA in the hospital or home setting, using polygraphy, oximetry or contactless sleep monitoring.

Automatic prediction of obstructive sleep apnea in patients with temporomandibular disorder based on multidata and machine learning

Obstructive sleep apnea (OSA) is closely associated with the development and chronicity of temporomandibular disorder (TMD). Given the intricate pathophysiology of both OSA and TMD, comprehensive diagnostic approaches are crucial. This study aimed to develop an automatic prediction model utilizing multimodal data to diagnose OSA among TMD patients. We collected a range of multimodal data, including clinical characteristics, portable polysomnography, X-ray, and MRI data, from 55 TMD patients who reported sleep problems. This data was then analyzed using advanced machine learning techniques. Three-dimensional VGG16 and logistic regression models were used to identify significant predictors. Approximately 53% (29 out of 55) of TMD patients had OSA. Performance accuracy was evaluated using logistic regression, multilayer perceptron, and area under the curve (AUC) scores. OSA prediction accuracy in TMD patients was 80.00-91.43%. When MRI data were added to the algorithm, the AUC score increased to 1.00, indicating excellent capability. Only the obstructive apnea index was statistically significant in predicting OSA in TMD patients, with a threshold of 4.25 events/h. The learned features of the convolutional neural network were visualized as a heatmap using a gradient-weighted class activation mapping algorithm, revealing that it focuses on differential anatomical parameters depending on the absence or presence of OSA. In OSA-positive cases, the nasopharynx, oropharynx, uvula, larynx, epiglottis, and brain region were recognized, whereas in OSA-negative cases, the tongue, nose, nasal turbinate, and hyoid bone were recognized. Prediction accuracy and heat map analyses support the plausibility and usefulness of this artificial intelligence-based OSA diagnosis and prediction model in TMD patients, providing a deeper understanding of regions distinguishing between OSA and non-OSA.

Validity study of a multiscaled fusion network using single-lead electrocardiogram signals for obstructive sleep apnea diagnosis

STUDY OBJECTIVES

We evaluated the validity of a squeeze-and-excitation and multiscaled fusion network (SE-MSCNN) using single-lead electrocardiogram (ECG) signals for obstructive sleep apnea detection and classification.

METHODS

Overnight polysomnographic data from 436 participants at the Sleep Center of the First Affiliated Hospital of Sun Yat-sen University were used to generate a new FAH-ECG dataset comprising 260, 88, and 88 single-lead ECG signal recordings for training, validation, and testing, respectively. The SE-MSCNN was employed for detection of apnea-hypopnea events from the acquired ECG segments. Sensitivity, specificity, accuracy, and F1 scores were assigned to assess algorithm performance. We also used the SE-MSCNN to estimate the apnea-hypopnea index, classify obstructive sleep apnea severity, and compare the agreement between 2 sleep technicians.

RESULTS

The SE-MSCNN's accuracy, sensitivity, specificity, and F1 score on the FAH-ECG dataset were 86.6%, 83.3%, 89.1%, and 0.843, respectively. Although slightly inferior to previously reported results using public datasets, it is superior to state-of-the-art open-source models. Furthermore, the SE-MSCNN had good agreement with manual scoring, such that the Spearman's correlations for the apnea-hypopnea index between the SE-MSCNN and 2 technicians were 0.93 and 0.94, respectively. Cohen's kappa scores in classifying the SE-MSCNN and the 2 sleep technicians were 0.72 and 0.78, respectively.

CONCLUSIONS

In this study, we validated the use of the SE-MSCNN in a clinical environment, and despite some limitations the network appeared to meet the performance standards for generalizability. Therefore, updating algorithms based on single-lead ECG signals can facilitate the development of novel wearable devices for efficient obstructive sleep apnea screening.

CITATION

Yue H, Li P, Li Y, et al. Validity study of a multiscaled fusion network using single-lead electrocardiogram signals for obstructive sleep apnea diagnosis. J Clin Sleep Med. 2023;19(6):1017-1025.

