Feasibility testing of a novel AI-enabled, cloud-based ECG diagnostic solution to enable fast and affordable diagnosis in long-term continuous ambulatory ECG monitoring

对新型人工智能、基于云的心电图诊断解决方案进行可行性测试，以在长期连续动态心电图监测中实现快速且经济实惠的诊断

• 批准号: 10545691
• 负责人: Bin Fang
• 金额: $ 25.96万
• 依托单位: ZBEATS, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-12 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10545691
• 关键词: Address; Adopted; Affect; Algorithms; Arrhythmia; Award; Businesses; Cardiac; Clinical; Clinical Data; Clinical Research; Cloud Computing; Collaborations; Cost Analysis; Data; Data Analyses; Data Collection; Data Set; Databases; Detection; Devices; Diagnosis; Diagnostic; Disease; Early Diagnosis; Electrocardiogram; Emergency department visit; Enrollment; Environment; Etiology; Evaluation; Event; Future; Goals; Healthcare; Holter Electrocardiography; Hospitalization; Human; Human Resources; Informed Consent; Investigation; Ischemic Stroke; Lead; Life; Medicaid; Medical; Medical center; Medicare; Monitor; Observational Study; Pain; Participant; Patients; Persons; Phase; Phase II Clinical Trials; Physicians; Population; Postdoctoral Fellow; Preventive treatment; Procedures; Protocols documentation; Questionnaires; Recording of previous events; Reporting; Research; Risk; Role; Sample Size; Signal Transduction; Small Business Innovation Research Grant; Small Business Technology Transfer Research; Statistical Data Interpretation; Stream; Stroke; Surveys; Symptoms; Syncope; System; Technology; Telemetry; Telephone; Test Result; Testing; Time; Training; United States National Institutes of Health; Universities; University Hospitals; Work; artificial intelligence algorithm; base; clinical practice; clinical research site; cloud based; commercialization; cost; data exchange; data visualization; design; detection sensitivity; disability; feasibility testing; flexibility; follow-up; high risk; improved; innovation; innovative technologies; mortality; novel; participant enrollment; phase 1 study; phase 2 study; prototype; recruit; remote patient monitoring; risk stratification; safety net; satisfaction; standard of care; stroke patient; success; telehealth; tool; wearable device

PROJECT SUMMARY. The proposed observational study is to evaluate the feasibility of a novel ECG monitoring system leveraging concurrent AI and cloud technologies in long-term continuous monitoring (LTCM) in the clinical environment. It does not intend to use any data or information from the investigational solution to interfere, intervene or affect any clinical decisions made for the participants. Among nearly 2M per year syncope or TIA/stroke patients, 12-15% are cardiac-arrhythmia associated, which usually carries higher risk for long-term disability and even mortality than other-etiologies patients. Proper risk stratification and early initiation of appropriate preventative treatment can result in significant reduction of the cardiac related diseases and their associated mortality. Although LTCM has been proven to be able to detect arrhythmia with high diagnostic yield, the current standard of care has major market pains: 1) days-to-weeks of delay to deliver final report for offline extended Holter; 2) low accuracy in stream arrhythmia detection for online Mobile Cardiac Telemetry; and 3) physicians do not have access to patients’ ECG data. ZBeats’ solution is aiming to improve today’s standard of care by addressing technology accessibility and affordability. ZBPro™, ZBeats’ alpha prototype was validated against our proprietary dataset as well as public datasets required in ANSI/AAMI EC57, demonstrating algorithms, data transmission and visualization work well as expected. In this Phase I study, the feasibility will be tested in the clinical environment by completing the following specific aims (SA): SA1: setup data collection systems and provide training to clinical personnel prior to recruitment. SA2: Conduct patients’ acceptability evaluation by enrolling 60-75 patients to wear the device for up to 7 days. SA3: Evaluate the arrhythmia-capturing capability by conducting physician’s satisfaction questionnaires after reviewing the reports generated from the study system. SA4: Conduct data analysis and start designing the protocol for Phase II study. This proposal will undergo collaboration among ZBeats, Stony Brook University Hospital and Lankenau Medical Center. The long- term goal is to dramatically improve the current standard of care in LTCM by reducing the time to detection of life-threatening arrhythmia from weeks to minutes for cardiac-related high-risk patients, increase the streaming detection accuracy and reducing the total costs by leveraging AI algorithms, cloud infrastructure and a low-cost flexible-material patch. This cost reduction will lead to more general medical use cases, such as telehealth & Remote Patient Monitoring (RPM) to benefit broader population.

项目总结。该观察性研究旨在评估一种新型心电监测的可行性