Quantifying statistical uncertainty in metrics of sleep disordered breathing

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.

Longitudinal risk factors for obstructive sleep apnea: A systematic review

Despite substantial disease burden, existing evidence on the risk factors for obstructive sleep apnea (OSA) have been derived primarily from cross-sectional studies without determining temporality. Therefore, we aimed to systematically synthesize the literature on longitudinal risk factors for sleep study-assessed OSA and questionnaire-assessed probable OSA from cohort studies in the general adult population settings. We systematically searched Embase and Medline (on OVID) databases. Eleven studies met the inclusion criteria. Meta-analyses were not conducted due to methodological heterogeneity of exposure and outcome measurements. There was consistent evidence that weight gain was associated with incident (n = 2) and greater severity (n = 2) of OSA. One study each observed an association of higher baseline body-mass index, male sex, asthma, a specific genetic polymorphism in rs12415421, and insulin resistance/hyperglycemia, with incident OSA. Long-term exposure to ambient air pollution (NO2, n = 1) was associated with OSA, and menopausal transitions (n = 1) with higher apnea-hypopnea index. There were no eligible studies on long-term smoking or alcohol use. In conclusion, approximately 10% increase in weight, especially in males, might alert clinicians to consider potential or worsening OSA. Large, well-designed longitudinal studies are needed to consolidate knowledge on other associations with OSA development, especially on potentially modifiable risk factors.

High night-to-night variability in sleep apnea severity is associated with uncontrolled hypertension

Obstructive sleep apnea (OSA) severity can vary markedly from night-to-night. However, the impact of night-to-night variability in OSA severity on key cardiovascular outcomes such as hypertension is unknown. Thus, the primary aim of this study is to determine the effects of night-to-night variability in OSA severity on hypertension likelihood. This study uses in-home monitoring of 15,526 adults with ~180 nights per participant with an under-mattress sleep sensor device, plus ~30 repeat blood pressure measures. OSA severity is defined from the mean estimated apnea-hypopnoea index (AHI) over the ~6-month recording period for each participant. Night-to-night variability in severity is determined from the standard deviation of the estimated AHI across recording nights. Uncontrolled hypertension is defined as mean systolic blood pressure ≥140 mmHg and/or mean diastolic blood pressure ≥90 mmHg. Regression analyses are performed adjusted for age, sex, and body mass index. A total of 12,287 participants (12% female) are included in the analyses. Participants in the highest night-to-night variability quartile within each OSA severity category, have a 50-70% increase in uncontrolled hypertension likelihood versus the lowest variability quartile, independent of OSA severity. This study demonstrates that high night-to-night variability in OSA severity is a predictor of uncontrolled hypertension, independent of OSA severity. These findings have important implications for the identification of which OSA patients are most at risk of cardiovascular harm.

Dynamic models of obstructive sleep apnea provide robust prediction of respiratory event timing and a statistical framework for phenotype exploration

Obstructive sleep apnea (OSA), in which breathing is reduced or ceased during sleep, affects at least 10% of the population and is associated with numerous comorbidities. Current clinical diagnostic approaches characterize severity and treatment eligibility using the average respiratory event rate over total sleep time (apnea-hypopnea index). This approach, however, does not characterize the time-varying and dynamic properties of respiratory events that can change as a function of body position, sleep stage, and previous respiratory event activity. Here, we develop a statistical model framework based on point process theory that characterizes the relative influences of all these factors on the moment-to-moment rate of event occurrence. Our results provide new insights into the temporal dynamics of respiratory events, suggesting that most adults have a characteristic event pattern that involves a period of normal breathing followed by a period of increased probability of respiratory event occurrence, while significant differences in event patterns are observed among gender, age, and race/ethnicity groups. Statistical goodness-of-fit analysis suggests consistent and substantial improvements in our ability to capture the timing of individual respiratory events using our modeling framework. Overall, we demonstrate a more statistically robust approach to characterizing sleep disordered breathing that can also serve as a basis for identifying future patient-specific respiratory phenotypes, providing an improved pathway towards developing individualized treatments.