The urocortin peptides: biological relevance and laboratory aspects of UCN3 and its receptor

The urocortins are polypeptides belonging to the corticotropin-releasing hormone family, known to modulate stress responses in mammals. Stress, whether induced physically or psychologically, is an underlying cause or consequence of numerous clinical syndromes. Identifying biological markers associated with the homeostatic regulation of stress could provide a clinical laboratory approach for the management of stress-related disorders. The neuropeptide, urocortin 3 (UCN3), and the corticotropin-releasing hormone receptor 2 (CRHR2) constitute a regulatory axis known to mediate stress homeostasis. Dysregulation of this peptide/receptor axis is believed to play a role in several clinical conditions including post-traumatic stress, sleep apnea, cardiovascular disease, and other health problems related to stress. Understanding the physiology and measurement of the UCN3/CRHR2 axis is important for establishing a viable clinical laboratory diagnostic. In this article, we focus on evidence supporting the role of UCN3 and its receptor in stress-related clinical syndromes. We also provide insight into the measurements of UCN3 in blood and urine. These potential biomarkers provide new opportunities for clinical research and applications of laboratory medicine diagnostics in stress management.

Evaluating Prediction Models of Sleep Apnea From Smartphone-Recorded Sleep Breathing Sounds

Importance

Breathing sounds during sleep are an important characteristic feature of obstructive sleep apnea (OSA) and have been regarded as a potential biomarker. Breathing sounds during sleep can be easily recorded using a microphone, which is found in most smartphone devices. Therefore, it may be easy to implement an evaluation tool for prescreening purposes.

Objective

To evaluate OSA prediction models using smartphone-recorded sounds and identify optimal settings with regard to noise processing and sound feature selection.

Design, Setting, and Participants

A cross-sectional study was performed among patients who visited the sleep center of Seoul National University Bundang Hospital for snoring or sleep apnea from August 2015 to August 2019. Audio recordings during sleep were performed using a smartphone during routine, full-night, in-laboratory polysomnography. Using a random forest algorithm, binary classifications were separately conducted for 3 different threshold criteria according to an apnea hypopnea index (AHI) threshold of 5, 15, or 30 events/h. Four regression models were created according to noise reduction and feature selection from the input sound to predict actual AHI: (1) noise reduction without feature selection, (2) noise reduction with feature selection, (3) neither noise reduction nor feature selection, and (4) feature selection without noise reduction. Clinical and polysomnographic parameters that may have been associated with errors were assessed. Data were analyzed from September 2019 to September 2020.

Main Outcomes and Measures

Accuracy of OSA prediction models.

Results

A total of 423 patients (mean [SD] age, 48.1 [12.8] years; 356 [84.1%] male) were analyzed. Data were split into training (n = 256 [60.5%]) and test data sets (n = 167 [39.5%]). Accuracies were 88.2%, 82.3%, and 81.7%, and the areas under curve were 0.90, 0.89, and 0.90 for an AHI threshold of 5, 15, and 30 events/h, respectively. In the regression analysis, using recorded sounds that had not been denoised and had only selected attributes resulted in the highest correlation coefficient (r = 0.78; 95% CI, 0.69-0.88). The AHI (β = 0.33; 95% CI, 0.24-0.42) and sleep efficiency (β = -0.20; 95% CI, -0.35 to -0.05) were found to be associated with estimation error.

Conclusions and Relevance

In this cross-sectional study, recorded sleep breathing sounds using a smartphone were used to create reasonably accurate OSA prediction models. Future research should focus on real-life recordings using various smartphone devices.

Differentiation Model for Insomnia Disorder and the Respiratory Arousal Threshold Phenotype in Obstructive Sleep Apnea in the Taiwanese Population Based on Oximetry and Anthropometric Features

Insomnia disorder (ID) and obstructive sleep apnea (OSA) with respiratory arousal threshold (ArTH) phenotypes often coexist in patients, presenting similar symptoms. However, the typical diagnosis examinations (in-laboratory polysomnography (lab-PSG) and other alternatives methods may therefore have limited differentiation capacities. Hence, this study established novel models to assist in the classification of ID and low- and high-ArTH OSA. Participants reporting insomnia as their chief complaint were enrolled. Their sleep parameters and body profile were accessed from the lab-PSG database. Based on the definition of low-ArTH OSA and ID, patients were divided into three groups, namely, the ID, low- and high-ArTH OSA groups. Various machine learning approaches, including logistic regression, k-nearest neighbors, naive Bayes, random forest (RF), and support vector machine, were trained using two types of features (Oximetry model, trained with oximetry parameters only; Combined model, trained with oximetry and anthropometric parameters). In the training stage, RF presented the highest cross-validation accuracy in both models compared with the other approaches. In the testing stage, the RF accuracy was 77.53% and 80.06% for the oximetry and combined models, respectively. The established models can be used to differentiate ID, low- and high-ArTH OSA in the population of Taiwan and those with similar craniofacial features.

