How sleepy patients differ from non-sleepy patients in mild obstructive sleep apnea?

Excessive daytime sleepiness is associated with relative delta frequency power among patients with mild OSA

Background

Excessive daytime sleepiness (EDS) is a cause of low quality of life among obstructive sleep apnoea (OSA) patients. Current methods of assessing and predicting EDS are limited due to time constraints or differences in subjective experience and scoring. Electroencephalogram (EEG) power spectral densities (PSDs) have shown differences between OSA and non-OSA patients, and fatigued and non-fatigued patients. Therefore, polysomnographic EEG PSDs may be useful to assess the extent of EDS among patients with OSA.

Methods

Patients presenting to Israel Loewenstein hospital reporting daytime sleepiness who recorded mild OSA on polysomnography and undertook a multiple sleep latency test. Alpha, beta, and delta relative powers were assessed between patients categorized as non-sleepy (mean sleep latency (MSL) ≥10 min) and sleepy (MSL <10 min).

Results

139 patients (74% male) were included for analysis. 73 (53%) were categorized as sleepy (median MSL 6.5 min). There were no significant differences in demographics or polysomnographic parameters between sleepy and non-sleepy groups. In multivariate analysis, increasing relative delta frequency power was associated with increased odds of sleepiness (OR 1.025 (95% CI 1.024-1.026)), while relative alpha and beta powers were associated with decreased odds. The effect size of delta PSD on sleepiness was significantly greater than that of either alpha or beta frequencies.

Conclusion

Delta PSD during polysomnography is significantly associated with a greater degree of objective daytime sleepiness among patients with mild OSA. Further research is needed to corroborate our findings and identify the direction of potential causal correlation between delta PSD and EDS.

The Beginning of the AI-Enabled Preventative PAP Therapy Era: A First-in-Human Proof of Concept Interventional Study

Positive Airway Pressure (PAP) therapy is the most common and efficacious treatment for Obstructive Sleep Apnea (OSA). However, it suffers from poor patient adherence due to discomfort and may not fully alleviate all adverse consequences of OSA. Identifying abnormal respiratory events before they have occurred may allow for improved management of PAP levels, leading to improved adherence and better patient outcomes. Our previous work has resulted in the successful development of a Machine-Learning (ML) algorithm for the prediction of future apneic events using existing airflow and air pressure sensors available internally to PAP devices. Although researchers have studied the use of ML for the prediction of apneas, research to date has focused primarily on using external polysomnography sensors that add to patient discomfort and has not investigated the use of internal-to-PAP sensors such as air pressure and airflow to predict and prevent respiratory events. We hypothesized that by using our predictive software, OSA events could be proactively prevented while maintaining patients' sleep quality. An intervention protocol was developed and applied to all patients to prevent OSA events. Although the protocol's cool-down period limited the number of prevention attempts, analysis of 11 participants revealed that our system improved many sleep parameters, which included a statistically significant 31.6% reduction in Apnea-Hypopnea Index, while maintaining sleep quality. Most importantly, our findings indicate the feasibility of unobtrusive identification and unique prevention of each respiratory event as well as paving the path to future truly personalized PAP therapy by further training of ML models on individual patients.

Independent Association Between Oxygen Desaturation Index and Cardiovascular Disease in Non-Sleepy Sleep-Disordered Breathing Subtype: A Chinese Community-Based Study

PURPOSE

Non-sleepy sleep-disordered breathing (SDB) is increasingly recognized as an important clinical subtype. The association between non-sleepy SDB and cardiovascular disease (CVD) is not well understood. Our objectives were to investigate the relationship between non-sleepy SDB and CVD and determine which nocturnal hypoxia parameter most strongly reflects this association in a large community population.

PATIENTS AND METHODS

Cross-sectional data from 3626 randomly-selected Chinese community-dwelling participants who underwent overnight type IV sleep monitoring were analyzed. Parameters of nocturnal hypoxemia were extracted from sleep monitoring devices, including mean nocturnal oxygen saturation, lowest oxygen saturation, oxygen desaturation index (ODI), and time with oxygen saturation <90%. An ODI ≥7.0 events/h was considered to signify SDB. An Epworth Sleepiness Scale score of 10 or less indicated no sleepiness.

RESULTS

The SDB rate was 30.7% (1114/3626), of which 96.5% (1075/1114) were considered the non-sleepy SDB subtype. ODI, typical nocturnal intermittent hypoxia indicator for SDB, was independently related to CVD, regardless of whether excessive daytime sleepiness was present. After adjusting for confounders, ODI most strongly reflected the association between non-sleepy SDB and CVD (OR:1.023; 95% CI:1.003-1.043). We observed a nonlinear association between ODI and the prevalence of CVD, where the likelihood of CVD increased with ODI≥10 events/h and a markedly increasing trend was observed with ODI ≥20 events/h (reference ODI = 7.0 events/h). Metabolic parameters, Pittsburgh Sleep Quality Index, and inflammatory marker did not mediate the association between ODI and CVD in the non-sleepy SDB subtype.

CONCLUSION

In the Chinese community-dwelling population, non-sleepy SDB was highly prevalent. ODI, an easily extracted indicator from a type IV sleep monitor, most strongly reflected the association between non-sleepy SDB and CVD.