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.

项目概要 心房颤动 (AF) 是最常见的心律失常，导致较高的发病率和死亡率 阻塞性睡眠呼吸暂停 (OSA) 是一种常见的睡眠障碍，但在以下人群中更为常见。 患有 AF 的患者已被认为是 AF 的风险之一，但需要澄清两者之间的关联。 由于肥胖等许多共同的危险因素，OSA 和 AF 一直具有挑战性。尚无研究。 已经证明有关 OSA 的信息是否可以改善对未来 AF 风险的预测。 确定“患有 OSA 的人”中哪些人有房颤风险目前尚不清楚。 OSA 患者中最容易发生 AF 的患者将告知公众和患者危急情况 OSA 评估所需的信息之一是。 评估中使用的传统指标，例如呼吸暂停低通气指数（AHI）并不能充分 我们和其他人已经确定了生理学上的下游心血管（CV）反应。 驱动的多导睡眠图 (PSG) 标记物可以更好地捕捉 OSA 的严重程度并改善心血管风险 特别是与 AF 相关的分层，我们的初步研究表明心率对 OSA 的反应 (HRR) 事件（而非 AHI）与社区老年男性的 AF 事件相关。 (ECG) 是一种易于使用的诊断工具，可捕获心脏的电活动。深度学习 (DL) 已实现。 在包括“清醒”状态下的房颤在内的各种临床结果的检测和风险预测方面显示出巨大的前景 单独的“睡眠”心电图受睡眠状态、呼吸，尤其是病理性呼吸的影响。 基于此，我们提出目标 1：评估是否存在新颖的基于 HRR 的 OSA 指标。 我们将使用组合的 AF 风险预测模型来改进 AF 风险预测。 社区动脉粥样硬化风险研究 (ARIC)-睡眠心脏健康研究 (SHHS) 的前瞻性队列， 心血管健康研究（CHS）-SHHS和动脉粥样硬化多种族研究（MESA）（N~5000，AF 事件~800） 目标 2：使用清醒心电图（10 秒 12 导联）和睡眠心电图开发和测试 DL 模型。 （单导联）来预测一般人群中“患有 OSA”的新发 AF。我们将开发一种卷积。 利用 ARIC + CHS 队列的神经网络 (CNN) 模型（N 与 OSA~1500、AF 事件~400 组合）以及 在 MESA 队列中进行外部验证（OSA~1000，AF 事件~100）。性能将与 CHARGE-AF 风险预测模型：与目标 2 相同，但它将是新预测中的 DL 模型。 基于 Aim 2 的 CNN 模型，我们将开发一个临床实践中患有 OSA 的 AF 患者。 使用来自单个学术医疗中心（N = 2000，AF~200）的临床心电图数据分离 CNN 模型 可能与现实世界的临床实践更相关，50% 的数据集将用于训练，50% 用于训练。 这项研究的结果将为 DL 的未来应用提供重要信息。 改善 OSA 患者的 CV 风险分层。