Predicting Short- and Long-term Future Occurrence of Atrial Fibrillation from Single-Lead ECG in Normal Sinus Rhythm with an Explainable Deep Learning Model.

使用可解释的深度学习模型，根据正常窦性心律的单导联心电图预测未来短期和长期房颤的发生。

• 批准号: 10441204
• 负责人: Giorgio Quer
• 金额: $ 22.19万
• 依托单位: SCRIPPS RESEARCH INSTITUTE, THE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10441204
• 关键词: Address; Algorithms; Anticoagulants; Atrial Fibrillation; Clinical; Clinical Data; Clinical Trials; Collaborations; Data; Data Science; Data Set; Devices; Diagnosis; Diagnostic; EKG P Wave; Early Diagnosis; Early identification; Elderly; Electrocardiogram; Event; Future; Goals; Health; Health Technology; Home; Hour; Individual; Ischemic Stroke; Lead; Learning; Left; Measures; Methods; Monitor; Outcome; Participant; Persons; Photoplethysmography; Play; Population; Preventive therapy; Probability; Prospective Studies; Research Personnel; Retrospective Studies; Risk; Role; Series; Signal Transduction; Sinus; Stroke; Techniques; Technology; Time; Validation; Work; base; clinically relevant; cohort; comorbidity; cost; deep learning; deep learning model; design; diagnostic accuracy; digital health; follow-up; high risk; improved; learning strategy; mortality; novel; novel strategies; prospective; screening; smart watch; stroke risk; tool; uptake; wearable sensor technology

Project Summary/Abstract More than 30 million individuals worldwide are diagnosed with atrial fibrillation (AF), however, another 13% of individuals with AF are left undiagnosed. People with AF have a five-fold increased risk of stroke with up to one-third of all strokes shown to be related to AF. Timely administration of appropriate preventative therapies, especially anticoagulants, can significantly decrease the complications of AF, including strokes, by 65% and mortality by 30%. Digital health technologies offer new approaches to identify individuals with undiagnosed AF, in particular paroxysmal AF (PAF), characterized by occasional episodes of limited duration, for whom a 10-second 12-lead electrocardiography (ECG) performed in the clinical setting is unlikely to overlap with an AF event. Continuous monitoring is promising, but still costly and burdensome for elderly individuals, who are at higher risk. To maximize the diagnostic yield of these technologies, we propose novel methods to predict the future occurrence of AF from a single-lead ECG during normal sinus rhythm. Only recently it was shown that it is possible to predict the future occurrence of AF from 12-lead ECGs in normal sinus rhythm collected in a clinical setting. Here, we propose to predict the occurrence of AF with commercially available single- lead ECG devices, which will enable a scalable alternative for early detection in a non-clinical setting. To achieve this goal, we will analyze retrospectively the raw single-lead ECG data of 10,000+ individuals with PAF over 14 days of monitoring. Validation work will then be carried out in a unique set of 1,718 asymptomatic individuals who participated in the prospective mSToPS clinical trial of AF screening (mean age 73), with full clinical information and co-morbidities. The three aims of this project are: 1. Compute the probability of a future AF event in the short-term for an individual in normal sinus rhythm using classic single-lead ECG features and representation learning based features. 2. Develop a method for long-term prediction of AF onset by evaluating individuals with AF detected in 1, 3, 6 and 12 months from the initial monitored period of normal sinus rhythm and by validating the algorithms using the mSToPS dataset with 3 years of clinical follow-up and annotated co-morbidities. 3. Develop a technique to provide a preliminary interpretation of representation learning features for time-series data applied to the short- and long-term prediction. This retrospective study will develop and optimize new predictive techniques from single-lead ECGs, available through consumer devices, with the goal of identifying individuals at high risk of developing AF. A future direction to build on from this study's results would include a prospective study of AF prediction using consumer single-lead ECG to improve clinical outcomes.

项目摘要/摘要 全世界有3000多万人被诊断出患有房颤(AF)，然而，另一个 13%的房颤患者没有得到诊断。患有房颤的人中风风险增加五倍 多达三分之一的中风与房颤有关。适时地管理适当的 预防性治疗，特别是抗凝剂，可以显著减少房颤的并发症， 包括中风在内，下降了65%，死亡率下降了30%。 数字健康技术提供了新的方法来识别未诊断的房颤患者，特别是 阵发性房颤(PAF)，特征是偶尔发作，持续时间有限，对他来说，10秒 在临床环境下进行的12导联心电图不太可能与房颤事件重叠。 持续监测是有希望的，但对于老年人来说，成本仍然很高，负担也很大，他们处于 风险更高。 为了最大限度地提高这些技术的诊断效率，我们提出了预测未来的新方法 正常窦性心律时单导联心电图出现房颤。就在最近，它才被证明 从12导联正常窦性心律的心电图中预测房颤的未来发生是可能的。 一种临床环境。在这里，我们建议用商业上可用的单项指标来预测房颤的发生。 领先的心电设备，这将为非临床环境中的早期检测提供可扩展的替代方案。 为了实现这一目标，我们将对1万多名个体的原始单导联心电数据进行回顾分析 与PAF进行了超过14天的监测。验证工作将在一套独特的1,718项工作中进行 参加房颤筛查的前瞻性mSToPS临床试验的无症状个体(Mean 73岁)，有完整的临床信息和合并症。这项计划的三个目标是： 1.计算正常窦性心律个体在短期内发生房颤事件的概率 使用经典的单导联心电信号特征和基于表征学习的特征。 2.开发一种长期预测房颤发病的方法，方法是评估在 从最初的正常窦性心律监测期起计1、3、6和12个月，并通过验证 算法使用mSToPS数据集，具有3年的临床随访和注释的共病。 3.开发一种技术来初步解释表征学习特征 时间序列数据应用于短期和长期预测。 这项回溯性研究将开发和优化单导联心电图的新预测技术， 可通过消费设备获得，目的是识别房颤的高危人群。 根据这项研究的结果，未来的研究方向将包括房颤预测的前瞻性研究 使用消费者单导联心电来改善临床结果。