Comparing in-lab full polysomnography for diagnosing sleep apnea in children to home sleep apnea tests (HSAT) with an online video attending technician

The Correlation of Computerized Scoring in Home Sleep Apnea Tests with Technician Visual Scoring for Assessing the Severity of Obstructive Sleep Apnea

Background: Obstructive sleep apnea (OSA) affects a significant proportion of the global population, with many having moderate or severe forms of the disease. Home Sleep Apnea Testing (HSAT) has become the most common method of diagnosing OSA, replacing in-lab polysomnography. Polysmith software Version 11 by Nihon Kohden allows for the automatic scoring of respiratory events. This study aimed to assess the validity of this technology. Study Objectives: The objective was to assess the accuracy of the Polysmith Software Automatic Scoring Algorithm of HSATs in comparison to that of sleep technicians. Methods: One hundred twenty HSATs were scored by both sleep technicians and Polysmith software. The measured values were the respiratory event index (REI), apneic events, and hypopneic events. Agreement between the two methods was reached by utilizing the Kruskal-Wallis test, Pearson correlation coefficient, and Bland-Altman plot, as well as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: The correlation between the REI calculated by the software and technicians proved to be strong overall (r = 0.96, p < 0.0001). The mild OSA group had a moderate correlation (r = 0.45, p = 0.0129). The primary snoring, moderate OSA, and severe OSA groups showed stronger correlations (r = 0.69, p < 0.0001; r = 0.56, p = 0.012; r = 0.71, p < 0.0001). The analysis conducted across all groups demonstrated an average sensitivity of 81%, specificity of 94%, PPV of 82%, and NPV of 94%, with an overall accuracy of 81%. When combining the moderate and severe OSA groups into a single category, the sensitivity was 90%, specificity was 100%, PPV was 100%, and NPV was 91%. Conclusions: OSA can be reliably diagnosed from HSATs with the automated Polysmith software across all OSA disease severity groups, with higher levels of accuracy in moderate/severe OSA and lower levels of accuracy in mild OSA.

Sleep Disordered Breathing in Children with Neuromuscular Disease

Sleep disordered breathing (SDB) in children with neuromuscular disease (NMD) is more prevalent compared to the general population, and often manifests as sleep-related hypoventilation, sleep-related hypoxemia, obstructive sleep apnea, central sleep apnea, and/or disordered control of breathing. Other sleep problems include, sleep fragmentation, abnormal sleep architecture, and nocturnal seizures in certain neuromuscular diseases. The manifestation of sleep disordered breathing in children depends on the extent, type, and progression of neuromuscular weakness, and in some instances, may be the first sign of a neuromuscular weakness leading to diagnosis of an NMD. In-lab diagnostic polysomnography (PSG) remains the gold standard for the diagnosis of sleep disordered breathing in children, but poses several challenges, including access to many children with neuromuscular disease who are non-ambulatory. If SDB is untreated, it can result in significant morbidity and mortality. Hence, we aimed to perform a comprehensive review of the literature of SDB in children with NMD. This review includes pathophysiological changes during sleep, clinical evaluation, diagnosis, challenges in interpreting PSG data using American Academy of Sleep (AASM) diagnostic criteria, management of SDB, and suggests areas for future research.

Environmental Benefits of Sleep Apnoea Detection in the Home Environment

Sleep Apnoea (SA) is a common chronic illness that affects nearly 1 billion people around the world, and the number of patients is rising. SA causes a wide range of psychological and physiological ailments that have detrimental effects on a patient’s wellbeing. The high prevalence and negative health effects make SA a public health problem. Whilst the current gold standard diagnostic procedure, polysomnography (PSG), is reliable, it is resource-expensive and can have a negative impact on sleep quality, as well as the environment. With this study, we focus on the environmental impact that arises from resource utilisation during SA detection, and we propose remote monitoring (RM) as a potential solution that can improve the resource efficiency and reduce travel. By reusing infrastructure technology, such as mobile communication, cloud computing, and artificial intelligence (AI), RM establishes SA detection and diagnosis support services in the home environment. However, there are considerable barriers to a widespread adoption of this technology. To gain a better understanding of the available technology and its associated strength, as well as weaknesses, we reviewed scientific papers that used various strategies for RM-based SA detection. Our review focused on 113 studies that were conducted between 2018 and 2022 and that were listed in Google Scholar. We found that just over 50% of the proposed RM systems incorporated real time signal processing and around 20% of the studies did not report on this important aspect. From an environmental perspective, this is a significant shortcoming, because 30% of the studies were based on measurement devices that must travel whenever the internal buffer is full. The environmental impact of that travel might constitute an additional need for changing from offline to online SA detection in the home environment.