Night-to-night fluctuations in sleep apnea severity: diagnostic and treatment implications

BAFNet: Bottleneck Attention Based Fusion Network for Sleep Apnea Detection

Sleep apnea (SA) is a common sleep-related breathing disorder that tends to induce a series of complications, such as pediatric intracranial hypertension, psoriasis, and even sudden death. Therefore, early diagnosis and treatment can effectively prevent malignant complications SA incurs. Portable monitoring (PM) is a widely used tool for people to monitor their sleep conditions outside of hospitals. In this study, we focus on SA detection based on single-lead electrocardiogram (ECG) signals which are easily collected by PM. We propose a bottleneck attention based fusion network named BAFNet, which mainly includes five parts of RRI (R-R intervals) stream network, RPA (R-peak amplitudes) stream network, global query generation, feature fusion, and classifier. To learn the feature representation of RRI/RPA segments, fully convolutional networks (FCN) with cross-learning are proposed. Meanwhile, to control the information flow between RRI and RPA networks, a global query generation with bottleneck attention is proposed. To further improve the SA detection performance, a hard sample scheme with k-means clustering is employed. Experiment results show that BAFNet can achieve competitive results, which are superior to the state-of-the-art SA detection methods. It means that BAFNet has great potential to be applied in the home sleep apnea test (HSAT) for sleep condition monitoring.

Multi-night measurement for diagnosis and simplified monitoring of obstructive sleep apnoea

Substantial night-to-night variability in obstructive sleep apnoea (OSA) severity has raised misdiagnosis and misdirected treatment concerns with the current prevailing single-night diagnostic approach. In-home, multi-night sleep monitoring technology may provide a feasible complimentary diagnostic pathway to improve both the speed and accuracy of OSA diagnosis and monitor treatment efficacy. This review describes the latest evidence on night-to-night variability in OSA severity, and its impact on OSA diagnostic misclassification. Emerging evidence for the potential impact of night-to-night variability in OSA severity to influence important health risk outcomes associated with OSA is considered. This review also characterises emerging diagnostic applications of wearable and non-wearable technologies that may provide an alternative, or complimentary, approach to traditional OSA diagnostic pathways. The required evidence to translate these devices into clinical care is also discussed. Appropriately sized randomised controlled trials are needed to determine the most appropriate and effective technologies for OSA diagnosis, as well as the optimal number of nights needed for accurate diagnosis and management. Potential risks versus benefits, patient perspectives, and cost-effectiveness of these novel approaches should be carefully considered in future trials.

A new wearable diagnostic home sleep testing platform: comparison with available systems and benefits of multinight assessments

STUDY OBJECTIVES

We assessed the real-world performance of the ANNE Sleep system against 2 Food and Drug Administration-cleared home sleep testing platforms and the intraindividual night-to-night variability of respiratory event index measured by ANNE Sleep.

METHODS

We evaluated the home performance of the ANNE Sleep system compared with 2 Food and Drug Administration-cleared home sleep testing platforms (WatchPAT: n = 29 and Alice NightOne: n = 46) during a synchronous night with unsupervised patient application. Additionally, we evaluated night-to-night variability of respiratory event index and total sleep time using the ANNE Sleep system (n = 30).

RESULTS

For the diagnosis of moderate and severe obstructive sleep apnea, the ANNE Sleep system had a positive percent agreement of 58% (95% confidence interval, 28-85%) and a negative percent agreement of 100% (95% confidence interval, 80-100%) compared to WatchPAT. The positive and negative percent agreement for ANNE Sleep vs Alice NightOne was 85% (95% confidence interval, 66-96%) and 95% (95% confidence interval, 74-100%). There were no differences in mean total sleep time or respiratory event index across multiple nights of monitoring with ANNE. There were no differences consistent with a first-night effect but testing multiple nights reclassified obstructive sleep apnea severity in 5 (17%) individuals and detected 3 additional cases of moderate disease, with only a 12% (standard deviation, 28%) mean fluctuation in respiratory event index from the first night of testing compared to a mean of multiple nights. Overall, 80% of users found ANNE comfortable and easy to use.

CONCLUSIONS

ANNE Sleep exhibited stronger concordance with Alice NightOne compared to WatchPAT. While we illustrated low night-to-night variability for ANNE Sleep, the results suggest multiple nights increased detection of moderate or severe obstructive sleep apnea.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Name: ANNE Diagnostic Agreement With Home Sleep Testing; URL: https://clinicaltrials.gov/ct2/show/NCT05421754; Identifier: NCT05421754.

CITATION

Walter J, Lee JY, Blake S, et al. A new wearable diagnostic home sleep testing platform: comparison with available systems and benefits of multinight assessments. J Clin Sleep Med. 2023;19(5):865-872.

Oximetría de pulso en enfermedades respiratorias

El pulsioxímetro es un dispositivo que utiliza principios de espectrofotometría y fotopletismografía para la medición de la saturación de oxígeno arterial, así como el ciclo cardiaco y respiratorio, lo que resulta útil para monitorear pacientes con compromisorespiratorio. En este trabajo se realiza una revisión bibliográfica de los principios físicos del pulsioxímetro y sus avances más recientes en pacientes con enfermedad pulmonar obstructiva crónica (EPOC), asma y COVID-19. Se encontró que la oximetría de pulso es una herramienta confiable y eficaz en el diagnóstico y la prevención de complicaciones en pacientes con estas enfermedades respiratorias.

A single-arm, open-label, multicenter, and comparative study of the ANNE sleep system vs polysomnography to diagnose obstructive sleep apnea

STUDY OBJECTIVES

Evaluate per-patient diagnostic performance of a wireless dual-sensor system (ANNE sleep) compared with reference standard polysomnography (PSG) for the diagnosis of moderate and severe obstructive sleep apnea (OSA) with a minimum prespecified threshold of 80% for both sensitivity and specificity.

METHODS

A multicenter clinical trial was conducted to evaluate ANNE sleep vs PSG to diagnose moderate and severe OSA in individuals 22 years or older. For each testing approach, apnea-hypopnea index (AHI) was manually scored and averaged by 3 registered sleep technologists blinded to the other system. Average variations > 15% were adjudicated by a sleep medicine physician.

RESULTS

In a total of n = 225 participants (mean age 53 years, range 22-88 years), PSG diagnosed 30% (n = 68) of participants with moderate or severe OSA (AHI ≥ 15 events/h) compared to 29% (n = 65) diagnosed by ANNE sleep (P = .55). The sensitivity and specificity for ANNE sleep were 90% (95% confidence interval: 80-96%) and 98% (95% confidence interval: 94-99%), respectively. Strong correlation was shown in terms of final AHI (r = .93), with an average AHI bias of 0.5 (95% limits of agreement: -12.8 to 11.8). The majority of users noted comfort with using the ANNE sleep in the home setting. No adverse events were noted.

CONCLUSIONS

Using PSG as the gold standard, ANNE sleep demonstrated high sensitivity and specificity for the diagnosis of moderate or severe OSA.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Name: Comparative Study of the ANNE™ One System to Diagnose Obstructive Sleep Apnea; URL: https://clinicaltrials.gov/ct2/show/NCT04643782; Identifier: NCT04643782.

CITATION

Davies C, Lee JY, Walter J et al. A single-arm, open-label, multicenter, and comparative study of the ANNE sleep system vs polysomnography to diagnose obstructive sleep apnea. J Clin Sleep Med. 2022;18(12):2703-2712.