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: 10195981
• 负责人: Giorgio Quer
• 金额: $ 26.63万
• 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 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10195981
• 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; Modeling; 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; 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 但是，全世界有超过3000万个人被诊断出房颤（AF） 13％的AF患者未被诊断。患有AF的人的中风风险增加了五倍 最多显示与AF有关的所有笔触中有三分之一。及时管理适当的 预防性疗法，尤其是抗凝剂，可以显着降低AF的并发症， 包括中风，增加65％，死亡率增加30％。 数字健康技术提供了新的方法来识别患有未诊断AF的人，特别是 阵发性AF（PAF），其特征是偶尔的持续时间有限的发作，十秒钟 在临床环境中进行的12条铅心电图（ECG）不太可能与AF事件重叠。 连续监控是有希望的，但对于老年人来说仍然是昂贵且负担重的 较高的风险。 为了最大化这些技术的诊断产量，我们提出了新的方法来预测未来 在正常窦性心律期间，单个铅ECG发生AF。直到最近才显示 可以预测在收集的正常窦性节奏中12铅ECG的AF的未来发生 临床环境。在这里，我们建议用市售单一的AF发生预测AF的发生 铅心电图设备，它将在非临床环境中实现可扩展的替代方案。 为了实现这一目标，我们将回顾性地分析10,000多个人的原始单铅ECG数据 PAF在14天内进行监控。然后，验证工作将以独特的1,718组进行 参加AF筛查的前瞻性MSTOPS临床试验的无症状个体（平均 73岁），具有完整的临床信息和合并症。该项目的三个目标是： 1。计算正常节奏中个人的未来AF事件的概率 使用经典的单铅ECG功能和基于表示的学习功能。 2。通过评估在中检测到的AF的个体长期预测AF发作的方法 从正常节奏的初始监测期开始，并通过验证 使用MSTOPS数据集进行3年临床随访和注释的合并症算法。 3。开发一种技术，以提供对表示特征的表示特征的初步解释 时间序列数据应用于短期和长期预测。 这项回顾性研究将开发并优化单铅ECG的新预测技术， 可通过消费者设备获得，目的是确定具有高风险开发AF的个体。 这项研究结果的未来方向将包括对AF预测的前瞻性研究 使用消费者单铅ECG改善临床结果。