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 心房颤动（AF）是最常见的心律失常，负责明显的发病率和死亡率 负担。阻塞性睡眠呼吸暂停（OSA）是一种常见的睡眠障碍，但在 AF患者。 OSA已被提议是AF的风险。但是，确定了 OSA和AF由于许多通常共同的危险因素（例如肥胖）而受到挑战。没有研究 已经证明了有关OSA的信息是否改善了对AF的未来风险的预测。尤其， 尚不清楚确定“在OSA中”中谁有AF的风险。更好地识别小组 在患有OSA的人中，最容易受到AF的影响，将告知临床医生和患者关键 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，例如呼吸暂停指数（AHI）不充分 捕获下游心血管（CV）反应。我们和其他人在身体上确定了承诺 - 驱动的多症术​​（PSG）标记，可以更好地捕获OSA的严重性并改善简历风险 分层。与AF特别相关，我们的初步研究表明，心率反应（HRR）对OSA 事件，但没有AHI与社区中的AF事件有关。心电图 （ECG）是一种捕获心脏电活动的易用诊断工具。深度学习（DL） 在检测和风险预测各种临床结果（包括“清醒”的AF）方面表现出了巨大的希望 单独使用心电图。 “睡眠”心电图受睡眠状态，呼吸，特别是病理呼吸的影响 例如OSA事件。基于此，我们提出目标1：评估新型基于HRR的OSA指标是否 改善AF的风险预测超出当前AF风险预测模型。我们将共同使用 社区研究中动脉粥样硬化风险的前瞻性队列（ARIC） - 睡眠心脏健康研究（SHHS）， 心血管健康研究（CHS）-SHHS和动脉粥样硬化的多种族研究（MESA）（N〜5000，AF 活动〜800）。目标2：使用清醒的心电图（10秒12领先）开发和测试DL模型和睡眠ECG （单个铅）预测普通人群中的新发作AF“与OSA”。我们将建立卷积 使用ARIC + CHS队列（与OSA〜1500，AF事件〜400）和 在梅萨队列中进行外部验证（OSA〜1000，AF事件〜100）。表演将与 ACH-AF风险预测模型。 AIM 3：与AIM 2相同，除了它将是新预测的DL模型 在临床实践中患有OSA的AF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）的临床心电图数据单独的CNN模型 在现实世界临床实践中可能更重要。 50％的数据集将用于培训，50％用于 验证。这项研究的发现将提供有关DL未来应用的关键信息 改善OSA患者的简历风险分层。