Polysomnographic determinants of requirement for advanced positive pressure therapeutic options for obstructive sleep apnea

Practical and rapidly-implemented parameters for assessing APAP titration failure

BACKGROUND

This study aimed to show the predictive value of simple polysomnographic parameters including latency of deep sleep (nREM3), latency of rapid eye movement sleep (REM), and minimum oxygen saturation (SpO₂) for predicting failure of autoadjusting positive airway pressure (APAP) titration.

METHODS

Out of 1470 patients with moderate to severe obstructive sleep apnea syndrome (OSAS) who underwent APAP titration between July 1, 2016, and December 31, 2017, 22 patients with titration failure were enrolled in the study. The demographic and polysomnographic characteristics of this group were compared with 44 patients with an adequate APAP titration who were matched with the titration failure group by age, sex, and OSAS severity. The periods between the start of sleep and the start of REM and nREM3 stages were noted as REM latency and nREM3 latency, respectively.

RESULTS

The between group differences in the parameters including nREM3 latency, REM latency, and minimum SpO₂ during the titration test were statistically significant (p = 0.004, p = 0.008, p <0.001 respectively). Possible threshold values to predict failure of APAP titration were found as 40 min and 135 min for nREM3 and REM latencies, respectively. The best threshold for minimum SpO₂ was 86% with an 86.4% of sensitivity. Despite the high negative predictive values (81% for nREM3 latency, 75% for REM latency), the sensitivities (63.6% for nREM3 latency, 54.5% for REM latency) of the prior parameters in determining titration failure were relatively low.

CONCLUSION

The minimum SpO₂ < 86% can be used as a readily available indicator of APAP titration failure. nREM3 and REM latencies may be regarded as supplementary indicators in detecting the patients who may need an advanced PAP device.

Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity

BACKGROUND

Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder with many pathophysiological pathways to disease. Currently, the diagnosis and classification of OSA is based on the apnea-hypopnea index, which poorly correlates to underlying pathology and clinical consequences. A large number of in-laboratory sleep studies are performed around the world every year, already collecting an enormous amount of physiological data within an individual. Clinically, we have not yet fully taken advantage of this data, but combined with existing analytical approaches, we have the potential to transform the way OSA is managed within an individual patient. Currently, respiratory signals are used to count apneas and hypopneas, but patterns such as inspiratory flow signals can be used to predict optimal OSA treatment. Electrocardiographic data can reveal arrhythmias, but patterns such as heart rate variability can also be used to detect and classify OSA. Electroencephalography is used to score sleep stages and arousals, but specific patterns such as the odds-ratio product can be used to classify how OSA patients responds differently to arousals.

OBJECTIVE

In this review, we examine these and many other existing computer-aided polysomnography signal processing algorithms and how they can reflect an individual's manifestation of OSA.

SIGNIFICANCE

Together with current technological advance, it is only a matter of time before advanced automatic signal processing and analysis is widely applied to precision medicine of OSA in the clinical setting.