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

评估症状和影响的新技术。

• 批准号: 10207740
• 负责人: Hamid Ghanbari
• 金额: $ 19.66万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10207740
• 关键词: Accelerometer; Address; Affect; Arrhythmia; Atrial Fibrillation; Behavioral; Cardiology; Cardiovascular system; Cellular Phone; Chest Pain; Clinic Visits; Clinical; Clinical Research; Complex; Congestive Heart Failure; Data; Data Analyses; Development; Dizziness; Dyspnea; Ecological momentary assessment; Electrocardiogram; Evaluation; Fatigue; Functional disorder; Goals; Healthcare; Heart Rate; Heart failure; Hospitalization; Intervention; Knowledge; Lead; Light; Link; Machine Learning; Measures; Medical; Mentors; Mentorship; Methodology; Noise; Palpitations; Patient Self-Report; Patients; Pattern; Perception; Pharmaceutical Preparations; Physical Function; Physiological; Procedures; Research; Research Methodology; Research Personnel; Resources; Role; Sensitivity and Specificity; Severities; Sinus; Symptoms; Techniques; Therapeutic; Time; Training; United States; Visit; associated symptom; base; clinical care; disability; functional decline; functional status; health care service utilization; health related quality of life; heart rhythm; innovation; insight; longitudinal analysis; machine learning algorithm; mobile application; mortality; multilevel analysis; negative affect; novel; programs; psychologic; public health relevance; signal processing; skills; stroke risk; wearable sensor technology

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.

项目摘要/摘要 房颤是美国最常见、最主要的心律失常。这会导致风险增加 中风、充血性心力衰竭和总死亡率。房颤的特征也是大多数 可能导致与健康相关的生活质量和功能状态显著下降的患者，这些患者包括 房颤患者的各种原因和心血管住院的强烈预测因素。因此，改进 症状的改善是房颤患者治疗的一个重要目标，同时减少 中风和死亡的风险。然而，先前评估房颤症状的研究受到它们的限制 回顾评估限制我们评估心率、心脏节律、 实时显示症状、影响和功能状态。 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名患有 使用连续心律记录器和一种新的移动应用程序收集数据的阵发性房颤 症状和影响评级在一天中的多个场合持续三周。然后我们将能够 检查症状、情绪、心率和心电附加特征之间的关系 记录并评估它们对房颤患者功能状态的影响。我们假设1)有些人 房颤症状(如心悸)比其他症状(如疲劳)更有针对性。 2)源自信号处理和机器学习算法的心电特征(特别是那些服务于 作为自主神经功能的替代者)将更敏感和具体地确定存在和 症状的严重程度与平均心率相比3)情感和 症状和功能状态都是如此。 这项提案的首要目标是让候选人(哈米德·甘巴里，医学博士，公共卫生硕士)制定一项独立的 研究程序检查发作性疾病患者的症状和相关的功能状态下降 自动对焦。应聘者将在之前培训的基础上与一支精通以下领域的导师团队合作 生态瞬时评估方法、信号处理和机器学习、影响和 功能状态以获得对症状的重复、实时评估的专业知识，并 探索新的心电特征，预测房颤存在或不存在以外的症状。与 建议的学习，候选人还将进行教学培训和一对一的指导与他的 研究目的。 这项建议将更清楚地描述症状及其生理和心理相关性，并 它们随后对房颤患者功能状态的影响。通过这项建议获得的见解 可能最终导致最好地解决这些症状的个性化行为和医学干预 以及相关的功能障碍。