Novel Techniques for Evaluating and Assessing Symptoms, Affect. Heart Rhythm and Functional Status in Patients with Atrial Fibrillation: miAfib Project

项目总结/摘要 心房颤动（AF）是美国最常见的主要心律失常。这会增加 中风、充血性心力衰竭和总死亡率的百分比。AF的特征还在于大多数患者的症状， 可导致健康相关生活质量和功能状态显著下降的患者， AF患者全因和心血管住院的强预测因子。因此，改善 在房颤患者的治疗中，沿着减少房颤的发生是一个重要的治疗目标。 中风和死亡的风险。然而，以往评估房颤症状的研究受到其 对症状的回顾性评估限制了我们评估心律， 真实的时间症状、情感和功能状态。 为了解决所有这些差距，我们提出了一项创新的研究，将集中检查100名患者， 使用连续心律记录仪和新型移动的应用程序收集阵发性AF的数据 症状和情感评级。我们将能够 检查症状、情绪、心律以及ECG中其他特征之间的关系 记录并评估它们对AF患者功能状态的影响。我们假设：1）一些 症状（例如心悸）比其他症状（例如疲劳）更能明确地指示AF 2)来自信号处理和机器学习算法的ECG特征（特别是那些服务于 作为自主功能的替代物）对于确定存在将更加敏感和特异， 与平均心率相比，症状的严重程度3）情感之间存在强烈的关系， 症状和功能状态。 该提案的总体目标是候选人（Hamid Ghanbari，医学博士，公共卫生硕士）制定一项独立的 一项研究计划，检查阵发性心脏病患者的症状和相关的功能状态下降 AF.候选人将在他以前的培训基础上，与一个在以下方面具有专长的导师团队合作： 生态瞬时评估方法，信号处理和机器学习，影响， 功能状态，以获得评价症状的重复、实时评估的专业知识， 探索新的ECG特征，预测AF存在或不存在以外的症状。 建议的研究，候选人还将追求教学培训和一对一的指导有关他的 研究目的。 该提案将更清楚地描述症状及其生理和心理相关性， 他们随后对AF患者功能状态的影响。通过这项建议获得的见解 可能最终导致个体化的行为和医疗干预，最好地解决这些症状 以及相关的功能障碍。

Project Summary/Abstract Atrial fibrillation (AF) is the most prevalent, major arrhythmia in the United States. It leads to an increased risk of stroke, congestive heart failure, and overall mortality. AF is also characterized by symptoms in a majority of patients that can result in significant decreases in health related quality of life and functional status, which are strong predictors of all-cause and cardiovascular hospitalizations in patients with AF. Therefore, improvement in symptoms is an important therapeutic goal in the management of patients with AF along with reducing the risk of stroke and mortality. However, previous studies evaluating symptoms in AF have been limited by their retrospective assessment of symptoms that limits our ability to assess the relationship between heart rhythm, symptoms, affect and functional status in real time. To address all of these gaps, we propose an innovative study that will intensively examine 100 patients with paroxysmal AF using a continuous heart rhythm recorder and a novel mobile application to collect data on symptom and affect ratings during multiple occasions across a day for three weeks. We will then be able to examine the relationship between symptoms, affect, heart rhythm as well as additional features within the ECG recording and assess their effect on functional status in patients with AF. We hypothesize that 1) some symptoms will be much more specifically indicative of being in AF (e.g. palpitations) than others (e.g. fatigue) 2) ECG features derived from signal processing and machine learning algorithms (especially those that serve as surrogates for autonomic function) will be more sensitive and specific for determining the presence and severity of symptoms compared to average heart rate 3) there will be a strong relationship between affect, and both symptoms and functional status . The overarching goal of this proposal is for candidate (Hamid Ghanbari, MD, MPH) to develop an independent research program examining symptoms and associated decline in functional status in patients with paroxysmal AF. The candidate will build upon his previous training by partnering with a team of mentors who are experts in ecological momentary assessment methodology, signal processing and machine learning, affect, and functional status to acquire expertise in evaluation of repeated, real-time assessments of symptoms and to explore novel ECG features that predict symptoms beyond the presence or absence of AF. In concert with the proposed study, the candidate will also pursue didactic training and one-on-one mentoring related to his research aims. This proposal will more clearly characterize symptoms and their physiological and psychological correlates and their subsequent influence on functional status in patients with AF. The insights obtained through this proposal could eventually lead to individualized behavioral and medical interventions that best address these symptoms and associated dysfunction.