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

项目摘要 房颤（AF）是最常见的心律失常，影响全球3350万人， 越来越普遍。AF与显著的发病率和死亡率相关， 中风，33%的住院治疗与心律失常有关， 死亡为了减少房颤相关的风险，如中风，重要的是能够早期诊断房颤， 无症状和阵发性时的AF轨迹，以启动有效卒中 包括抗凝在内的预防干预措施。不幸的是，据估计有七十万人 在美国，可能有先前未知的AF，并且在卒中时发现新检测到AF 在18%的房颤相关中风事件中。体积描记法（PPG）测量脉动血液 体积变化，并可用于高达71%的消费者可穿戴设备。因为这个无与伦比的 可用性，基于PPG的AF检测非常适合实现低成本，长期和连续的 大规模房颤监测。然而，当PPG时，基于PPG的AF检测的适度性能 信号不具有完美的信号质量仍然是充分实现其潜力的关键障碍， 一个大规模的房颤监测工具。拟议的研究旨在通过以下方式克服这一挑战： 以下目标：1）设计，开发和验证一种新的深度神经网络（DNN）架构， 将PPG信号质量评估与AF检测相结合，即使对于信号也能准确检测AF 信号质量不完美; 2）使用以下方法验证和测试所提出的DNN的进一步个性化 在门诊环境中收集卒中后患者的前瞻性数据; 3）开发和 验证可解释的EHR数据驱动的机器学习方法，以识别升高的患者 基于PPG的AF监测最有可能受益的AF风险。

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.