Advanced polysomnographic analysis for OSA: A pathway to personalized management?

Abstract

Obstructive sleep apnea (OSA) is a highly heterogeneous disorder, with diverse pathways to disease, expression of disease, susceptibility to co‐morbidities and response to therapy, and is ideally suited to precision medicine approaches. Clinically, the content of the information‐rich polysomnogram (PSG) is not currently fully utilized in determining patient management. Novel PSG parameters such as hypoxic burden, pulse transit time, cardiopulmonary coupling and the frequency representations of PSG sensor signals could predict a variety of cardiovascular disease, cancer and neurodegeneration co‐morbidities. The PSG can also be used to identify key pathophysiological parameters such as loop gain, arousal threshold and muscle compensation which can enhance understanding of the causes of OSA in an individual, and thereby guide choices on therapy. Machine learning methods performing their own parameter extraction coupled with large PSG data sets offer an exciting opportunity for discovering new links between the PSG variables and disease outcomes. By exploiting existing and emerging analytical methods, the PSG may offer a pathway to personalized management for OSA.

<p>A Review of Current Tools Used for Evaluating the Severity of Obstructive Sleep Apnea</p>

“…An electrocardiogram (ECG)-based method has raised extensive attention after use in many studies to characterize the coupling of heart rate variability (HRV) and ECG-derived respiratory fluctuations and quantify the relative sleep quality as stable (high-frequency coupling, HFC) or unstable (low-frequency coupling, LFC), thus termed cardiopulmonary coupling (CPC). 55 This measure was proven to be correlated with objective sleep quality. 56 In addition, in one study, an increased HFC and decreased LFC were seen in a CPAP group, but not in the matched control group, 57 suggesting that CPC plays a potential role in reflecting and monitoring therapeutic effects for patients with OSA.…”

Big Data in sleep apnoea: Opportunities and challenges

“…We have used this Big Data resource to identify drugs previously unknown as generating or exacerbating CSA. By applying a ‘case‐ non‐case’ methodology to VigiBase with a disproportionality analysis, our group revealed the implication of baclofen, ticagrelor and gabapentinoids in triggering CSA and elucidated the mechanisms underlying this reaction …”

Update on oral appliance therapy

“…The AHI has been found to be a weaker predictor to identify MAD treatment responders than previously believed and severe patients might also have a good outcome [16][17][18]. Instead, more advanced ways to interpret sleep apnoea recordings are under development [19,20]. This will allow the identification of various phenotypes of sleep apnoea patients and provide a more promising way forward to find patients who respond to OSA treatment in different ways.…”