Cathy A. Goldstein

Study objectives: Evaluate the performance of a novel home sleep apnea test with embedded ECG (SANSA, Huxley Medical, Inc.) in the diagnosis of obstructive sleep apnea (OSA).Methods: This prospective multicenter validation study included 340 participants who underwent simultaneous polysomnography (PSG) and SANSA recordings across 7 clinical sites. Participants were diverse across age, sex, race, skin tone, and body mass index. Diagnostic performance was assessed with the apnea-hypopnea index (AHI) using both Rule 1A and Rule 1B across standard cutoffs for mild, moderate, or severe (≥5 events/h), moderate-to-severe (≥15 events/h), and severe (≥30 events/h) OSA. The agreement for AHI and total sleep time (TST) between SANSA and consensus PSG scores from three independent scorers was evaluated using Pearson’s correlation and Bland–Altman analysis. Sensitivity and specificity were calculated at each OSA severity level. Performance of participating site PSG scores were also evaluated against consensus PSG scores for comparison.Results: SANSA demonstrated excellent agreement with PSG for most performance parameters. AHI correlation was 0.91 (95% CI: 0.89, 0.93) using Rule 1B and 0.90 (95% CI: 0.87, 0.92) using Rule 1A. Compared to consensus scored PSG, the device detected moderate-to-severe OSA using Rule 1B (the primary endpoint) with a sensitivity of 88% (95% CI: 81, 93%) and specificity of 87% (95% CI: 82, 91%), while site PSG sensitivity was 89% (95% CI: 82, 94%) and specificity was 93% (95% CI: 88, 96%). SANSA TST highly correlated with PSG TST (R = 0.82, 95% CI: 0.78, 0.85) and classified sleep epochs with an accuracy of 87.2% (95% CI: 87.0, 87.5%).Conclusion: The SANSA home sleep apnea test demonstrated robust diagnostic performance for OSA detection including measurement of sleep compared to PSG. Its patch morphology and embedded ECG confer ease of use and multi-diagnostic potential in sleep medicine and cardiology for the detection of OSA and cardiac arrhythmias across diverse clinical populations.Clinical trial registration: [https://www.clinicaltrials.gov/study/NCT06070389], identifier [NCT06070389]

Evaluate the performance of the SANSA device to simultaneously assess obstructive sleep apnea (OSA) and cardiac arrhythmias. Participants suspected or known to have OSA underwent polysomnography (PSG) while wearing SANSA. SANSA's algorithm was trained using 86 records and tested on 67 to evaluate training bias. SANSA performance was evaluated against ground truth PSG scored by the consensus of three technologists. PSG scoring from individual testing sites was also evaluated against consensus. Diagnostic performance was evaluated using standard apnea-hypopnea index (AHI) cutoffs. AHI and total sleep time (TST) agreement was analyzed using correlation and Bland-Altman plots. ECG was reviewed for presence of significant arrhythmias (frequent premature atrial/ventricular complexes and atrial fibrillation). SANSA's sensitivity and specificity to detect OSA ranged from 91-97% and 78-97%, respectively, across all severity levels. SANSA TST correlation with Consensus PSG TST was 0.83 with a mean difference of 3.8 minutes (limits of agreement: -91.1 to 98.7). Significant arrhythmias were detected in 32% of participants. These participants had a greater AHI (27.5 versus 15.8, P=0.003) and spent nearly twice as long at reduced oxygenation levels (47.5 versus 20.5 minutes under 88% SpO₂, P = 0.009). SANSA is a promising tool for comprehensive OSA evaluation, offering the unique advantage of concurrent arrhythmia detection. This dual functionality may improve patient outcomes through early diagnosis and management of both conditions.

Cathy Goldstein, Massimiliano de Zambotti; Into the wild…the need for standardization and consensus recommendations to leverage consumer-facing sleep technologi

Background The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI. Method The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature. Results Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI’s generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice. Conclusion Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.

Study Objectives:Obstructive sleep apnea (OSA) and other sleep disorders overlap with co-morbidities associated with poor outcomes related to SARS-CoV-2 infection. However, the prevalence of OSA am...

A paradox exists in sleep medicine and research—disordered sleep is detrimental through nightly, chronic exposure to sleep disturbances over months to years, but our gold-standard approach to assess sleep, the polysomnogram (PSG) [ 1], is generally used in the laboratory and is not practical for more than one to two nights of consecutive sleep assessments. Thus, current PSG technology lacks the capacity to assess sleep in an ecologically valid environment outside the laboratory where night-to-night variation in sleep disturbances likely occur over months to years.

Citation:Goldstein CA, Burgess HJ. Hit or miss: the use of melatonin supplements. J Clin Sleep Med. 2020;16(suppl_1):29S–30S.