Artificial Intelligence (AI) Enabled, Cloud-based ECG Diagnostic Solution (ZBPro) Feasibility Testing

NCT06013917 | Unknown FDA Device

• 2 other identifiers: Pro00065572R41HL160317-01A1
• Study Type: observational
• Target: 75
• Locations: 1 country
• Sites: 1

Brief Summary

The proposed research is to address the accessibility and affordability of technology to capture symptomatic and asymptomatic cardiac events via Long-Term Continuous Electrocardiogram Monitoring (LTCM), and to provide physicians with full access to their patients' recorded data in a timely manner. We adopt an FDA cleared single- lead OEM patch Holter made of flexible material to make long-term wearing more comfortable and more patient compliant. The patch transmits the recorded data to our cloud-based platform: ZBPro™, where our innovative technologies reside. Our proprietary AI algorithms with innovative human interaction tools, which were developed under the National Science Foundation (NSF) Small Business Innovation Research (SBIR) Phase I (Award #: 2025951) Award analyze and interpret the recorded data to detect and annotate arrhythmia/cardiac events, and generates daily reports for physician review. The feasibility of our algorithms has been verified using ZBeats' proprietary ECG database based on the standard ANSI/AAMI EC57. Data collection and transmission has been verified in the office environment. This proposed observational study will utilize a multidisciplinary collaboration of ZBeats Inc. (ZBTS), Stony Brook University (SBU), and Lankenau Medical Center (LMC). The study will enroll patients undergoing ECG monitoring. The primary outcome measure will be the ability to capture cardiac arrhythmias and events from participants. The prescribed FDA-cleared device will serve as the ground truth (GT) for our analyses. The detected arrhythmias and events from our solution will be compared with the findings from the ground truth to obtain our system's detection rate. The goal is to achieve a detection rate of \>80% to be deemed successful.

Conditions

Arrhythmias, Cardiac

Keywords

Artificial Intelligence; Cardiac Monitoring

Outcome Measures

Primary Outcomes (1)

• Capturing cardiac arrhythmias and events

  The ability to capture cardiac arrhythmias and events from participants will be the primary outcome of this study. The prescribed FDA-cleared device will serve as the ground truth (GT) for our analyses. The detected arrhythmias and events from our solution will be compared with the findings from the ground truth to obtain our system's detection rate. The goal is to achieve a detection rate of \>80% to be deemed successful.

  7 days after intervention

Secondary Outcomes (2)

• Assessing signal quality

  7 days after intervention

• Assessing user acceptability

  7 days after intervention

Study Arms (1)

Patients on ECG monitoring post-transcatheter aortic valve replacement (TAVR) or other cardiac event

Patients at our clinical collaborators practices who have undergone TAVR or other cardiac event and who are placed on ECG monitoring will be placed on an FDA cleared cardiac monitor. We adopt an FDA cleared, reusable, single-lead OEM patch Holter made of flexible material to make long-term wearing more comfortable and more patient compliance.

Device: Cardiac Monitoring

Interventions

Cardiac Monitoring DEVICE

FDA cleared ECG monitoring device that will be used to obtain ECG recordings and capture cardiac arrhythmias/events from participants

Patients on ECG monitoring post-transcatheter aortic valve replacement (TAVR) or other cardiac event

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Our study population will include patients that are prescribed to undergo ECG monitoring following a TAVR or recent cardiac-related event. We will recruit 60-75 participants to provide 62-78 events. SBU will recruit 15-20 TAVR patients (\< 4 participants /month, \< 2 in any week). LMC will recruit 10-15 TAVR patients \& 35-40 other cardiac patients (\< 10 participants/month, \< 4 in any week). A combined recruitment pool of 956 patients can be identified within 7 months from both sites.

You may qualify if:

• Patients who are \> 18 years of age
• Patients who are from one of the clinical collaborators practices
• Patients who are prescribed to undergo ECG monitoring using an FDA-cleared monitoring device
• Patients who are post TAVR procedure, a recent history of stroke/TIA, or having cardiac related symptoms
• Patients who are comfortable using a smartphone or have someone in the home to help with data transmission
• Patients who have manual dexterity to be able to recharge the phone battery or someone in the home to help them
• Patients who are English speaking

You may not qualify if:

• Patients who are \<18 years of age
• Patients who are not being prescribed with cardiac monitor testing
• Patients who refuse to sign informed consent
• Patients who are unable to provide informed consent
• Patients who have a pacemaker implanted
• Patients who are presenting with any dermatitis or infected skin over left anterior thorax
• Patients who have a history of reaction to a prior cardiac monitor device
• Patients who are uncomfortable having a cell phone at home for the duration of study participation
• Patients who non-English speaking

Sponsors & Collaborators

• ZBeats INC: lead
• National Heart, Lung, and Blood Institute (NHLBI): collaborator
• Stony Brook University: collaborator
• Main Line Health: collaborator

Study Sites (1)

Stony Brook University

Stony Brook, New York, 11794, United States

RECRUITING

MeSH Terms

Conditions

Arrhythmias, Cardiac

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Puja Parikh, MD

  Stony Brook University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Bin Fang, PhD

CONTACT

6312202463; bin.fang@zbeats.co

Ashutosh Yaligar, MBA

CONTACT

5182219417; ashutosh.yaligar@zbeats.co

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 22, 2023
• First Posted: August 28, 2023
• Study Start: August 30, 2023
• Primary Completion: May 1, 2024
• Study Completion: May 1, 2024
• Last Updated: September 26, 2023

Record last verified: 2023-09

Locations

US (1)