Machine learning approaches for screening the risk of obstructive sleep apnea in the Taiwan population based on body profile

(a) Objective: Obstructive sleep apnea syndrome (OSAS) is typically diagnosed through polysomnography (PSG). However, PSG incurs high medical costs. This study developed new models for screening the risk of moderate-to-severe OSAS (apnea-hypopnea index, AHI ≥15) and severe OSAS (AHI ≥30) in various age groups and sexes by using anthropometric features in the Taiwan population.(b) Participants: Data were derived from 10,391 northern Taiwan patients who underwent PSG.(c) Methods: Patients' characteristics - namely age, sex, body mass index (BMI), neck circumference, and waist circumference - was obtained. To develop an age- and sex-independent model, various approaches - namely logistic regression, k-nearest neighbor, naive Bayes, random forest (RF), and support vector machine - were trained for four groups based on sex and age (men or women; aged <50 or ≥50 years). Dataset was separated independently (training:70%; validation: 10%; testing: 20%) and Cross-validated grid search was applied for model optimization. Models demonstrating the highest overall accuracy in validation outcomes for the four groups were used to predict the testing dataset.(d) Results: The RF models showed the highest overall accuracy. BMI was the most influential parameter in both types of OSAS severity screening models.(e) Conclusion: The established models can be applied to screen OSAS risk in the Taiwan population and those with similar craniofacial features.

Using a portable monitoring device for diagnosing obstructive sleep apnea in patients with multiple coexisting medical illnesses

INTRODUCTION

The existing guidelines recommend type III devices should be used in patients without significant comorbidities.

OBJECTIVES

This study explored the reliability of using a type III device in patients with significant medical conditions to diagnose sleep apnea.

METHODS

Patients had an overnight sleep study conducted simultaneously with both polysomnography (PSG) and a type III (NOX-T3) monitoring device. All patients had stable multiple coexisting medical illnesses without any changes in medications and conditions within 1 month of sleep study.

RESULTS

Between July 2019 and March 2020, there were altogether 74 patients recruited with analyzable data. Five major disease groups were identified in the cohort: psychiatric illnesses, stroke, ischemic heart diseases (IHDs), chronic kidney diseases (CKDs), and others. Psychiatric patients with medications were found to have the lowest apnea hypopnea index (AHI) (23.7 per hour) and arousal index (46.6 per hour). The CKD group had the highest mean arousal index (71.4 per hour) and obstructive apnea count (110.2). NOX-T3 respiratory event index (REI) was significantly lower than the PSG AHI (mean REI 31.4 vs. mean AHI: 42.2). The number of patients with no/mild/moderate/severe obstructive sleep apnea (OSA) diagnosed by NOX-T3 and PSG was 7/17/19/31 and 5/11/20/38, respectively.

CONCLUSION

NOX-T3 device can reliably diagnose OSA in patients with different stable coexisting medical conditions. There is a tendency for underestimation of the severity of the OSA with NOX-T3 in patients with coexisting medical conditions especially with sedative medications. A positive NOX-T3 reliably diagnoses OSA whereas a negative NOX-T3 result needs to be interpreted with caution.

Estimating sleep stages using cardiorespiratory signals: validation of a novel algorithm across a wide range of sleep-disordered breathing severity

STUDY OBJECTIVES

We have developed the CardioRespiratory Sleep Staging (CReSS) algorithm for estimating sleep stages using heart rate variability and respiration, allowing for estimation of sleep staging during home sleep apnea tests. Our objective was to undertake an epoch-by-epoch validation of algorithm performance against the gold standard of manual polysomnography sleep staging.

