Heart Rate Dynamics in Response to Upper-Extremity Function Test to Identify Irreversible Frailty After Invasive Therapy in Older Adults with Advanced Heart Disease

心率动态对上肢功能测试的反应，以识别患有晚期心脏病的老年人在侵入性治疗后的不可逆转的虚弱

• 批准号: 10582957
• 负责人: Nima Toosizadeh
• 金额: $ 1.45万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10582957
• 关键词: Adverse event; Age; Aortic Valve Stenosis; Arizona; Assessment tool; Beds; Behavior; Biomedical Engineering; Biometry; Cardiac; Cardiology; Cardiopulmonary; Cardiovascular system; Categories; Cause of Death; Characteristics; Chest; Clinical; Clinical Treatment; Cognitive; Data; Disease; Elbow; Elderly; Electrocardiogram; Engineering; Environment; Exercise; Eye; Fatigue; Geriatrics; Goals; Healthcare; Heart; Heart Diseases; Heart Rate; Homeostasis; Inpatients; Lasso; Lead; Left; Legal patent; Machine Learning; Measures; Methods; Mission; Modeling; Morphologic artifacts; Motion; Motor; Outcome; Participant; Patients; Performance; Physical activity; Physiological; Prediction of Response to Therapy; Procedures; Recovery; Replacement Therapy; Research; Risk; Risk Assessment; Sampling; Side; Speed; Stress; Symptoms; System; Testing; Therapeutic; Training; Treatment Cost; Treatment outcome; United States; Universities; Upper Extremity; Vascular blood supply; Walking; adverse outcome; aggressive therapy; aortic valve replacement; arm; candidate identification; candidate selection; clinical care; common treatment; comorbidity; comparison control; cost; design; flexibility; follow-up; frailty; heart function; heart rate monitor; improved; motion sensor; motor disorder; motor impairment; multidisciplinary; multimodality; novel; outcome prediction; predict clinical outcome; programs; resilience; response; risk stratification; sensor; sex; signal processing; stressor; therapy outcome; tool; wearable sensor technology

PROJECT SUMMARY/ABSTRACT Advanced heart diseases lead to a reduced blood supply from the heart and consequently fatigue and deficits in performing physical activity. In the proposed research, we will assess the lack of physiological reserve in older adults with advanced heart disease, focusing on motor and cardiac function, to develop a novel, objective, quick, and accurate frailty score. We designed this approach to enhance candidate selection of older adults going through invasive therapies for advanced heart diseases. Although our approach is generic, to reduce between- subject variability we focus on transcatheter aortic valve replacement (TAVR) for older adult with aortic stenosis. Therapeutic options continue to grow for TAVR; however, it can be difficult to identify candidates with frailty level that prohibit them from tolerating the stress from aggressive therapy and those with potential reversible frailty. It is thus critically important to introduce meaningful routine objective frailty assessment into clinical care of cardiac patients. Frailty assessment is, however, not common in cardiology because current assessment tools are burdensome for older adults with advanced heart disease and impractical to implement in busy clinical environments. More importantly, no disease-specific tool is available to identify heart disease-related frailty. In continuation of our research program in the field of sensor-based frailty assessment, within our multidisciplinary team of engineers, cardiologists, and geriatricians, we propose the multimodal upper-extremity function (UEF) test for identifying frailty among older adults with advanced heart disease. UEF incorporates a synchronized system of motion sensors and electrocardiography (ECG) to measure motor performance and heart rate (HR) during a rapid 20-second elbow flexion task. Using our previously established UEF motion tracking component, we can precisely measure the level of deficits in motor performance (slowness, weakness, inflexibility, and fatigue), which are not detectable using eyeball assessment. Using the ECG setup, minimizing the motion artifact using this localized task, we accurately track HR response to the physical demand, as well as recovery behavior after the task. HR behavior during the task and recovery (HR dynamics) provide a measure of cardiac reserve associated with frailty in heart disease. We will further quantify dysregulation between HR response and motor demand as a novel measure of resilience. Based on our preliminary data, we expect a weaker and delayed HR response to physical activity as well as an impaired motor function due to frailty. The proposed research will be a four-year study at the University of Arizona Cardiovascular program. In the first arm, we will establish a new HR score and merge it with our previously validated UEF motor score to develop a multimodal frailty score among older adults with advanced heart disease (n=120). In the second arm, in a longitudinally setting we will validate the UEF multimodal frailty score for predicting TAVR complications for advanced heart disease (n=75). Accomplishing these aims, we will promote HR dynamic assessment for risk stratification of older adults with advanced heart disease, with huge potentials for other comorbid conditions.

项目总结/摘要 晚期心脏病导致心脏供血减少，从而导致疲劳和心脏功能不足。 进行体力活动。在拟议的研究中，我们将评估老年人缺乏生理储备的情况。 患有晚期心脏病的成年人，专注于运动和心脏功能，开发一种新颖，客观，快速， 和准确的虚弱评分。我们设计这种方法是为了提高老年人的候选人选择， 通过侵入性疗法治疗晚期心脏病。虽然我们的方法是通用的，以减少之间- 受试者变异性我们关注老年主动脉瓣狭窄患者的经导管主动脉瓣置换术（TAVR）。 TAVR的治疗选择继续增加;然而，可能难以识别虚弱程度的候选人 这使他们无法忍受来自积极治疗的压力，以及那些潜在的可逆性虚弱。它 因此，将有意义常规客观虚弱评估引入心脏病的临床护理中是至关重要的 患者然而，虚弱评估在心脏病学中并不常见，因为目前的评估工具 这对于患有晚期心脏病的老年人来说是沉重的负担，并且在忙碌的临床 环境.更重要的是，没有疾病特异性工具可用于识别心脏病相关的虚弱。 为了继续我们在基于传感器的脆弱性评估领域的研究计划，在我们的范围内 多学科团队的工程师，心脏病学家和老年病学家，我们提出了多模式上肢 功能（UEF）测试用于识别患有晚期心脏病的老年人的虚弱。UEF包含一个 运动传感器和心电图（ECG）的同步系统，以测量运动性能， 心率（HR）在快速20秒肘关节屈曲任务。利用我们之前建立的UEF动议 跟踪组件，我们可以精确地测量运动性能的缺陷水平（缓慢，虚弱， 易疲劳和疲劳），这是使用眼球评估无法检测到的。使用ECG设置，最小化 运动伪影使用这个本地化的任务，我们准确地跟踪HR响应的物理需求，以及 任务后的恢复行为。任务和恢复期间的HR行为（HR动力学）提供了一种测量方法 与心脏病虚弱相关的心脏储备。我们将进一步量化HR与 反应和运动需求作为一种新的弹性措施。根据我们的初步数据， 对身体活动的较弱和延迟的HR反应以及由于虚弱而导致的运动功能受损。 这项拟议中的研究将在亚利桑那大学心血管项目进行为期四年的研究。上 手臂，我们将建立一个新的HR评分，并将其与我们先前验证的UEF运动评分合并，以开发一个 在患有晚期心脏病的老年人中进行多模态虚弱评分（n=120）。在第二只手臂中， 纵向设置，我们将验证UEF多模式虚弱评分用于预测TAVR并发症， 晚期心脏病（n=75）。为实现这一目标，我们将推动人力资源风险的动态评估 对患有晚期心脏病的老年人进行分层，具有其他共病疾病的巨大潜力。