Data-Driven Sleep Biomarkers of Brain Health, Heart Health, and Mortality

摘要：大脑健康、心脏健康和死亡率的数据驱动睡眠生物标记物 睡眠状态信号编码了有关大脑和心血管健康的关键生物信息。然而， 现有的多导睡眠图数据处理方法(“睡眠研究”)抛弃了大部分收集的信息 使用20世纪60年代的可视分析和规则提供相对简单的度量(例如，30秒 睡眠分期、呼吸暂停-低呼吸指数)。视觉得分也受到得分者之间不一致的限制。近期 计算科学和机器学习(ML)/人工智能(AI)的进步为1) 标准评分，具有无与伦比的精确度和一致性；2)新的数据驱动的量化衡量标准。那里 是对新工具、算法和数据集的迫切需求，这些工具、算法和数据集利用数据科学的最新进展 开发以睡眠为基础的大脑和心血管健康生物标记物。 我们建议为所有标准睡眠测量创建一个完整的AI睡眠报告(CAISR)算法，并 逐步积累新奇分析的文库。我们处于缩小这一差距的理想位置。我们会 在我们的六个合作机构之间收集来自&gt；20万患者的睡眠数据(已经收集了3.5万人)， 我们有为研究整理大量临床生理学和电子病历数据的经验；我们有 在建立可扩展的公共数据共享门户方面已经取得了进展；我们在基本 和转化性睡眠科学；我们有成功开发和验证的既定记录 用于分析睡眠数据的新型深度学习工具和算法。 我们的长期目标是通过开放分析取代人工分析来增加睡眠生理数据的价值。 源数据驱动的人工智能方法。我们的中心假设是睡眠信号携带着可测量的潜伏期 关于死亡率和大脑和心脏健康的信息。我们的具体目标是：1)创建在线公共门户 具有识别的多导睡眠图(PSG)和横断面和纵向电子健康记录(EHR) &gt；200K成人和儿童患者的数据；2)实施CAISR并验证它适用于不同年龄的患者， 性和种族。CAISR还将在公共研究队列中的13,000个PSG上进行外部验证；3)开发 人工智能算法，a)区分患有和不存在脑部和心脏疾病的患者；b)预测 所有原因和心血管死亡率的结果，以及心脏病的次要结果(冠状动脉 疾病、心肌梗死、充血性心力衰竭、房颤、高血压)；以及脑部疾病 (痴呆症、中风、颅内出血)。 完成这些目标将导致这些预期结果：(1)整个生命周期的睡眠数据，(2)睡眠评分 人工智能算法在年龄、性别和种族方面得到验证；(3)死亡率和大脑和心脏健康的预测因素。 这些结果将导致新的可检验的假设，使睡眠诊断更容易为社会和 生物服务不足的群体，并刺激数据驱动的睡眠研究进展。

Abstract: Data-Driven Sleep Biomarkers of Brain Health, Heart Health, and Mortality Sleep state signals encode critical biological information about brain and cardiovascular health. However, present approaches to polysomnography data (“sleep studies”) discard most of the collected information, instead providing, using visual analysis and rules from the 1960s, relatively unsophisticated metrics (e.g., 30-second sleep stages, apnea-hypopnea index). Visual scoring is also limited by interscorer inconsistencies. Recent advances in computational science and Machine Learning (ML) / Artificial Intelligence (AI) open the way for 1) standard scoring with unparalleled precision and consistency; 2) new data-driven, quantitative measures. There is a critical unmet need for new tools, algorithms and datasets that leverage recent advances in data science to develop robust sleep-based biomarkers of brain and cardiovascular health. We propose to create a Complete AI Sleep Report (CAISR) algorithm for all standard sleep measures, and a progressively accumulating library of novel analytics. We are ideally positioned to close this gap. We will assemble between our six collaborating institutions sleep data from >200K patients (35,000 already assembled), we have experience curating large clinical physiology and electronic medical records data for research; we have progress already underway with building a scalable public data sharing portal; we have deep expertise in basic and translational sleep science; and we have an established record of successfully developing and validating novel deep learning tools and algorithms to analyze sleep data. Our long-term goal is to increase the value of sleep physiology data by replacing manual analysis by open- source data-driven AI approaches. Our central hypothesis is that sleep signals carry measurable latent information about mortality and brain and heart health. Our specific aims are: 1) Create an online public portal with de-identified polysomnograms (PSG) and cross-sectional and longitudinal electronic health records (EHR) data for >200K adult and pediatric patients; 2) Implement CAISR and validated that it generalizes across age, sex, and race. CAISR will also be externally validated on >13,000 PSGs from public research cohorts; 3) Develop AI algorithms that a) differentiate patients with vs. without existing brain and heart disease; b) predict primary outcomes of all cause and cardiovascular mortality, and secondary outcomes of heart disease (coronary artery disease, myocardial infarction, congestive heart failure, atrial fibrillation, hypertension); and brain disease (dementia, stroke, intracranial hemorrhage). Completing these aims will lead to these expected outcomes: (1) sleep data across the lifespan, (2) sleep scoring AI algorithms validated across age, sex, and ethnicity; (3) predictors of mortality and brain and heart health. These outcomes will lead to new testable hypotheses, make sleep diagnostics more accessible to socially and biologically underserved groups, and stimulate progress in data-driven sleep research.