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: 10684096
• 负责人: Michael Brandon Westover
• 金额: $ 218.87万
• 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/10684096
• 关键词: Address; Adult; Age; Algorithms; Apnea; Arousal; Artificial Intelligence; Atrial Fibrillation; Biological; Biological Markers; Brain; Brain Diseases; Cardiac health; Cardiopulmonary; Cardiovascular system; Childhood; Clinical; Clinical Data; Code; Collaborations; Complement; Computational Science; Computerized Medical Record; Congestive Heart Failure; Coronary Arteriosclerosis; Coupling; Data; Data Science; Data Set; Dementia; Detection; Development; Diagnosis; Diagnostic; Electroencephalography; Electronic Health Record; Ensure; Epidemiology; Ethnic Origin; Ethnic Population; Future; Goals; Graph; Health; Heart Diseases; Human; Hypertension; Hypoxia; Improve Access; Institution; Intracranial Hemorrhages; Ischemic Stroke; Libraries; Link; Longevity; Machine Learning; Manuals; Measurable; Measures; Medical; Medicine; Morbidity - disease rate; Motor; Myocardial Infarction; Outcome; Patients; Performance; Periodicals; Persons; Phenotype; Physiology; Polysomnography; Positioning Attribute; Publishing; REM Sleep; Race; Reporting; Research; Sampling; Science; Signal Transduction; Sleep; Sleep Apnea Syndromes; Sleep Fragmentations; Sleep Stages; Source; Staging; Stroke; Time; Training; Underserved Population; Visual; Work; artificial intelligence algorithm; brain health; cardiovascular health; cohort; data portal; deep learning; diverse data; experience; indexing; limb movement; machine learning method; mortality; novel; open data; open source; pediatric patients; population based; primary outcome; racial population; respiratory; secondary outcome; sex; sleep physiology; social; tool

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.

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。我们将 在我们的六个合作机构之间收集来自> 20万患者的睡眠数据（已经收集了35，000）， 我们有经验策划大型临床生理学和电子病历数据的研究;我们有 在建立可扩展的公共数据共享门户方面已经取得进展;我们在基础数据共享方面拥有深厚的专业知识。 我们有一个成功的开发和验证的记录， 新的深度学习工具和算法来分析睡眠数据。 我们的长期目标是增加睡眠生理数据的价值，通过开放式分析取代人工分析， 源数据驱动的AI方法。我们的中心假设是睡眠信号携带着可测量的潜在的 关于死亡率以及大脑和心脏健康的信息。我们的具体目标是：1）创建一个在线公共门户网站 去识别多导睡眠图（PSG）和横截面和纵向电子健康记录（EHR） > 20万成人和儿童患者的数据; 2）实施CAISR并验证其可推广到不同年龄， 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）预测原发性 全因和心血管死亡率的结局，以及心脏病（冠状动脉）的次要结局 疾病、心肌梗塞、充血性心力衰竭、心房纤颤、高血压）;和 （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。