Statistical uncertainty of the apnea-hypopnea index is another reason to question the utility of this metric

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.

Assessing the Physiologic Endotypes Responsible for REM- and NREM-Based OSA

BACKGROUND

Patients with OSA can have the majority of their respiratory events in rapid eye movement (REM) sleep or in non-rapid eye movement (NREM) sleep. No previous studies have linked the different physiologic conditions in REM and NREM sleep to the common polysomnographic patterns seen in everyday clinical practice, namely REM predominant OSA (REM_OSA) and NREM predominant OSA (NREM_OSA).

RESEARCH QUESTION

(1) How does OSA physiologic condition change with sleep stage in patients with NREM_OSA and REM_OSA? (2) Do patients with NREM_OSA and REM_OSA have different underlying OSA pathophysiologic conditions?

STUDY DESIGN AND METHODS

We recruited patients with three polysomnographic patterns. (1) REM_OSA: twice as many respiratory events in REM sleep, (2) NREM_OSA: twice as many events in NREM sleep, and (3) uniform OSA: equal number of events in NREM/REM sleep. We deployed a noninvasive phenotyping method to determine OSA endotype traits (Vpassive, Vactive, loop gain, arousal threshold) in NREM sleep, REM sleep, and total night sleep in each group of patients (NREM_OSA, REM_OSA, uniform OSA).

RESULTS

Patients with NREM_OSA have significantly worse ventilatory control stability in NREM sleep compared with REM sleep (loop gain, 0.546 [0.456,0.717] in NREM vs 0.365 [0.238,0.459] in REM sleep; P = .0026). Patients with REM_OSA displayed a significantly more collapsible airway (ie, lower Vpassive) in REM compared with NREM sleep (98.4 [97.3,99.2] %Veupnea in NREM vs 95.9 [86.4,98.9] %Veupnea in REM sleep; P < .0001). The major between-group difference across the whole night was a significantly higher loop gain in the NREM_OSA group (0.561 [0.429,0.675]) compared with the REM_OSA group (0.459 [0.388,0.539]; P = .0033).

INTERPRETATION

This study is the first to link long-recognized polysomnographic patterns of OSA to underlying physiologic differences. Patients with NREM_OSA have a higher loop gain in NREM sleep; patients with REM_OSA have a worsening of Vpassive in REM sleep.

Overnight airway obstruction severity prediction centered on acoustic properties of smart phone: validation with esophageal pressure

OBJECTIVE

Obstructive sleep apnea is characterized by a number of airway obstructions. Esophageal pressure manometry (EPM) based estimation of consecutive peak to trough differences (ΔPes) is the gold standard method to quantify the severity of airway obstructions. However, the procedure is rarely available in sleep laboratories due to invasive nature. There is a clinical need for a simplified, scalable technology that can quantify the severity of airway obstructions. In this paper, we address this and propose a pioneering technology, centered on sleep related respiratory sound (SRS) to predict overnight ΔPes signal.

APPROACH

We recorded streams of SRS using a bedside iPhone 7 smartphone from subjects undergoing diagnostic polysomnography (PSG) studies and EPM was performed concurrently. Overnight data was divided into epochs of 10 s duration with 50% overlap. Altogether, we extracted 42 181 such epochs from 13 subjects. Acoustic features and features from the two PSG signals serve as an input to train a machine learning algorithm to achieve mapping between non-invasive features and ΔPes values. A testing dataset of 14 171 epochs from four new subjects was used for validation.

MAIN RESULTS

The SRS based model predicted the ΔPes with a median of absolute error of 6.75 cmH2O (±0.59, r = 0.83(±0.03)). When information from the PSG were combined with the SRS, the model performance became: 6.37cmH2O (±1.02, r = 0.85(±0.04)).

SIGNIFICANCE

The smart phone based SRS alone, or in combination with routinely collected PSG signals can provide a non-invasive method to predict overnight ΔPes. The method has the potential to be automated and scaled to provide a low-cost alternative to EPM.