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.

项目概要： 心血管疾病 (CVD) 每年导致超过 80 万人死亡，即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 - - 最近被美国心脏协会生命要素 8 认可 - 作为独立的预防目标 缓解下游主要不良心血管事件（MACE）的是睡眠。 最一致地与通过间歇性缺氧和交感神经途径运作的心血管风险有关的疾病 然而，我们小组和其他人的新兴科学已经发现了其他神经系统激活。 睡眠中断的各个方面，例如睡眠减少和睡眠结构中断，也会增加心血管风险。 不仅增强了 OSA 的表型分析——超越了通常使用的呼吸暂停-呼吸不足指数的限制 (AHI) --- 但其他睡眠障碍也可以提高描述睡眠相关病理生理学特征的能力 然而，导致心血管风险的重叠睡眠表型很难表征， 此外，考虑到需要大量数据，睡眠障碍的“困倦”表型也与此相关。 心血管风险增加；然而，对于如何将其整合到心血管风险预测中，人们的了解还有限。 因此，我们建议利用现有的多模式心肺和神经系统临床登记 生理睡眠数据，即超过 186,000 项存档的睡眠研究。工作范围包括进行分析。 来自多导睡眠图 (PSG) 数据集的生物学上合理的 CVD 聚合生物标志物 使用人工智能模型识别结构化数据和原始 PSG 信号数据的模式以进行预测 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）的发生率以及 检查困倦表型的影响我们将进一步检查合并自动的效用。 当前临床 CV 风险分层方案中的 PSG 分析将为外部奠定基础。 其他临床队列和 NHLBI 国家睡眠研究资源（汇集了 拟议的工作提供了超过 45,000 项睡眠研究的创新成果。 有机会评估睡眠研究（即 PSG 生物标志物）的能力，以预测个体的风险增加 使用我们团队建立的方法进行 CVD 的创新还在于使用最先进的深度学习。 策略，包括用于 PSG 直接生理学低维表示的 Transformers 模型 鉴于专业知识和经验，我们的团队完全有能力实现以下研究目标。 我们在睡眠医学、心血管和计算机科学研究方面有研究。