METHODS

Using 296 polysomnographs, we created a limited montage of airflow and heart rate and deployed CReSS to identify each 30-second epoch as wake, light sleep (N1 + N2), deep sleep (N3), or rapid eye movement (REM) sleep. We calculated Cohen's kappa and the percentage of accurately identified epochs. We repeated our analyses after stratification by sleep-disordered breathing (SDB) severity, and after adding thoracic respiratory effort as a backup signal for periods of invalid airflow.

RESULTS

CReSS discriminated wake/light sleep/deep sleep/REM sleep with 78% accuracy; the kappa value was 0.643 (95% confidence interval, 0.641-0.645). Discrimination of wake/sleep demonstrated a kappa value of 0.711 and accuracy of 89%, non-REM sleep/REM sleep demonstrated a kappa of 0.790 and accuracy of 94%, and light sleep/deep sleep demonstrated a kappa of 0.469 and accuracy of 87%. Kappa values did not vary by more than 0.07 across subgroups of no SDB, mild SDB, moderate SDB, and severe SDB. Accuracy increased to 80%, with a kappa value of 0.680 (95% confidence interval, 0.678-0.682), when CReSS additionally utilized the thoracic respiratory effort signal.

CONCLUSIONS

We observed substantial agreement between CReSS and the gold-standard comparator of manual sleep staging of polysomnographic signals, which was consistent across the full range of SDB severity. Future research should focus on the extent to which CReSS reduces the discrepancy between the apnea-hypopnea index and the respiratory event index, and the ability of CReSS to identify REM sleep-related obstructive sleep apnea.

Performance of peripheral arterial tonometry-based testing for the diagnosis of obstructive sleep apnea in a large sleep clinic cohort

STUDY OBJECTIVES

Peripheral arterial tonometry (PAT)-based technology represents a validated portable monitoring modality for the diagnosis of OSA. We assessed the diagnostic accuracy of PAT-based technology in a large point-of-care cohort of patients studied with concurrent polysomnography (PSG).

METHODS

During study enrollment, all participants suspected to have OSA and tested by in-laboratory PSG underwent concurrent PAT device recordings.

RESULTS

Five hundred concomitant PSG and WatchPat tests were analyzed. Median (interquartile range) PSG AHI was 18 (8-37) events/h and PAT AHI_3% was 25 (12-46) events/h. Average bias was + 4 events/h. Diagnostic concordance was found in 42%, 41%, and 83% of mild, moderate, and severe OSA, respectively (accuracy = 53%). Among patients with PAT diagnoses of moderate or severe OSA, 5% did not have OSA and 19% had mild OSA; in those with mild OSA, PSG showed moderate or severe disease in 20% and no OSA in 30% of patients (accuracy = 69%). On average, using a 3% desaturation threshold, WatchPat overestimated disease prevalence and severity (mean + 4 events/h) and the 4% threshold underestimated disease prevalence and severity by -6 events/h.

CONCLUSIONS

Although there was an overall tendency to overestimate the severity of OSA, a significant percentage of patients had clinically relevant misclassifications. As such, we recommend that patients without OSA or with mild disease assessed by PAT undergo repeat in-laboratory PSG. Optimized clinical pathways are urgently needed to minimize therapeutic decisions instituted in the presence of diagnostic uncertainty.

Poor performance of screening questionnaires for obstructive sleep apnea in male commercial drivers

PURPOSE

Screening commercial drivers (CDs) for obstructive sleep apnea (OSA) reduces the risk of motor vehicle accidents. We evaluated the accuracy of standard OSA questionnaires in a cohort of CDs.

STUDY DESIGN AND METHODS

We enrolled consecutive male CDs at 10 discrete transportation companies during their yearly scheduled occupational health visit. The CDs had their anthropometric measures taken; completed the Berlin, STOP, STOP-BANG, OSAS-TTI, SACS, EUROSAS, and ARES questionnaires; and underwent a home sleep apnea test (HSAT) for the determination of their respiratory events index (REI). We assessed the questionnaires' ability to predict OSA (REI ≥ 5 events/h) and moderate-to-severe OSA (REI ≥ 15 events/h).

