Deep learning of awake and sleep electrocardiography to identify atrial fibrillation risk in sleep apnea

深度学习清醒和睡眠心电图来识别睡眠呼吸暂停中的房颤风险

• 批准号: 10579141
• 负责人: Oguz Akbilgic
• 金额: $ 10.9万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10579141
• 关键词: Ablation; Academic Medical Centers; Affect; Anticoagulation; Apnea; Arrhythmia; Atherosclerosis Risk in Communities; Atrial Fibrillation; Cardiac health; Cardiovascular system; Characteristics; Clinical; Clinical Data; Clinical Trials; Cohort Studies; Communities; Data; Data Set; Decision Making; Detection; Development; Elderly man; Electrocardiogram; Electrophysiology (science); Evaluation; Event; Future; General Population; Heart; Heart Atrium; Heart Rate; Hypertension; Individual; Ischemic Stroke; Lead; Link; Maps; Mechanics; Morbidity - disease rate; Multi-Ethnic Study of Atherosclerosis; Neural Network Simulation; Obesity; Obstructive Sleep Apnea; Oral; Outcome; Pathologic; Patients; Pattern; Performance; Persons; Physiological; Polysomnography; Population; Predictive Value; Predisposition; Property; Prospective cohort; Recurrence; Respiration; Risk; Risk Assessment; Risk Factors; Severities; Signal Transduction; Sleep; Sleep Apnea Syndromes; Sleep Disorders; Stretching; Testing; Therapeutic; Training; United States; Universities; Validation; Virginia; awake; cardiovascular health; cardiovascular risk factor; clinical practice; cohort; convolutional neural network; deep learning; deep learning model; design; diagnostic tool; efficacy testing; heart electrical activity; improved; improvement on sleep; indexing; learning strategy; modifiable risk; mortality; novel; novel marker; personalized approach; predictive modeling; prevent; response; risk prediction; risk prediction model; risk sharing; risk stratification; screening

Project Summary Atrial fibrillation (AF) is the most common cardiac arrhythmia responsible for significant morbidity and mortality burden. Obstructive sleep apnea (OSA) is a common sleep disorder but disproportionately more common in patients with AF. OSA has been proposed as a risk for AF. However, clarifying the association between the OSA and AF has been challenging due to many commonly shared risk factors such as obesity. No studies have demonstrated whether information about OSA improves prediction of future risk of AF. In particular, identifying who “among those with OSA” would be at risk for AF is unclear. Better identification of the group most vulnerable to developing AF among those with OSA will inform clinicians and patients of critical information needed for therapeutic decision making. One major challenge in OSA evaluation is that conventional metrics used in the evaluation, such as the apnea hypopnea index (AHI) do not adequately capture downstream cardiovascular (CV) responses. We and others have identified promising physiologically- driven polysomnography (PSG) markers that better capture the severity of OSA and improve CV risk stratification. Specifically related to AF, our preliminary study shows that heart rate response (HRR) to OSA events, but not AHI, is associated with incident AF in community dwelling elderly men. Electrocardiography (ECG) is a readily available diagnostic tool that captures electrical activity of the heart. Deep learning (DL) has shown great promise in detection and risk prediction of various clinical outcomes including AF from `awake' ECGs alone. `Sleep' ECG is affected by sleep state, respiration and particularly by pathological respiration such as OSA events. Based on this, we propose Aim 1: To evaluate whether novel HRR-based OSA metrics improves risk prediction of AF beyond the current AF risk prediction model. We will use a combined prospective cohort of Atherosclerosis Risk in Communities Study (ARIC)-Sleep Heart Health Study (SHHS), Cardiovascular Health Study (CHS)-SHHS and Multi-Ethnic Study of Atherosclerosis (MESA) (N~5000, AF events~800). Aim 2: To develop and test the DL model using an awake ECG (10 sec 12 lead) and sleep ECG (single lead) to predict a new onset AF in general population “with OSA”. We will develop a convolutional neural network (CNN) model utilizing ARIC + CHS cohorts (combined N with OSA~1500, AF events ~400) and externally validate in MESA cohort (OSA~1000, AF events ~100). The performance will be compared with the CHARGE-AF risk prediction model. Aim 3: Same as Aim 2 except it will be the DL model in prediction of new onset AF patients with OSA in clinical practice. Building upon the CNN model from Aim 2, we will develop a separate CNN model using clinical ECG data from a single academic medical center (N= 2000, AF~200) that may be more relevant in real world clinical practice. 50% of the dataset will be used for training and 50% for validation. The findings of this study will provide critical information about the future application of DL in improving CV risk stratification of people with OSA.

项目摘要 房颤(房颤)是最常见的心律失常，导致严重的发病率和死亡率。 负担。阻塞性睡眠呼吸暂停(OSA)是一种常见的睡眠障碍，但在 房颤患者。OSA已被认为是房颤的危险因素。然而，澄清两者之间的关联 由于肥胖等许多共同的风险因素，阻塞性睡眠呼吸暂停综合征和房颤一直具有挑战性。没有研究 已经证明了有关OSA的信息是否改善了对未来房颤风险的预测。特别是， 目前尚不清楚“阻塞性睡眠呼吸暂停综合征患者”中谁有患房颤的风险。更好地识别群体 OSA患者中最容易发生房颤的人将告知临床医生和患者危重 治疗决策所需的信息。OSA评估中的一个主要挑战是 评估中使用的常规指标，如呼吸暂停低通气指数(AHI)不足以 捕获下游心血管(CV)反应。我们和其他人已经发现了生理学上有希望的- 驱动多导睡眠图(PSG)标记，更好地捕捉OSA的严重程度并改善CV风险 分层。特别是与房颤有关，我们的初步研究表明，对OSA的心率反应(HRR) 社区老年男性发生房颤事件与事件相关，但与呼吸暂停无关。心电图学 心电图是一种现成的诊断工具，可以捕捉心脏的电活动。深度学习(DL)具有 在各种临床结果的检测和风险预测方面显示出巨大的希望，其中包括来自“觉醒”的房颤 只有心电波。睡眠的心电图受睡眠状态、呼吸，尤其是病理性呼吸的影响 例如OSA活动。基于此，我们提出了目标1：评估新的基于HRR的OSA度量 改进了房颤风险预测，超越了目前的房颤风险预测模型。我们将使用组合的 社区动脉粥样硬化风险前瞻性队列研究(ARIC)-睡眠心脏健康研究(SHHS)， 心血管健康研究(CHS)-SHHS和动脉粥样硬化(MESA)的多民族研究(N~5000，房颤) 事件~800)。目的2：利用清醒心电(10秒12导联)和睡眠心电建立和测试动态脑电模型 (单导联)预测患有阻塞性睡眠呼吸暂停综合征(OSA)的普通人群中新发的房颤。我们将开发一种卷积 使用ARIC+CHS队列的神经网络(CNN)模型(N与OSA~1500、AF Events~400组合)和 MESA队列中的外部验证(OSA~1000，房颤事件~100)。我们将把它们的表现与 Charge-AF风险预测模型。目标3：与目标2相同，不同之处在于它将成为预测新的 临床实践中伴发阻塞性睡眠呼吸暂停的房颤患者。在Aim 2的CNN模型的基础上，我们将开发一个 使用单个学术医学中心(N=2000，AF~200)的临床心电数据建立单独的CNN模型 可能在现实世界的临床实践中更具相关性。50%的数据集将用于训练，50%用于培训 验证。这项研究的结果将为数字图书馆的未来应用提供关键信息 改善阻塞性睡眠呼吸暂停患者的心血管风险分层。