Applying Innovative Artificial Intelligence Approaches to a Large Sleep Physiologic Biorepository to Integrate Sleep Disruption in Cardiovascular Risk Calculation

将创新的人工智能方法应用于大型睡眠生理生物库，将睡眠中断纳入心血管风险计算

• 批准号: 10731500
• 负责人: Matheus Lima Diniz Araujo
• 金额: $ 12.08万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10731500
• 关键词: Address; American; American Heart Association; Apnea; Archives; Arousal; Arrhythmia; Artificial Intelligence; Atherosclerosis; Biological Markers; Carbon Dioxide; Cardiology; Cardiovascular Diseases; Cardiovascular system; Cessation of life; Clinic; Clinical; Clinical Trials; Clinical Trials Design; Complex; Coronary heart disease; Data; Data Set; Databases; Dimensions; Drowsiness; Electrocardiogram; Electroencephalography; Equation; Event; Functional disorder; Geography; Hypnogram; Hypoxia; Incidence; Individual; Investigation; Life; Machine Learning; Medicine; Methods; Modeling; Myocardial Infarction; National Heart, Lung, and Blood Institute; Neurologic; Obstructive Sleep Apnea; Oxygen; Pathway interactions; Pattern; Performance; Periodicals; Phenotype; Physiological; Positioning Attribute; Registries; Research; Resources; Risk; Risk Factors; Science; Signal Transduction; Sleep; Sleep Architecture; Sleep Disorders; Sleep disturbances; Snoring; Stroke; Sympathetic Nervous System; Testing; Time; Training; United States; Validation; Work; biobank; cardiovascular disorder risk; cardiovascular risk factor; clinically relevant; cohort; college; computer science; cost; deep learning; deep learning model; design; epidemiologic data; experience; indexing; innovation; large datasets; learning strategy; limb movement; machine learning model; multimodality; risk prediction; risk prediction model; risk stratification; structured data

PROJECT SUMMARY: Cardiovascular disease (CVD) accounts for >800,000 deaths annually, i.e., 32% of all deaths in the US, with total costs projected to reach $2.5 trillion by 2035. Experimental and epidemiologic data identify sleep disorders- -recently recognized in American Heart Association Life’s Essential 8--as independent preventative targets to mitigate downstream major adverse cardiovascular events (MACE). Obstructive sleep apnea (OSA) is the sleep disorder most consistently implicated in CV risk operating via pathways of intermittent hypoxia and sympathetic nervous system activation. Emerging science, however, from our group and others, has identified that other facets of sleep disruption, such as curtailed sleep and sleep architectural disruption, also increase CV risk. Enhanced phenotyping of not only OSA--beyond the limitations of the standardly used apnea-hypopnea index (AHI) --- but also other sleep disorders could refine the ability to characterize sleep-related pathophysiology and MACE prediction. However, overlapping sleep phenotypes contributing to CV risk are difficult to characterize, given the need for large datasets. Moreover, the “sleepy” phenotype of sleep disorders is associated with increased CV risk; however, there is limited understanding of how to integrate this into CV risk prediction. Therefore, we propose leveraging an existing clinical registry of multimodal cardiorespiratory and neurologic physiologic sleep data, i.e.,>186,000 archived sleep studies. The scope of work involves conducting an analysis of biologically plausible aggregate biomarkers of CVD from datasets of polysomnograms (PSG) that combine with artificial intelligence models to identify patterns from structured data and raw PSG signal data to forecast the incidence of MACE (nonfatal myocardial infarction, fatal coronary heart disease, nonfatal, or fatal stroke) and examine the influence of the sleepy phenotype. We will further examine the utility of incorporating automatic PSG analysis in the current clinical CV risk stratification schema. This work will set the stage for external validation work in other clinical cohorts and the NHLBI National Sleep Research Resource, a pooled geographically diverse compilation of >45,000 sleep studies. The proposed work provides an innovative opportunity to assess the ability of sleep study, i.e., PSG biomarkers, to predict individuals at increased risk for CVD using methods established by our group. Innovation also lies in the use of state-of-the-art deep learning strategies, including Transformers models for low-dimensional representation of PSG direct physiological signals. Our group is well-positioned to undertake the following study aims, given the expertise and experience we have in sleep medicine, cardiovascular, and computer science research.

项目总结： 心血管疾病每年造成80万人死亡，占美国所有死亡人数的32%。 预计到2035年，总成本将达到2.5万亿美元。实验和流行病学数据确定了睡眠障碍- -最近在美国心脏协会生命基本8中被确认为独立的预防目标 减轻下游主要不良心血管事件(MACE)。阻塞性睡眠呼吸暂停(OSA)是指睡眠 通过间歇性缺氧和交感神经通路操作的心血管风险最一致的疾病 神经系统激活。然而，来自我们小组和其他人的新兴科学已经确定了另一个 睡眠障碍的方方面面，如睡眠减少和睡眠结构紊乱，也会增加简历风险。 不仅是OSA的增强表型--超越标准使用的呼吸暂停-低呼吸指数的限制 (AHI)-但其他睡眠障碍也可能提高表征睡眠相关病理生理学和 梅斯预测。然而，导致心血管风险的重叠睡眠表型很难表征， 考虑到对大型数据集的需求。此外，睡眠障碍的“昏昏欲睡”表型与 增加了简历风险；然而，对于如何将这一点整合到CV风险预测中，人们的理解有限。 因此，我们建议利用现有的多模式心肺和神经病学的临床登记 生理睡眠数据，即&gt；186,000个已存档的睡眠研究。工作范围包括进行分析 从多导睡眠图(PSG)数据集结合 使用人工智能模型从结构化数据和原始PSG信号数据中识别模式以进行预测 MACE(非致命性心肌梗死、致命性冠心病、非致命性或致命性中风)和 检查嗜睡表型的影响。我们将进一步研究合并Automatic PSG分析在当前临床心血管危险分层方案中的应用。这项工作将为外部 在其他临床队列和NHLBI国家睡眠研究资源中的验证工作，汇集 对45,000项睡眠研究进行了不同地域的汇编。建议的工作提供了一种创新的 有机会评估睡眠研究的能力，即PSG生物标志物，以预测患高血压风险增加的个人 CVD采用本课题组建立的方法。创新还在于使用最先进的深度学习 策略，包括用于PSG直接生理的低维表示的Transformers模型 信号。鉴于我们的专业知识和经验，我们的团队有能力承担以下研究目标 我们有睡眠医学、心血管和计算机科学研究。