RESULTS

Among 315 CDs recruited, 243 (77%) completed the study protocol, while 72 subjects were excluded for inadequate HSAT quality. The demographics and clinical data were comparable in both the included and excluded subjects. The included CDs had a median age of 50 years (interquartile range (IQR) 25-70) and a mean body mass index of 27 ± 4 kg/m². One hundred and seventy-one subjects (71%) had OSA, and 68 (28%) had moderate-to-severe OSA. A receiver operating characteristic curve of the questionnaires were 0.51-0.71 for predicting OSA and 0.51-0.66 for moderate-to-severe OSA. The STOP-BANG questionnaire had an unsatisfactory positive predictive value, while all of the other questionnaires had an inadequate negative predictive value.

CONCLUSIONS

Standard OSA questionnaires are not suited for screening among CDs. The use of the HSAT could provide an objective evaluation of for OSA in this special population.

Automated Apnea-Hypopnea Index from Oximetry and Spectral Analysis of Cardiopulmonary Coupling

Rationale: The increased prevalence of obstructive sleep apnea (OSA) coincides with a severe shortage of sleep physicians. There is a need for widescale home-sleep-testing devices with accurate automated scoring to accelerate access to treatment.Objectives: To examine the accuracy of an automated apnea-index (AHI) derived from spectral analysis of cardiopulmonary coupling (CPC) extracted from electrocardiograms, combined with oximetry signals, in relation to polysomnograms (PSGs).Methods: Electrocardiograms and pulse-oximeter tracings on PSGs from APPLES (Apnea Positive Pressure Long-term Efficacy Study) were analyzed. Distinct CPC spectral bands were combined with the oxygen desaturation index to create a derived AHI (DAHI). Correlation statistics between the DAHI and the conventionally scored AHI, in which hypopneas required ≥50% airflow reduction alone or a lesser airflow reduction associated with ≥3% desaturation or arousal, using PSGs from APPLES were calculated.Results: A total of 833 adult subjects were included. The DAHI has excellent and strong correlation with the conventionally scored AHI on PSGs, with Pearson coefficients of 0.972 and receiver operating characteristic curves demonstrating strong agreement in all OSA categories: 98.5% in mild OSA (95% confidence interval [CI], 97.6-99.3%), 96.4% in moderate OSA (95% CI, 95.3-97.5%), and 98.5% in severe OSA (95% CI, 97.8-99.2%).Conclusions: An accurate automated AHI can be derived from oximetry and CPC.

Evaluation and Management of Adults with Obstructive Sleep Apnea Syndrome

Obstructive sleep apnea syndrome (OSAS) is a common and underdiagnosed medical condition characterized by recurrent sleep-dependent pauses and reductions in airflow. While a narrow, collapsible oropharynx plays a central role in the pathophysiology of OSAS, there are other equally important nonanatomic factors including sleep-stage dependent muscle tone, arousal threshold, and loop gain that drive obstructive apneas and hypopneas. Through mechanisms of intermittent hypoxemia, arousal-related sleep fragmentation, and intrathoracic pressure changes, OSAS impacts multiple organ systems. Risk factors for OSAS include obesity, male sex, age, specific craniofacial features, and ethnicity. The prevalence of OSAS is rising due to increasing obesity rates and improved sensitivity in the tools used for diagnosis. Validated questionnaires have an important but limited role in the identification of patients that would benefit from formal testing for OSA. While an in-laboratory polysomnography remains the gold standard for diagnosis, the widespread availability and accuracy of home sleep apnea testing modalities increase access and ease of OSAS diagnosis for many patients. In adults, the most common treatment involves the application of positive airway pressure (PAP), but compliance continues to be a challenge. Alternative treatments including mandibular advancement device, hypoglossal nerve stimulator, positional therapies, and surgical options coupled with weight loss and exercise offer possibilities of an individualized personal approach to OSAS. Treatment of symptomatic patients with OSAS has been found to be beneficial with regard to sleep-related quality of life, sleepiness, and motor vehicle accidents. The benefit of treating asymptomatic OSA patients, particularly with regard to cardiovascular outcomes, is controversial and more data are needed.

The Epidemiology of Obstructive Sleep Apnea in Poland-Polysomnography and Positive Airway Pressure Therapy

The aim of this study is to provide a brief summary of the epidemiological data on obstructive sleep apnea syndrome (OSAS) diagnosis and therapy in different regions of Poland from 2010 to 2019. We performed a retrospective study in the sleep center of the Department of Sleep Medicine and Metabolic Disorders, Medical University of Lodz, Poland. We requested data from the National Health Service concerning the number of new diagnoses of OSAS, the polysomnographies (PSGs) that were performed, and reimbursements of positive airway pressure (PAP) therapy in each region of Poland in the period 2010-2019. The constant increase in the number of polysomnographies performed and PAP reimbursements suggests the need to create a national network between regional sleep centers to provide proper care for patients with OSAS, and PAP therapy.

