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

基本信息

批准号：
10463763
负责人：
Sardar Ansari
金额：
$ 16.4万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
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/10463763
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 Health Care Costs Healthcare Systems Heart Research 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 Research Project Grants Rest Retrospective cohort Risk Risk Estimate Sampling Scientist Severities Signal Transduction Supervision Symptoms Techniques Testing Therapeutic Intervention Training Translational Research Treatment outcome United States United States National Institutes of Health Universities Weight Writing actigraphy analytical tool autoencoder base cardiogenesis career career development clinical care clinical decision support cost deep learning experience heart rate variability hemodynamics high risk improved improved outcome indexing 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

项目摘要

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，深度学习和商业可穿戴设备项目概要/摘要研究：心力衰竭是美国死亡的主要原因之一，也是医疗费用的驱动因素国家。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，这将为改善患者体验和结果创造机会开始早期的行为和治疗干预。电子健康记录 (EHR) 包含信息可用于在心力衰竭发作前预测其。然而，现有模型导致大量假阳性预测，限制了其临床实用性。 PI 建议通过以下方式扩充 EHR 数据：心电图 (ECG) 和心率变异性 (HRV) 功能可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。这些模型将是使用密歇根医学提供的患者数据进行回顾性开发和验证。第二个目标是该提案的目的是通过用临床测量的心电图和心率变异性替代来增加这项研究的影响通过智能手表等消费类可穿戴设备获得的数据。一组前瞻性患者将佩戴可穿戴设备 7 天，这将允许 PI 确定收集的信息是否（间歇性心电图、来自光电体积描记法和体动记录法的连续 HRV）与 EHR 相结合，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。虽然这该方法将使更多患者受益，但仍仅限于有既往病史的患者。到进一步将这项研究的影响扩大到那些佩戴消费者可穿戴设备但以前没有使用过的人病史，仅依赖于可穿戴设备收集的信息的有限模型将被评价。因此，这项研究的结果将包括针对不同人群的多种模型，例如有或没有既往病史的人。候选人/职业发展：Sardar Ansari 博士是一名计算机拥有生物医学信号处理、机器学习和医学专业知识的科学家和统计学家可穿戴设备。他过去的研究经验包括分析心电图信号以改进检测心律失常并减少重症监护病房的误报；检测和消除噪声和运动生物医学信号中的伪影，例如心电图和生物阻抗；血流动力学失代偿的预测使用心率变异性；以及失血性休克、透析中低血压和低心脏指数的检测可穿戴技术。该奖项将使 Ansari 博士能够获得心血管方面所需的额外培训生理学和心力衰竭病理生理学，通过指导、教学培训、参加研讨会和科学会议和临床接触，为他的独立职业生涯做好准备，专注于发展心血管医学的诊断和临床决策支持工具。