Revolutionizing Central Apnea Detection with Wesper

Central sleep apnea (CSA) is a sleep disorder characterized by intermittent pauses in breathing due to a failure of the brain's respiratory control centers. Unlike obstructive sleep apnea (OSA), where the pauses are caused by physical blockages in the airway, CSA arises from disruptions in the neural signals that regulate breathing. Although CSA is less prevalent than OSA, it can significantly impair quality of life if not properly managed. Compounding this issue, CSA can sometimes develop or worsen as a side effect of treatments for OSA, particularly those involving positive airway pressure (PAP) therapy. Consequently, precise detection and differentiation of CSA during both diagnosis and treatment phases are crucial for effective patient care.

Home sleep tests (HSTs) have become a widely used method for diagnosing sleep apnea, offering a convenient and cost-effective alternative to traditional in-lab polysomnography (PSG). As of 2022, it is estimated that 40-60% of all sleep studies in the United States are conducted using HSTs. However, while HSTs are adept at detecting OSA, they often lack the sensitivity required to reliably identify CSA. Wesper, an FDA-cleared type 3 HST, presents a promising solution with its two biosensor patches that measure direct respiratory signals and a pulse oximeter capable of distinguishing between obstructive and central apneas. This clinical validation study aims to assess the performance of Wesper’s auto-scoring algorithm in reliably detecting CSA, thus providing a reliable tool for comprehensive sleep apnea evaluation.

 
Introduction
Central sleep apnea (CSA) is characterized by pauses in breathing during sleep due to the brain failing to appropriately control respiratory drive. Unlike obstructive sleep apnea, where airway blockages cause breathing pauses, CSA stems from issues with the respiratory control center in the brain. 

While CSA is less common than obstructive sleep apnea (OSA), it still significantly impacts one's quality of life if left untreated. Further, treatment-emergent CSA commonly develops or worsens as a result of certain treatments for OSA, particularly positive airway pressure (PAP) therapy. Thus, reliable detection of CSA during diagnosis and treatment is essential for patient success.

Home sleep tests (HSTs) have become a prevalent method for diagnosing sleep apnea, offering a convenient and cost-effective alternative to traditional in-lab polysomnography. As of 2022, it is estimated that approximately 40-60% of all sleep studies are conducted using home sleep tests, translating to hundreds of thousands of tests annually in the United States alone. While HSTs are primarily designed for OSA detection, most are not reliable for diagnosing CSA because they are not sensitive enough to detect breathing patterns indicative of CSA.

Wesper is an FDA-cleared type 3 HST comprised of two biosensor patches that adhere to the abdomen and thorax, that detect direct respiratory signals such as respiratory effort at high sensitivity. When paired with a pulse oximeter, Wesper is capable of distinguishing between obstructive and central breathing events, making Wesper a highly effective option for testing and monitoring CSA (Figure 1). To assess the performance of Wesper’s auto-scoring algorithm in assessing CSA, the following clinical validation study was performed.

Figure 1: Wesper CSA detection. Abdominal and thoracic effort, derived airflow and pressure, and SpO2 during a run of automatically-detected central apneas. The central apnea events are marked in pink. Automatically detected blood oxygen desaturation are marked in orange. The patient is in the supine position.

Methods
Adult patients (n=53) from three sleep clinics across the United States were scheduled for a PSG evaluation for OSA and were recruited to wear two Wesper patches and a fingertip pulse oximeter in addition to the PSG equipment (Figure 1). The Wesper patches measured respiratory effort, derived airflow and nasal pressure, body position, and movement for the duration of the PSG study. The pulse oximeter measured blood oxygen saturation and heart rate.

PSG studies were scored blindly by an outside Registered Polysomnographic Technologist who was not involved in data collection and was asked to distinguish between obstructive, central and mixed apneas. Studies were scored in a randomized order. Separately, Wesper Lab's automatic scoring algorithms analyzed the same sleep periods for CSA. For each study, PSGs apneas scored as CSA dominant using both manual PSG scoring and the automatic Wesper scoring algorithm were compared. A “CSA dominant” apnea refers to at least 50% of the detected apneas being scored as central versus obstructive.

Studies with fewer than 10 detected apneas were not assessed as the central dominance determination for these studies is very sensitive to small changes in apnea classification, which could result in misleading results. For this purpose, an apnea manually scored as a mixed apnea on PSG signals was included in the central apnea count. The agreement was assessed using manual PSG scoring as the gold standard.

Results
In total, 46 studies from 46 unique participants were manually scored. N=19 studies from 19 unique participants had at least 10 apneas manually scored and were further analyzed. N=12 participants (63%) were male, ages ranged from 30 to 76 years, BMI ranged from 23.6 to 61 kg/m2. Skin tone information was collected using the Fitzpatrick Scale. Of the 12 participants, 10 (83%) had dark skin (Fitzpatrick IV, V or VI).
Agreement of detection of CSA dominance derived manually from PSG signals and automatically from Wesper signals resulted in a sensitivity of 100%, and a positive predictive value (PPV) of 83%.

Summary
All patients independently identified by a manual scorer as having central sleep apnea dominance were correctly identified by the Wesper CSA automated scoring algorithm, with very few false identifications. This analysis demonstrates that the Wesper CSA scoring algorithm provides a clinically reliable method for the detection of CSA as a part of sleep testing for patients with SDB. In addition, the use of this very sensitive algorithm should help to reduce human error and improve clinical efficiencies related to sleep testing.