This clinical trial focuses on testing the efficacy of different digital interventions to promote re-engagement in cancer-related long-term follow-up care for adolescent and young adult (AYA) survivors of childhood cancer.
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.
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
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
| Inclusion Criteria | Exclusion Criteria |
|---|---|
|
|
Sponsor: ZBeats INC
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.