Alternative Care Pathways for Obstructive Sleep Apnea and the Impact on Positive Airway Pressure Adherence: Unraveling the Puzzle of Adherence

The high burden of obstructive sleep apnea (OSA), combined with inadequate supply of sleep specialists and constraints on polysomnography resources, has prompted interest in alternative models of care to improve access and treatment effectiveness. In appropriately selected patients, ambulatory clinical pathways and use of nonphysicians or primary care providers to manage OSA can improve timely access and costs without compromising adherence or other clinical outcomes. Although initial studies show promising results, there are several potential barriers that must be considered before broad implementation, and further implementation research and economic evaluation studies are required.

Quantitative detection of sleep apnea with wearable watch device

The spread of wearable watch devices with photoplethysmography (PPG) sensors has made it possible to use continuous pulse wave data during daily life. We examined if PPG pulse wave data can be used to detect sleep apnea, a common but underdiagnosed health problem associated with impaired quality of life and increased cardiovascular risk. In 41 patients undergoing diagnostic polysomnography (PSG) for sleep apnea, PPG was recorded simultaneously with a wearable watch device. The pulse interval data were analyzed by an automated algorithm called auto-correlated wave detection with adaptive threshold (ACAT) which was developed for electrocardiogram (ECG) to detect the cyclic variation of heart rate (CVHR), a characteristic heart rate pattern accompanying sleep apnea episodes. The median (IQR) apnea-hypopnea index (AHI) was 17.2 (4.4–28.4) and 22 (54%) subjects had AHI ≥15. The hourly frequency of CVHR (Fcv) detected by the ACAT algorithm closely correlated with AHI (r = 0.81), while none of the time-domain, frequency-domain, or non-linear indices of pulse interval variability showed significant correlation. The Fcv was greater in subjects with AHI ≥15 (19.6 ± 12.3 /h) than in those with AHI <15 (6.4 ± 4.6 /h), and was able to discriminate them with 82% sensitivity, 89% specificity, and 85% accuracy. The classification performance was comparable to that obtained when the ACAT algorithm was applied to ECG R-R intervals during the PSG. The analysis of wearable watch PPG by the ACAT algorithm could be used for the quantitative screening of sleep apnea.

Improving the performance of peripheral arterial tonometry-based testing for the diagnosis of obstructive sleep apnea

Outside sleep laboratory settings, peripheral arterial tonometry (PAT, eg, WatchPat) represents a validated modality for diagnosing obstructive sleep apnea (OSA). We have shown before that the accuracy of home sleep apnea testing by WatchPat 200 devices in diagnosing OSA is suboptimal (50%-70%). In order to improve its diagnostic performance, we built several models that predict the main functional parameter of polysomnography (PSG), Apnea Hypopnea Index (AHI). Participants were recruited in our Sleep Center and underwent concurrent in-laboratory PSG and PAT recordings. Statistical models were then developed to predict AHI by using robust functional parameters from PAT-based testing, in concert with available demographic and anthropometric data, and their performance was confirmed in a random validation subgroup of the cohort. Five hundred synchronous PSG and WatchPat sets were analyzed. Mean diagnostic accuracy of PAT was improved to 67%, 81% and 85% in mild, moderate-severe or no OSA, respectively, by several models that included participants' age, gender, neck circumference, body mass index and the number of 4% desaturations/hour. WatchPat had an overall accuracy of 85.7% and a positive predictive value of 87.3% in diagnosing OSA (by predicted AHI above 5). In this large cohort of patients with high pretest probability of OSA, we built several models based on 4% oxygen desaturations, neck circumference, body mass index and several other variables. These simple models can be used at the point-of-care, in order to improve the diagnostic accuracy of the PAT-based testing, thus ameliorating the high rates of misclassification for OSA presence or disease severity.

