Clinical validation of at-home methods of sleep assessment in Rett Syndrome

项目摘要 Rett综合征（RTT）是一种罕见的X连锁神经遗传性疾病，每10，000名女性中就有1名受到影响，由以下原因引起： 甲基CpG结合蛋白2基因（MECP 2）突变。RTT的经典或典型形式是 其特征是一段时期的发展倒退，丧失有目的的手部使用，丧失口语， 步态异常和重复的、刻板的手部动作。RTT表型中还包括睡眠 问题以及各种暗示自主神经失调的症状，包括呼吸 不规则性（例如换气过度、呼吸暂停、屏气）、心率变异性、体温失调。 睡眠中断对RTT儿童及其父母/照顾者的生活质量产生不利影响。临床 RTT的试验正在进行中，其中一些试验包括基于睡眠的结果测量，包括护理人员报告 问卷调查和睡眠日记。虽然这些策略被广泛用于研究睡眠，但它们是受限制的。 多种形式的偏见，包括回忆和观察者偏见。目的，照顾者独立的方法， 因此需要评估睡眠质量。客观评估睡眠的金标准是 多导睡眠图（PSG），但这是时间密集型，成本高，不切实际的纵向评估 这是RTT临床试验的必要组成部分。可穿戴设备越来越多地用于睡眠研究 研究，因为他们的易用性，并能够捕获数据的延长时间内的家庭 环境虽然我们的试验数据证明了可穿戴设备在RTT中评估睡眠的可行性， RTT中这些睡眠质量测量的灵敏度或特异性是未知的， 根据PSG或临床严重程度的测量进行验证。因此，该项目将使用最先进的信号 处理和模式识别方法，以开发可量化的远程/在家睡眠测量 用于临床试验。我们将确定可穿戴设备对睡眠质量的可靠性 与黄金标准PSG的RTT相比。本研究中使用的设备还捕获自主生理 因此，我们将额外确定RTT中睡眠质量的灵敏度和特异性的变化 当将这些测量值添加到单独的体动记录仪之外时。最后，我们将确定收敛有效性 通过可穿戴设备测量的睡眠质量与目前用于临床试验的结果测量之间的差异 在RTT中。成功完成这些目标可能会对RTT产生很大影响，因为它们可以用作 用于临床严重程度的生物标志物和用于临床试验的患者分层的目的。最后这些 这些措施可以帮助制定更个性化、更有针对性的干预措施，以改善睡眠质量。 和照顾者的生活质量。

Project Summary Rett Syndrome (RTT) is a rare, X-linked neurogenetic disorder that affects 1 in 10,000 females and is caused by mutations in the methyl CpG-binding protein 2 gene (MECP2). The classic, or typical, form of RTT is characterized by a period of developmental regression, loss of purposeful hand use, loss of spoken language, gait abnormalities and repetitive, stereotyped hand movements. Also included in the RTT phenotype are sleep problems as well as a variety of symptoms suggestive of autonomic dysregulation including breathing irregularities (e.g. hyperventilation, apnea, breath holding), heart rate variability, temperature dysregulation. Disrupted sleep adversely impacts the quality of life of children with RTT and their parents/caregivers. Clinical trials are underway in RTT, and several include sleep-based outcome measures consisting of caregiver reports from questionnaires and sleep diaries. While these strategies are widely used for studying sleep, they are subject to multiple forms of bias including recall as well as observer bias. Objective, caregiver independent methods of assessing sleep quality are thus required. The gold-standard for objective assessment of sleep is polysomnography (PSG), however this is time-intensive, costly, and impractical for longitudinal assessments that are necessarily part of clinical trials in RTT. Wearable devices are increasingly utilized in sleep research studies because of their ease of use, and ability to capture data for extended periods of time within the home environment. Although our pilot data demonstrates the feasibility of a wearable device to assess sleep in RTT, the sensitivity or specificity of these measurements for sleep quality in RTT is unknown, and they have never been validated against PSG or measures of clinical severity. As such, this project will use state-of-the-art signal processing and pattern recognition methodologies to develop a quantifiable, remote/at-home measure of sleep for use within the context of a clinical trial. We will determine the reliability of a wearable device for sleep quality in RTT against gold-standard PSG. The device being utilized in this study also captures autonomic physiological measurements, so we will additionally determine the change in sensitivity and specificity for sleep quality in RTT when adding these measurements beyond actigraphy alone. Finally, we will determine the convergent validity between sleep quality measured by a wearable device and currently used outcome measures for clinical trials in RTT. Successful completion of these aims could have high impact for RTT because they could be utilized as biomarkers of clinical severity and for the purposes of patient stratification for clinical trials. Finally, these measures could assist in formulating more personalized, targeted interventions aimed at improving sleep quality and caregiver quality of life.