Novel Algorithm and Data Strategies to detect and Predict atrial fibrillation for post-stroke patients (NADSP)

用于检测和预测中风后患者心房颤动的新算法和数据策略 (NADSP)

• 批准号: 10561108
• 负责人: Xiao Hu
• 金额: $ 70.06万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-10 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10561108
• 关键词: Address; Adhesives; Adult; Affect; Aging; Algorithms; Anticoagulation; Apple; Arrhythmia; Atrial Fibrillation; Attention; Benchmarking; Big Data; Blood Volume; Cardiac; Classification; Complement; Data; Data Set; Detection; Diagnosis; Diagnostic; Effectiveness; Electrocardiogram; Event; Fingers; Goals; Heart; Hospitalization; Inferior; Inpatients; Knowledge; Label; Learning; Link; Machine Learning; Measures; Modeling; Monitor; Morbidity - disease rate; Output; Patients; Pattern; Performance; Persons; Photoplethysmography; Physiological; Plethysmography; Population; Predictive Value; Prevalence; Probability; Prospective Studies; Reporting; Risk; Screening procedure; Signal Transduction; Sinus; Stroke; Stroke prevention; Testing; Time; Training; Weight; Wrist; convolutional neural network; cost; cost effective; data integration; deep neural network; design; discrete time; fitbit; follow-up; high risk; improved; loss of function; mortality; mortality risk; neural network architecture; novel; post stroke; preventive intervention; prospective; recurrent neural network; repository; sensor; stroke patient; tool; wearable device

Project Summary Atrial fibrillation (AF) is the most common arrhythmia, affecting 33.5 million people globally with a growing prevalence. AF is associated with significant morbidity and mortality, including 20% of all strokes, 33% of hospitalizations related to cardiac arrhythmias, and a two-fold increase in risk of death. To reduce AF-associated risks such as stroke, it is important to be able to diagnose AF early in the AF trajectory when it is asymptomatic and paroxysmal in order to initiate effective stroke prevention interventions including anticoagulation. Unfortunately, it is estimated that 700,000 people in the USA may have previously unknown AF, and newly detected AF at the time of stroke was found among 18% of AF-associated stroke incidents. Plethysmography (PPG) measures pulsatile blood volume changes and is available in up to 71% of consumer wearables. Because of this unmatched availability, PPG-based AF detection is ideally poised to enable low-cost, long-term, and continuous AF monitoring at scale. However, modest performance of PPG-based AF detection when PPG signals do not have perfect signal quality remains a critical impediment to fully realize its potential as an AF-monitoring tool at scale. The proposed study aims to overcome this challenge by pursuing the following aims: 1) design, develop, and validate a novel deep neural network (DNN) architecture that integrate PPG signal quality assessment with AF detection to accurately detect AF even for signals with imperfect signal quality; 2) validate and test further personalization of the proposed DNN using prospective data from post stroke patients to be collected in ambulatory settings; 3) develop and validate interpretable EHR-data driven machine learning approaches to identify patients with elevated risk of AF for whom PPG-based AF monitoring can be most likely beneficial.

项目摘要 心房颤动（AF）是最常见的心律失常，全球影响3350万人 越来越多的患病率。 AF与明显的发病率和死亡率有关，包括所有的20％ 中风，33％与心律不齐相关的住院 死亡。为了减少与AF相关的风险，例如中风，重要的是能够在早期诊断AF AF轨迹无症状和阵发性时，为了启动有效的中风 预防干预措施，包括抗凝治疗。不幸的是，估计有70万人 在美国，以前可能有未知的AF，并且在中风时发现了新发现的AF 在18％的AF相关中风事件中。杂质学（PPG）测量脉冲血液 体积变化，可在多达71％的消费者可穿戴设备上使用。因为这个无与伦比 可用性，基于PPG的AF检测是理想情况下可以实现低成本，长期和连续的 对AF进行大规模监测。但是，当PPG时，基于PPG的AF检测的适度性能 信号没有完美的信号质量仍然是完全意识到其潜力的关键障碍 大规模的AF监控工具。拟议的研究旨在通过追求 以下目的：1）设计，开发和验证一种新颖的深神经网络（DNN）体系结构 将PPG信号质量评估与AF检测相结合以准确检测AF，即使是信号 信号质量不完美； 2）使用并测试使用建议的DNN的进一步个性化 从卧床环境中收集的中风患者的前瞻性数据； 3）发展和 验证可解释的EHR-DATA驱动的机器学习方法，以识别患者升高的患者 基于PPG的AF监测的AF风险很可能是有益的。