Watch-PAT is Useful in the Diagnosis of Sleep Apnea in Patients with Atrial Fibrillation

BACKGROUND

Early diagnosis and treatment of sleep apnea in patients with atrial fibrillation (AF) is critical. The WatchPAT (WP) device was shown to be accurate for the diagnosis of sleep apnea; however, studies using the WatchPAT device have thus far excluded patients with arrhythmias due to the potential effect of arrhythmias on the peripheral arterial tonometry (PAT) amplitude and pulse rate changes.

PURPOSE

To examine the accuracy of the WP in detecting sleep apnea in patients with AF.

PATIENTS AND METHODS

Patients with AF underwent simultaneous WP and PSG studies in 11 sleep centers. PSG scoring was blinded to the automatically analyzed WP data.

RESULTS

A total of 101 patients with AF (70 males) were recruited. Forty-six had AF episodes during the overnight sleep study. A significant correlation was found between the PSG-derived AHI and the WP- derived AHI (r=0.80, p<0.0001). There was a good agreement between PSG-derived AHI and WP-derived AHI (mean difference of AHI: -0.02±13.2). Using a threshold of AHI ≥15 per hour of sleep, the sensitivity and specificity of the WP were 0.88 and 0.63, respectively. The overall accuracy in sleep staging between WP and PSG was 62% with Kappa agreement of 0.42.

CONCLUSION

WP can detect sleep apnea events in patients with AF. AF should not be an exclusion criterion for using the device. This finding may be of even greater importance in the era of the COVID19 epidemic, when sleep labs were closed and most studies were home based.

Quality of an ambulatory monitoring technique for diagnosing obstructive sleep apnea under conditions of limited resources

Objectives:

To: 1) evaluate the quality of an ambulatory monitoring technique for diagnosing Obstructive Sleep Apnea Syndrome (OSAS) while patients move through the city; and 2) identify factors that lead to data loss.

Methods:

Clinical histories were reviewed and ambulatory portable monitorings of adults with high pretest probability for OSAS were included, the signals monitored were pulse oximetry, heart rate, nasal pressure, snoring, chest band and body position. The equipment was connected from 14:00-20:00 h and then patients moved through the city turning it off and on at home. Results were analyzed visually to record all the minutes lost. A good-quality study was defined as recording time 240 min and signal loss <20%. A cost/benefit analysis was performed using Golpe et al.'s methodology.

Results:

A total of 70 recordings were analyzed. Most subjects were obese men with severe OSAS. Signal quality was determined to be good with a median signal loss of 4.9 min (0-405) that represented 1% (0-99) of total recording time. The signal lost most often was pulse oximetry at 1.8 min (0-403, p=0.0001). Of the 70 studies performed, 57 (81%) met the definition of good quality, while 13 (19%) had to be repeated. Men lost the pulse oximetry signal more often than women. This technique could represent savings of 65-75%.

Conclusions:

Placing a portable OSAS monitor during the day while patients move around the city turning it on and off at home does not affect the quality of the study results obtained and is a cost-effective method.

The role of home sleep testing for evaluation of patients with excessive daytime sleepiness: focus on obstructive sleep apnea and narcolepsy

Excessive daytime sleepiness (EDS) is a common complaint in the general population, which may be associated with a wide range of sleep disorders and other medical conditions. Narcolepsy is a sleep disorder characterized primarily by EDS, which involves a substantial burden of illness but is often overlooked or misdiagnosed. In addition to identifying low cerebrospinal fluid (CSF) hypocretin (orexin) levels, evaluation for narcolepsy requires in-laboratory polysomnography (PSG). Polysomnography is the gold standard for diagnosis of obstructive sleep apnea (OSA) as well as other sleep disorders. However, the use of home sleep apnea testing (HSAT) to screen for OSA in adults with EDS has increased greatly based on its lower cost, lower technical complexity, and greater convenience, versus PSG. The most commonly used, types 3 and 4, portable monitors for HSAT lack capability for electroencephalogram recording, which is necessary for the diagnosis of narcolepsy and other sleep disorders and is provided by PSG. These limitations, combined with the increased use of HSAT for evaluation of EDS, may further exacerbate the under-recognition of narcolepsy and other hypersomnias, either as primary or comorbid disorders with OSA. Adherence to expert consensus guidelines for use of HSAT is essential. Differential clinical characteristics of patients with narcolepsy and OSA may help guide correct diagnosis. Continued EDS in patients diagnosed and treated for OSA may indicate comorbid narcolepsy or another sleep disorder. Although HSAT may diagnose OSA in appropriately selected patients, it cannot rule out or diagnose narcolepsy. Therefore, at present, PSG and MSLT remain the cornerstone for narcolepsy diagnosis.

