Prediction of Heart Failure Onset using Multimodal Data Analysis, Deep Learning and Commercial Wearables

使用多模态数据分析、深度学习和商业可穿戴设备预测心力衰竭发作

• 批准号: 10681229
• 负责人: Sardar Ansari
• 金额: $ 16.35万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10681229
• 关键词: Accelerometer; Affect; Age; American; Arrhythmia; Autonomic nervous system; Award; Behavior Therapy; Cardiac; Cardiovascular Physiology; Cardiovascular system; Cause of Death; Clinical; Computers; Computing Methodologies; Data; Data Analyses; Detection; Development; Diagnosis; Diagnostic; Early Diagnosis; Early identification; Early treatment; Educational workshop; Electrocardiogram; Electronic Health Record; Ensure; Excision; Exposure to; Functional disorder; Future; Grant; Health Care Costs; Healthcare Systems; Heart failure; Hemorrhagic Shock; Home environment; Hypotension; Incidence; Individual; Intensive Care Units; Intervention; K-Series Research Career Programs; Lead; Machine Learning; Measures; Medical; Medical History; Medicine; Mentorship; Methods; Michigan; Modality; Modeling; Monitor; Morphologic artifacts; Morphology; Motion; Noise; Onset of illness; Outcome; Outcome Study; Patient-Focused Outcomes; Patients; Photoplethysmography; Physical activity; Physiology; Pilot Projects; Population; Price; Procedures; Prospective cohort; Research; Research Personnel; Rest; Retrospective cohort; Risk; Sampling; Scientist; Severities; Signal Transduction; Symptoms; Techniques; Testing; Therapeutic Intervention; Training; Translational Research; Treatment outcome; United States; United States National Institutes of Health; Universities; Writing; actigraphy; analytical tool; autoencoder; career; career development; clinical care; clinical decision support; cost; deep learning; experience; heart rate variability; hemodynamics; high risk; improved; improved outcome; indexing; information gathering; learning strategy; meetings; mortality; multimodal data; multimodality; multiple data types; novel; patient population; prevent; prospective; response; responsible research conduct; signal processing; smart watch; string theory; support tools; tool; usability; wearable device

Prediction of Heart Failure Onset using Multimodal Data Analysis, Deep Learning and Commercial Wearables Project Summary/Abstract Research: Heart failure is one of the leading causes of mortality and drivers of healthcare costs in the United States. By 2030, the number of heart failure patients is projected to reach 8 million. If we could predict who will develop heart failure, this would create an opportunity to improve patient experiences and outcomes by initiating earlier behavioral and therapeutic interventions. Electronic health records (EHR) contain information that can be used to predict heart failure before its onset. However, the existing models lead to a large number of false positive predictions, limiting their clinical utility. The PI proposes to augment the EHR data with electrocardiogram (ECG) and heart rate variability (HRV) features to improve the accuracy of predicting the onset of heart failure 12 months in advance. The three modalities of data (EHR, ECG and HRV) will be analyzed using deep learning methods, including novel techniques proposed by the PI. The models will be developed and validated retrospectively using patient data available at Michigan Medicine. The second aim of the proposal is to increase the impact of this research by replacing the clinically measured ECG and HRV with those obtained by consumer wearables such as smart watches. A prospective cohort of patients will wear a wearable device for seven days, which will allow the PI to determine whether the collected information (intermittent ECG, continuous HRV derived from photoplethysmography, and actigraphy), combined with EHR, can provide clinicians with a more effective tool to identify which patients are at risk of heart failure. While this approach will benefit a larger population of patients, it will still be limited to those with past medical history. To further expand the impact of this research to those who wear consumer wearables but have no previous medical history, a limited model that depends only on the information gathered by the wearable device will be evaluated. Thus, the outcomes of this study will include multiple models targeting various populations, such as those with and without prior medical history. Candidate / Career Development: Dr. Sardar Ansari is a computer scientist and statistician with expertise in biomedical signal processing, machine learning, and medical wearable devices. His past research experience includes analysis of ECG signal to improve detection of cardiac arrhythmias and reduce false alarms in intensive care units; detection and removal of noise and motion artifacts in biomedical signals such as ECG and bioimpedance; prediction of hemodynamic decompensation using HRV; and detection of hemorrhagic shock, intradialytic hypotension, and low cardiac index using wearable technology. This award will allow Dr. Ansari to acquire needed additional training in cardiovascular physiology and heart failure pathophysiology through mentorship, didactic training, attending workshops and scientific meetings, and clinical exposure, preparing him for an independent career focused on developing diagnostic and clinical decision support tools for cardiovascular medicine.

使用多模态数据分析预测心力衰竭发作 学习和商业可穿戴设备 项目总结/摘要 研究：心力衰竭是美国死亡率和医疗费用的主要原因之一 States.到2030年，心力衰竭患者人数预计将达到800万。如果我们能预测谁会 发生心力衰竭，这将创造一个机会，通过以下方式改善患者体验和结局： 开始早期的行为和治疗干预。电子健康记录（EHR）包含信息 可以用来在心力衰竭发作前预测它。然而，现有的模型导致大量的 假阳性预测，限制了它们的临床应用。PI建议增加EHR数据， 心电图（ECG）和心率变异性（HRV）特征，以提高预测的准确性 提前12个月出现心力衰竭。三种数据模式（EHR、ECG和HRV）将 使用深度学习方法进行分析，包括PI提出的新技术。模特们将在 使用密歇根医学的患者数据进行回顾性研究和验证。的第二个目的 该提案旨在通过用以下方法取代临床测量的ECG和HRV来增加这项研究的影响力： 通过诸如智能手表的消费者可穿戴设备获得的那些。前瞻性患者队列将佩戴 可穿戴设备七天，这将允许PI确定收集的信息是否 （间歇性ECG、来自光电容积描记术的连续HRV和体动描记术），结合EHR， 可以为临床医生提供更有效的工具来识别哪些患者有心力衰竭的风险。虽然这 尽管这种方法将使更多的患者受益，但它仍将仅限于那些有既往病史的患者。到 进一步扩大这项研究的影响，以那些谁穿消费者可穿戴设备，但没有以前的 病史，一个有限的模型，只依赖于可穿戴设备收集的信息将是 评估。因此，本研究的结果将包括针对不同人群的多个模型，例如 有无既往病史者。候选人/职业发展：Sardar Ansari博士是一名计算机 科学家和统计学家，在生物医学信号处理，机器学习和医疗方面拥有专业知识 可穿戴设备他过去的研究经验包括分析心电图信号，以提高检测 心律失常和减少重症监护室的假警报;检测和消除噪音和运动 生物医学信号中的伪影，如ECG和生物阻抗;预测血流动力学失代偿 使用心率变异性;和检测出血性休克，透析中低血压，和低心脏指数，使用 可穿戴技术该奖项将使安萨里博士获得所需的额外培训，在心血管 生理学和心力衰竭病理生理学，通过指导，教学培训，参加研讨会， 科学会议和临床接触，为他的独立职业生涯做好准备，专注于开发 心血管医学的诊断和临床决策支持工具。