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

数据驱动的大脑健康、心脏健康和死亡率的睡眠生物标志物

• 批准号: 10758996
• 负责人: Michael Brandon Westover
• 金额: $ 191.83万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10758996
• 关键词: Data; Driven; Sleep; Biomarkers; Brain

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 have access between our four collaborating institutions to sleep data from >80K patients (35,000 already assembled), and at least >20K more during the project; experience curating large clinical physiology and electronic medical records data for research; progress already underway with building a scalable public data sharing portal; expertise in basic and translational sleep science; and 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 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 100K adult and pediatric patients; 2) Implement CAISR and validated that it generalizes across age, gender, 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; 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, gender, 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.

翻译后摘要：脑健康，心脏健康和死亡率的数据驱动的睡眠生物标志物 睡眠状态信号编码有关大脑和心血管健康的关键生物信息。然而，在这方面， 目前的多导睡眠图数据方法（“睡眠研究”）丢弃了大部分收集的信息，而不是 使用20世纪60年代的视觉分析和规则，提供相对简单的度量（例如，30秒 睡眠阶段、呼吸暂停低通气指数）。视觉评分也受到评分者之间不一致的限制。最近 计算科学和机器学习（ML）/人工智能（AI）的进步为1） 具有无与伦比的精确度和一致性的标准评分; 2）新的数据驱动的定量措施。那里 是对新工具、算法和数据集的一个关键的未满足的需求，这些工具、算法和数据集利用数据科学的最新进展， 开发基于睡眠的大脑和心血管健康生物标志物。 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， 不断积累的新分析库。我们处于缩小这一差距的理想位置。我们有权 我们的四个合作机构之间的睡眠数据从> 80 K患者（35，000已经组装），并在 在项目期间至少> 20 K以上;有管理大型临床生理学和电子病历的经验 研究数据;在建立可扩展的公共数据共享门户方面已经取得进展; 基础和转化睡眠科学;以及成功开发和验证新颖的 深度学习工具和算法来分析睡眠数据。 我们的长期目标是通过开源数据取代人工分析来增加睡眠数据的价值- 驱动AI的方法。我们的中心假设是，睡眠信号携带可测量的潜在信息， 死亡率以及大脑和心脏健康。我们的具体目标是：1）创建一个在线公共门户网站， 多导睡眠图（PSG）以及10万成人的横截面和纵向电子健康记录（EHR）数据 2）实施CAISR，并验证其适用于年龄、性别和种族。 CAISR还将在来自公共研究队列的> 13，000个PSG上进行外部验证; 3）开发AI算法 a）区分患有与不患有脑和心脏疾病的患者; B）预测所有患者的主要结局 心血管疾病的次要结局（冠状动脉疾病、心肌梗死、心肌梗死） 梗塞，充血性心力衰竭，心房纤维性颤动，高血压）;和脑疾病（痴呆，中风， 颅内出血）。 完成这些目标将导致这些预期的结果：（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、性别和种族上得到验证;（3）死亡率、大脑和心脏健康的预测因素。 这些结果将导致新的可测试的假设，使睡眠诊断更容易获得社会和 生物学上服务不足的群体，并刺激数据驱动的睡眠研究的进展。