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: 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
• 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

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.

使用多模式数据分析，深度预测心力衰竭发作 学习和商业可穿戴设备 项目摘要/摘要 研究：心力衰竭是统一死亡率和医疗保健费用驱动因素的主要原因之一 国家。到2030年，预计心力衰竭患者的数量将达到800万。如果我们能预测谁 发展心力衰竭，这将为改善患者体验和结果而创造机会 启动早期的行为和治疗干预措施。电子健康记录（EHR）包含信息 可以用来在心力衰竭发作之前预测心力衰竭。但是，现有模型导致了大量 虚假的积极预测，限制其临床实用性。 PI建议使用EHR数据增加 心电图（ECG）和心率变异性（HRV）特征，以提高预测的准确性 心力衰竭提前12个月发作。数据的三种方式（EHR，ECG和HRV）将是 使用深度学习方法进行分析，包括PI提出的新技术。模型将是 使用密歇根医学可用的患者数据进行了回顾性的回顾性验证。第二个目标 该提议是通过用临床测量的ECG和HRV来增加这项研究的影响 那些由消费者可穿戴设备（例如智能手表）获得的。前瞻性患者队列将佩戴 可穿戴设备7天，这将使PI能够确定收集的信息是否 （间歇性心电图，源自光的hRV，源自光绘画学，动作法），与EHR相结合， 可以为临床医生提供更有效的工具，以确定哪些患者有患心力衰竭的风险。同时 方法将使较大的患者受益，它仍将仅限于过去病史的患者。到 进一步将这项研究的影响扩大到那些穿消费者可穿戴设备但没有以前的人 病史是一种有限的模型，仅取决于可穿戴设备收集的信息 评估。因此，这项研究的结果将包括针对各种人群的多种模型，例如 那些有或没有先前病史的人。候选人 /职业发展：Sardar Ansari博士是计算机 科学家和统计学家具有生物医学信号处理，机器学习和医学方面的专业知识 可穿戴设备。他过去的研究经验包括对ECG信号的分析，以改善对检测的检测 心律不齐并减少重症监护病房中的错误警报；检测和去除噪声和运动 生物医学信号（例如心电图和生物阻抗）中的伪影；血液动力学代理的预测 使用HRV;并检测出血性休克，子宫内低血压和低心脏指数的检测 可穿戴技术。该奖项将使Ansari博士可以接受心血管的其他培训 通过指导，教学培训，参加研讨会和 科学会议和临床曝光 心血管医学的诊断和临床决策支持工具。