Comparative associations of oximetry patterns in Obstructive Sleep Apnea with incident cardiovascular disease

Obstructive sleep apnea diagnosis and beyond using portable monitors

Obstructive sleep apnea (OSA) is a chronic sleep and breathing disorder with significant health complications, including cardiovascular disease and neurocognitive impairments. To ensure timely treatment, there is a need for a portable, accurate and rapid method of diagnosing OSA. This review examines the use of various physiological signals used in the detection of respiratory events and evaluates their effectiveness in portable monitors (PM) relative to gold standard polysomnography. The primary objective is to explore the relationship between these physiological parameters and OSA, their application in calculating the apnea hypopnea index (AHI), the standard metric for OSA diagnosis, and the derivation of non-AHI metrics that offer additional diagnostic value. It is found that increasing the number of parameters in PMs does not necessarily improve OSA detection. Several factors can cause performance variations among different PMs, even if they extract similar signals. The review also highlights the potential of PMs to be used beyond OSA diagnosis. These devices possess parameters that can be utilized to obtain endotypic and other non-AHI metrics, enabling improved characterization of the disorder and personalized treatment strategies. Advancements in PM technology, coupled with thorough evaluation and validation of these devices, have the potential to revolutionize OSA diagnosis, personalized treatment, and ultimately improve health outcomes for patients with OSA. By identifying the key factors influencing performance and exploring the application of PMs beyond OSA diagnosis, this review aims to contribute to the ongoing development and utilization of portable, efficient, and effective diagnostic tools for OSA.

A Review of Novel Oximetry Parameters for the Prediction of Cardiovascular Disease in Obstructive Sleep Apnoea

Obstructive sleep apnoea (OSA) is a sleep disorder with repetitive collapse of the upper airway during sleep, which leads to intermittent hypoxic events overnight, adverse neurocognitive, metabolic complications, and ultimately an increased risk of cardiovascular disease (CVD). The standard diagnostic parameter for OSA, apnoea-hypopnoea index (AHI), is inadequate to predict CVD morbidity and mortality, because it focuses only on the frequency of apnoea and hypopnoea events, and fails to reveal other physiological information for the prediction of CVD events. Novel parameters have been introduced to compensate for the deficiencies of AHI. However, the calculation methods and criteria for these parameters are unclear, hindering their use in cross-study analysis and studies. This review aims to discuss novel parameters for predicting CVD events from oximetry signals and to summarise the corresponding computational methods.

A comparison of cardiovascular disease associations of time-domain oximetry parameters in sleep apnoea cases from the Sleep Heart Health Study

Polysomnograms (PSGs) contain a wealth of physiological information that is routinely recorded but not utilised in sleep studies. Intermittent hypoxia arising from obstructive sleep apnoea (OSA) events is an important risk in the later development of cardiovascular disease (CVD). Analysis of oximetry patterns from PSG studies may enable early assessment of CVD risk. The aim of this study was to compare associations of different time-domain oximetry patterns with incident CVD in OSA patients. All participants with OSA and no pre-existing CVD at baseline or within the first two years of follow-up, were selected from the Sleep Heart Health Study data and used for analysis (N=2878). We examined oximetry parameters that are calculated from desaturation events and from time series analysis and compared them to incident CVD outcomes using proportional hazards regression models adjusted for age, race, smoking, BMI, and sex. Our results show that were no associations between OSA oximetry parameters and incident CVD for OSA patients.

A comparison of hypoxic burden algorithms using three different methods for calculating baseline oxygen saturation for predicting cardiovascular death in the Sleep Heart Health Study

Respiratory event related oxygen desaturation area measures have recently shown merit as novel predictors of cardiovascular disease (CVD) outcomes. In this study, we investigate one such measure (hypoxic burden (HB)) and investigate how three different ways of calculating the SpO2 baseline of the HB algorithm impact its ability to predict cardiovascular mortality. The three baseline estimation steps include a pre-event baseline, a record-based baseline, and a fixed baseline. Pulse oximetry signals from the Sleep Heart Health Study and the corresponding CVD outcomes were analyzed. The performance of each baseline method was compared using adjusted Cox proportional hazard regression analysis. Results show that HB with the record-based baseline method returned the best performing results with a hazard ratio (HR) of 1.83 (95% CI: 1.03-3.27, p<0.05) in the fully adjusted model, compared to HB with the pre-event baseline method (HR: 1.60, 95%CI: 0.86-3.00, p>0.05) and HB with the fixed baseline method (HR: 1.73, 95%CI: 0.93-3.22, p>0.05).

Time Spent with Saturation below 80% versus 90% in Patients with Obstructive Sleep Apnoea

BACKGROUND

Nocturnal hypoxaemia measured as the percentage of total sleep time spent with saturation below 90% (TST90%) may better predict cardiovascular consequences of obstructive sleep apnoea (OSA) than the number of obstructive respiratory events measured with the apnoea-hypopnea index (AHI). Deeper hypoxaemia may potentially induce more severe pathophysiological consequences. However, the additional value of the percentage of total sleep time spent with saturation below 80% (TST80%) to TST90% is not fully explored.

METHODS

Comprehensive medical history was taken and fasting lipid and C-reactive protein levels were measured in 797 volunteers participating in two cohort studies in Hungary and Romania. Sleep parameters, including AHI, TST90% and TST80%, were recorded following a polysomnography (PSG, n = 598) or an inpatient cardiorespiratory polygraphy (n = 199). The performance of TST80% to predict cardiovascular risk was compared with TST90% using linear and logistic regression analyses as well receiver operating characteristics curves. Sensitivity analyses were performed in patients who had PSG, separately.

RESULTS

Both parameters are significantly related to cardiovascular risk factors; however, TST80% did not show better predictive value for cardiovascular risk than TST90%. On the other hand, patients with more severe hypoxaemia reported more excessive daytime sleepiness.

CONCLUSIONS

TST80% has limited additional clinical value compared to TST90% when evaluating cardiovascular risk in patients with OSA.

Role of precision medicine in obstructive sleep apnoea

Obstructive sleep apnoea is a substantial clinical and public health problem because it contributes to harmful effects on quality of life, daytime symptoms, road traffic incidents, and cardiometabolic disease. Increasingly, obstructive sleep apnoea is recognised as a heterogeneous disease, and patients have varied susceptibility to long term complications and different responses to treatment. This narrative review summarises the current knowledge of precision medicine in obstructive sleep apnoea, particularly the role of symptom clusters, polysomnogram phenotypes, physiological endotypes, and circulating biomarkers in defining subtypes. In the near future, the prognostic accuracy of these measures in predicting long term complications in obstructive sleep apnoea will likely be improved, together with better matching of treatments to disease subtypes.