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.

摘要：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 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， 目前的多导睡眠图数据方法（“睡眠研究”）丢弃了大部分收集到的信息，而是 使用 20 世纪 60 年代的视觉分析和规则提供相对简单的指标（例如 30 秒 睡眠阶段、呼吸暂停-呼吸不足指数）。视觉评分也受到评分者之间不一致的限制。最近的 计算科学和机器学习 (ML)/人工智能 (AI) 的进步为 1) 开辟了道路 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 对新工具、算法和数据集的关键未满足需求，这些工具、算法和数据集利用数据科学的最新进展来 开发基于睡眠的大脑和心血管健康的强大生物标志物。 我们建议为所有标准睡眠测量创建一个完整的人工智能睡眠报告（CAISR）算法，以及一个 不断积累的新颖分析库。我们处于缩小这一差距的理想位置。我们将 我们的六个合作机构收集了超过 20 万患者的睡眠数据（已收集了 35,000 名患者）， 我们拥有整理大型临床生理学和电子病历数据以供研究的经验；我们有 构建可扩展的公共数据共享门户已经取得进展；我们在基础领域拥有深厚的专业知识 和转化睡眠科学；我们拥有成功开发和验证的既定记录 用于分析睡眠数据的新颖深度学习工具和算法。 我们的长期目标是通过开放式分析代替手动分析来提高睡眠生理学数据的价值。 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可测量的潜在信号 有关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 具有去识别化的多导睡眠图 (PSG) 以及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 并验证其适用于各个年龄段， 性别和种族。 CAISR 还将在来自公共研究队列的超过 13,000 个 PSG 上进行外部验证； 3）开发 人工智能算法 a) 区分患有和不患有脑部和心脏病的患者； b) 预测初级 全因死亡率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、心肌梗塞、充血性心力衰竭、心房颤动、高血压）；和脑部疾病 （痴呆、中风、颅内出血）。 完成这些目标将带来以下预期结果：(1) 整个生命周期的睡眠数据，(2) 睡眠评分 人工智能算法经过年龄、性别和种族验证； (3) 死亡率以及大脑和心脏健康的预测因素。 这些结果将带来新的可检验假设，使睡眠诊断更容易被社会和公众所接受。 生物学服务不足的群体，并刺激数据驱动的睡眠研究的进展。