OSA and Chronic Respiratory Disease: Mechanisms and Epidemiology

Obstructive sleep apnea (OSA) is a highly prevalent disorder that has profound implications on the outcomes of patients with chronic lung disease. The hallmark of OSA is a collapse of the oropharynx resulting in a transient reduction in airflow, large intrathoracic pressure swings, and intermittent hypoxia and hypercapnia. The subsequent cytokine-mediated inflammatory cascade, coupled with tractional lung injury, damages the lungs and may worsen several conditions, including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and pulmonary hypertension. Further complicating this is the sleep fragmentation and deterioration of sleep quality that occurs because of OSA, which can compound the fatigue and physical exhaustion often experienced by patients due to their chronic lung disease. For patients with many pulmonary disorders, the available evidence suggests that the prompt recognition and treatment of sleep-disordered breathing improves their quality of life and may also alter the course of their illness. However, more robust studies are needed to truly understand this relationship and the impacts of confounding comorbidities such as obesity and gastroesophageal reflux disease. Clinicians taking care of patients with chronic pulmonary disease should screen and treat patients for OSA, given the complex bidirectional relationship OSA has with chronic lung disease.

Multi-night Prevalence, Variability, and Diagnostic Misclassification of Obstructive Sleep Apnea

Rationale Recent studies suggest that obstructive sleep apnea (OSA) severity can vary markedly from night to night which may have important implications for diagnosis and management. Objectives This study aimed to assess OSA prevalence from multi-night in-home recordings and the impact of night-to-night variability in OSA severity on diagnostic classification in a large, global, non-randomly selected community sample from a consumer database of people that purchased a novel, validated, under-mattress sleep analyzer. Methods 67,278 individuals aged between 18 and 90 years underwent in-home nightly monitoring over an average of ~170 nights per participant between July 2020 to March 2021. OSA was defined as a nightly mean apnea-hypopnea index (AHI) >15 events/h. Outcomes were multi-night global prevalence and likelihood of OSA misclassification from a single night AHI value. Measurements and Main Results Over 11.6 million nights of data were collected and analyzed. OSA global prevalence was 22.6% (95% CI: 20.9-24.3%). The likelihood of misdiagnosis in people with OSA based on a single night ranged between ~20% and 50%. Misdiagnosis error rates decreased with increased monitoring nights (e.g. 1-night F1-score=0.77 vs. 0.94 for 14-nights); and remained stable after 14-nights of monitoring. Conclusions Multi-night in-home monitoring using novel non-invasive under mattress sensor technology indicates a global prevalence of moderate to severe OSA of ~20%, and that ~20% of people diagnosed with a single night study may be misclassified. These findings highlight the need to consider night-to-night variation on OSA diagnosis and management. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Validation of the Withings Sleep Analyzer, an under-the-mattress device for the detection of moderate-severe sleep apnea syndrome

STUDY OBJECTIVES

To assess the diagnostic performance of a nonintrusive device placed under the mattress to detect sleep apnea syndrome.

METHODS

One hundred eighteen patients suspected to have obstructive sleep apnea syndrome completed a night at a sleep clinic with a simultaneous polysomnography (PSG) and recording with the Withings Sleep Analyzers. PSG nights were scored twice: first as simple polygraphy, then as PSG.

RESULTS

Average (standard deviation) apnea-hypopnea index from PSG was 31.2 events/h (25.0) and 32.8 events/h (29.9) according to the Withings Sleep Analyzers. The mean absolute error was 9.5 events/h. The sensitivity, specificity, and area under the receiver operating characteristic curve at thresholds of apnea-hypopnea index ≥ 15 events/h were, respectively, sensitivity (Se)₁₅ = 88.0%, specificity (Sp)₁₅ = 88.6%, and area under the receiver operating characteristic curve (AUROC) ₁₅ = 0.926. At the threshold of apnea-hypopnea index ≥ 30 events/h, results included Se₃₀ = 86.0%, Sp₃₀ = 91.2%, AUROC₃₀ = 0.954. The average total sleep time from PSG and the Withings Sleep Analyzers was 366.6 (61.2) and 392.4 (67.2) minutes, sleep efficiency was 82.5% (11.6) and 82.6% (11.6), and wake after sleep onset was 62.7 (48.0) and 45.2 (37.3) minutes, respectively.

CONCLUSIONS

Withings Sleep Analyzers accurately detect moderate-severe sleep apnea syndrome in patients suspected of sleep apnea syndrome. This simple and automated approach could be of great clinical value given the high prevalence of sleep apnea syndrome in the general population.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Name: Validation of Withings Sleep for the Detection of Sleep Apnea Syndrome; URL: https://clinicaltrials.gov/ct2/show/NCT04234828; Identifier: NCT04234828.