Detecting sleep apnea in adults with Down syndrome using WatchPAT: A feasibility study

Down Syndrome-Basque Alzheimer Initiative (DS-BAI): Clinic-Biological Cohort

BACKGROUND

Down syndrome (DS) is the most common genetically determined intellectual disability. In recent decades, it has experienced an exponential increase in life expectancy, leading to a rise in age-related diseases, including Alzheimer's disease (AD). Specific health plans for the comprehensive care of the DS community are an unmet need, which is crucial for the early and accurate diagnosis of main medical comorbidities. We present the protocol of a newly created clinical and research cohort and its feasibility in real life.

METHODS

The Down Syndrome-Basque Alzheimer Initiative (DS-BAI) is a population-based, inclusive, multidisciplinary initiative for the clinical-assistance and clinical-biological research approach to aging in DS led by the CITA-Alzheimer Foundation (Donostia, Basque Country). It aims to achieve the following: (1) provide comprehensive care for adults with DS, (2) optimize access to rigorous and quality training for socio-family and healthcare references, and (3) create a valuable multimodal clinical-biological research platform.

RESULTS

During the first year, 114 adults with DS joined the initiative, with 36% of them showing symptoms indicative of AD. Furthermore, adherence to training programs for healthcare professionals and families has been high, and the willingness to collaborate in basic and translational research has been encouraging.

CONCLUSION

Specific health plans for DS and conducting clinical and translational research on the challenges of aging, including AD, are necessary and feasible.

Advancements in Home-Based Devices for Detecting Obstructive Sleep Apnea: A Comprehensive Study

Obstructive Sleep Apnea (OSA) is a respiratory disorder characterized by frequent breathing pauses during sleep. The apnea-hypopnea index is a measure used to assess the severity of sleep apnea and the hourly rate of respiratory events. Despite numerous commercial devices available for apnea diagnosis and early detection, accessibility remains challenging for the general population, leading to lengthy wait times in sleep clinics. Consequently, research on monitoring and predicting OSA has surged. This comprehensive paper reviews devices, emphasizing distinctions among representative apnea devices and technologies for home detection of OSA. The collected articles are analyzed to present a clear discussion. Each article is evaluated according to diagnostic elements, the implemented automation level, and the derived level of evidence and quality rating. The findings indicate that the critical variables for monitoring sleep behavior include oxygen saturation (oximetry), body position, respiratory effort, and respiratory flow. Also, the prevalent trend is the development of level IV devices, measuring one or two signals and supported by prediction software. Noteworthy methods showcasing optimal results involve neural networks, deep learning, and regression modeling, achieving an accuracy of approximately 99%.

Validation of a predictive model for obstructive sleep apnea in people with Down syndrome

Detecting obstructive sleep apnea (OSA) is important to both prevent significant comorbidities in people with Down syndrome (DS) and untangle contributions to other behavioral and mental health diagnoses. However, laboratory-based polysomnograms are often poorly tolerated, unavailable, or not covered by health insurance for this population. In previous work, our team developed a prediction model that seemed to hold promise in identifying which people with DS might not have significant apnea and, consequently, might be able to forgo a diagnostic polysomnogram. In this study, we sought to validate these findings in a novel set of participants with DS. We recruited an additional 64 participants with DS, ages 3-35 years. Caregivers completed the same validated questionnaires, and our study team collected vital signs, physical exam findings, and medical histories that were previously shown to be predictive. Patients then had a laboratory-based polysomnogram. The best modeling had a validated negative predictive value of 50% for an apnea-hypopnea index (AHI) > 1/hTST and 73.7% for AHI >5/hTST. The positive predictive values were 60% and 39.1%, respectively. As such, a clinically reliable screening tool for OSA in people with DS was not achieved. Patients with DS should continue to be monitored for OSA according to current healthcare guidelines.