Clinical Trial Enrollment Enrichment in Resource-Constrained Research Environments: Multivariable Apnea Prediction (MAP) Index in SCIP-PA Trial

STUDY OBJECTIVES

Determine the Multivariable Apnea Prediction (MAP) index predictive utility for enrollment enrichment in a clinical trial wherein enrollment was prior to obstructive sleep apnea diagnosis.

METHODS

Secondary analysis of screening data (n = 264) from randomized, double-blind, pilot trial. Clinical sleep center patients with complete screening and polysomnography data were included. To determine diagnostic test accuracy of the MAP index using apnea-hypopnea index criterion ≥ 10 events/h (primary) and ≥ 5, ≥ 15, and ≥ 30 events/h (secondary), sensitivity, specificity, negative and positive predictive values, likelihood positive and negative ratios, and receiver operating characteristic curves were calculated. Predictive utility was examined by characteristic variables.

RESULTS

Middle-aged, overweight or obese, men and women were included. Employing a MAP index threshold ≥ 0.5, sensitivity for obstructive sleep apnea (apnea-hypopnea index ≥ 10 events/h) was 83.6%; specificity was 46.4%; area under the curve = 0.74. Sensitivity was higher in males than females (95.3%, 68.7%, respectively); specificity was lower in males than females (30.4%, 57.6%, respectively) with similar area under the curve (0.74 versus 0.72, respectively). MAP accuracy was higher in younger versus older adults (younger than 50 years, or 50 years or older; area under the curve 0.82 versus 0.63, respectively). Varied apnea-hypopnea index criteria produced stable accuracy estimates.

CONCLUSIONS

Recruitment/enrollment is a high-cost endeavor. Screening procedures may confer resource savings but careful evaluation prior to study implementation assures effectiveness and efficiency.

CLINICAL TRIAL REGISTRATION

The secondary analysis reports data from the SCIP-PA Trial (NCT 01454830); study information available at: https://clinicaltrials.gov.

Diagnosis of sleep apnea in patients with stable chronic heart failure using a portable sleep test diagnostic device

PURPOSE

ApneaLink is a portable device for the screening of sleep apnea, a prevalent and underdiagnosed comorbidity in heart failure patients. A prospective cross-sectional study in patients with chronic heart failure was carried out to assess the sensitivity and specificity of apnea-hypopnea index (AHI) measurements using ApneaLink against the standard polysomnography test.

METHODS

Adult patients with a prior hospitalization in an acute heart failure hospital unit were recruited for the study. All participants were tested for sleep apnea using ApneaLink and polysomnography simultaneously during an overnight stay at a sleep laboratory. Global sleep apnea was evaluated according to the AHI, which was analyzed and compared. Subpopulation comparison based on ejection fraction was not realized due to population size.

RESULTS

Thirty-five patients with stable chronic heart failure completed the study (mean age 70.9 ± 10.5 years and body mass index 30.0 ± 4.7 kg/m²). Two patients were excluded due to insufficient study duration. ApneaLink had a sensitivity greater than 80% for all AHI measurements, and a specificity greater than 80% for all AHI measurements, except for AHI ≥ 5 events/h (61.5%). The results showed higher sensitivities and specificities at AHI values of ≥ 10 events/h (sensitivity 81.3% and specificity 84.2%) and ≥ 15 events/h (sensitivity 83.3% and specificity 91.3%). Correlation analysis showed that AHI measurements using ApneaLink and polysomnography had a strong and significant correlation (r = 0.794; P < 0.001).

CONCLUSIONS

Our results suggest that ApneaLink could be used in clinical practice to identify heart failure patients with high (AHI ≥ 15 events/h) and low (AHI < 5 events/h) probability of having sleep apnea, sparing the need for a diagnostic polysomnography and thus potentially impacting prognosis by providing a more cost-effective and timely diagnosis of this non-cardiac comorbidity.