Eavesdropping on heart-brain conversations during sleep for early detection and prevention of fatal cardiovascular disease

在睡眠期间窃听心脑对话，以及早发现和预防致命的心血管疾病

• 批准号: 10002962
• 负责人: Deeptankar DeMazumder
• 金额: $ 18.58万
• 依托单位: UNIVERSITY OF CINCINNATI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-19 至 2022-07-27
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10002962
• 关键词: Address; Animal Model; Animals; Apnea; Arousal; Artificial Intelligence; Award; Brain; Cardiac Electrophysiologic Techniques; Cardiovascular Diseases; Cardiovascular Pathology; Cardiovascular system; Cause of Death; Cessation of life; Clinical; Clinical Research; Clinics and Hospitals; Conscious; Critical Care; Data; Defibrillators; Disease; Early Diagnosis; Early treatment; Electrocardiogram; Electroencephalogram; Engineering; Health; Heart; Heart Arrest; Heart Diseases; Heart failure; Hour; Human; Incidence; Individual; Industrialization; Interdisciplinary Study; Life; Link; Mathematics; Medicine; Methods; Molecular; Monitor; Multivariate Analysis; Patients; Personal Satisfaction; Prevention; Research; Risk; Risk Factors; Risk stratification; Signal Transduction; Sleep; Sleep Apnea Syndromes; Technology; Time; United States National Institutes of Health; Unnecessary Procedures; body system; cardiovascular risk factor; clinical practice; dynamic system; early onset; high risk; improved; innovation; insight; novel; novel strategies; personalized strategies; prevent; prognostic; socioeconomics; statistics; success; tool

ABSTRACT The intimate link between the heart, brain and sleep is central to our well-being and ability to meet the demands of life. A majority of cardiovascular (CV) deaths occur in the early waking hours from sleep. For example, sudden cardiac arrest (SCA), an extremely prevalent and devastating condition, claims more lives (>350,000/year) in the USA than all disease-related causes of death combined. A defibrillator can prevent SCA, but current clinical strategies are grossly inadequate, both in terms of identifying people at risk and importantly, monitoring and controlling the CV risk. To address this major gap, we are proposing an entirely novel approach for studying heart-brain interactions during sleep. To our knowledge, our compelling new preliminary data and innovative strategy are unprecedented. In robust preliminary studies of animals and humans, we have identified unique signatures of evolving, potentially fatal, CV disease within electrocardiogram (ECG) and electroencephalogram (EEG) waveforms that otherwise cannot be detected by current clinical methods and conventional statistics. Our powerful new tools reveal these “hidden” signatures during sleep (i.e., as conscious activity decreases and autonomic control of the heart becomes prominent). This missing link we have identified between the heart, brain, spontaneous intrinsic arousals, and critical CV disease is independent (in multivariate analyses) of sleep disordered breathing (e.g., apnea) and established risk factors. Our novel and highly promising findings may account for the high incidence of CV deaths associated with sleep and have potential for broad application, ranging from animal models to improved reclassification of individuals currently consid- ered “low”, “moderate” or “high” risk in contemporary clinical practice. This is important because asymptomatic individ- uals without advanced CV disease comprise the majority of SCA victims. They also are the ones “missed” by current risk stratification methods and the most challenging to identify. Further, our fundamental new approach to EEG and ECG analysis will add new, clinically valuable, prognostic insight for patients with advanced CV disease (e.g., heart failure). This robust, inexpensive, personalized strategy for identifying who will and will not need lifesaving therapy will also avoid unnecessary procedures and complications, and thus, will provide substantial socioeconomic benefits. This paradigm-shifting application for a NIH Director’s New Innovator Award incorporates clinical cardiac electro- physiology, critical care and sleep medicine with engineering, mathematics, artificial intelligence, statistical dynamical systems, and molecular, cellular and clinical research to enable early diagnosis and therapy of critical CV disease. Our unique approach for gaining novel mechanistic insight into CV pathology and risk during sleep is likely to spawn new avenues in collaborative multidisciplinary research. Because our new paradigm can be seamlessly incorporated into existing technology readily available in hospitals and clinics, we expect our findings to rapidly transform contem- porary clinical practice in multiple fields. Importantly, the ability to identify and decode “hidden” EEG and ECG signa- tures of early onset of fatal, subclinical CV illness, including SCA, the leading cause of death in the industrialized world, has extraordinary implications for human health and the potential for broad worldwide application.

抽象的 心脏、大脑和睡眠之间的密切联系对于我们的健康和满足需求的能力至关重要 大多数心血管 (CV) 死亡发生在睡眠初期。 心脏骤停 (SCA) 是一种极其普遍且具有破坏性的疾病，在世界各地夺走了更多人的生命（>350,000 人/年） 美国除颤器可以预防 SCA，但目前的临床策略是所有疾病相关死亡原因的总和。 无论是在识别高危人群方面还是在监控和控制简历方面都严重不足 为了解决这一重大差距，我们提出了一种全新的方法来研究心脑相互作用。 据我们所知，我们令人信服的新初步数据和创新策略是前所未有的。 在对动物和人类进行的强有力的初步研究中，我们已经确定了进化的独特特征，潜在的 心电图 (ECG) 和脑电图 (EEG) 波形中的致命性心血管疾病，否则无法 通过当前的临床方法和传统统计数据可以检测到，我们强大的新工具揭示了这些“隐藏的”。 睡眠期间的特征（即，随着意识活动减少并且心脏的自主控制变得突出）。 我们已经在心脏、大脑、自发性内在唤醒和严重心血管疾病之间发现了这一缺失的环节 与睡眠呼吸障碍（例如呼吸暂停）和已确定的危险因素无关（在多变量分析中）。 新颖且非常有希望的发现可能解释了与睡眠相关的心血管死亡的高发生率，并已 广泛应用的潜力，从动物模型到目前考虑的个体的改进重新分类 在当代临床实践中，将无症状个体分为“低”、“中”或“高”风险，这一点很重要。 没有晚期心血管疾病的人占 SCA 受害者的大多数，他们也是当前“错过”的人。 风险分层方法和最具挑战性的识别此外，我们的脑电图和基本新方法。 心电图分析将为患有晚期心血管疾病（例如心脏病）的患者增加新的、有临床价值的预后见解。 这种稳健、廉价、个性化的策略可用于确定谁需要或不需要挽救生命的治疗。 还将避免不必要的手术和并发症，从而带来巨大的社会经济效益。 NIH 主任新创新者奖的这一范式转变应用结合了临床心脏电 生理学、重症监护和睡眠医学与工程学、数学、人工智能、统计动力学 系统以及分子、细胞和临床研究，以实现危重心血管疾病的早期诊断和治疗。 我们可能会产生对心血管病理学和睡眠期间风险进行新颖机制洞察的独特方法 因为我们的新范式可以无缝整合。 融入医院和诊所现有的技术中，我们希望我们的发现能够迅速改变人们的观念 重要的是，识别和解码“隐藏”脑电图和心电图信号的能力。 致命的亚临床心血管疾病的早期发作，包括 SCA，它是工业化国家的首要死因 世界，对人类健康具有非凡的影响，并具有在全球范围内广泛应用的潜力。