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学信息。然而， 当前的多个术数据数据（“睡眠研究”）丢弃了大多数收集的信息，而是 使用1960年代的视觉分析和规则提供相对不合适的指标（例如，30秒 睡眠阶段，呼吸暂停指数）。视觉评分也受到间际矛盾的限制。最近的 计算科学和机器学习（ML） /人工智能（AI）的进步为1开辟道路） 具有无与伦比的精度和一致性的标准评分； 2）新的数据驱动，定量测量。那里 是对新工具，算法和数据集的至关重要的需求 发展出强大的基于睡眠的脑和心血管健康的生物标志物。 我们建议为所有标准睡眠措施创建完整的AI睡眠报告（CAISR）算法，并 理想情况下，我们可以缩小这一差距。我们将 在我们的六个合作机构之间组装> 200k患者的睡眠数据（已经组装了35,000名）， 我们有策划大型临床生理学和电子病历数据的经验；我们有 通过建立可扩展的公共数据共享门户的进展；我们在基本方面拥有深厚的专业知识 并翻译睡眠科学；我们有成功开发和验证的既定记录 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放替换手动分析来增加睡眠生理数据的价值 源数据驱动的AI方法。我们的中心假设是睡眠信号带有可测量的潜在 有关死亡率，大脑和心脏健康的信息。我们的具体目的是：1）创建一个在线公共门户网站 具有去识别的多个多识别图（PSG）以及横截面和纵向电子健康记录（EHR） > 200k成人和儿科患者的数据； 2）实施CAISR，并证实了它通常在年龄之间 性和种族。 CAISR还将在公共研究队列的13,000 psg上进行外部验证； 3）发展 A）a）a）区分患者与没有现有大脑和心脏病的患者； b）预测主要 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）。 完成这些目标将导致这些预期的结果：（1）整个寿命的睡眠数据，（2）睡眠评分 AI算法在各个年龄段，性别和种族之间得到了验证； （3）死亡率，大脑和心脏健康的预测指标。 这些结果将导致新的可检验的假设，使睡眠诊断能够在社会上更容易获得 在生物学上服务不足的组，并刺激数据驱动的睡眠研究的进展。