Beta© 2026 Adaptrex, LLC
Beta© 2026 Adaptrex, LLC
Federal grant · project grant (b)
Novel, Automated Mobile Heart Rhythm Analysis Technology to Start Antiarrhythmic Medications Safely at Home - Project Abstract This Proposal Provides Additional Objectives to Advance the Research and Entrepreneurial Skills of the Postdoctoral Candidate and the Parent Award, Which Aims to Improve Cardiovascular Outcomes and Quality of Life for Atrial Fibrillation (AF) Patients by Allowing Them to Safely Start Proven Antiarrhythmic Drugs (aads). Af Is the Most Common Heart Rhythm Disorder (>38M Cases Worldwide) and Causes Significant Morbidity and Mortality. Aads Reduce Mortality Unlike Other Af Drugs, But Access Is Restricted by a Three-day Hospitalization Required to Start These Oral Drugs. This Is Due to a Rare (<0.6%) Heart Rhythm Side Effect That May Require Defibrillation and Can Be Avoided by Heart Rhythm Analysis. During a “drug Load” Hospitalization, Corrected QT (QTC) Intervals From Patient Electrocardiograms (ECGS) Are Manually Measured and Tracked, as QTC Changes Predict Adverse Responses to Aads. for Af Patients at Low Risk of Proarrhythmia, the Cost and Risks of Hospitalization May Outweigh the Benefits. Safebeat Developed the First Machine Learning Algorithm to Produce Visually Verifiable QTC Measurements for Any Ecg Format, Enabling Easy Review or Modification From a Mobile Phone. the High QTC Accuracy (5±7 MS) Enabled the Algorithm to Recommend Aad Dosing With 95% Accuracy Compared to Physicians. This Proposal Is Supported by Cardiac Monitoring Device Manufacturers, Hospital Systems, and Key Opinion Leaders in Cardiology Due to Significant Cost Savings, Improved Long-term Af Monitoring, and Expanded Aad Access. the Goals of This Proposal Are Facilitated by 1) Assessment of Patient and Provider Interaction for Refinement of the Safebeat Platform, 2) Evaluate Patient and Ecg Characteristics as Predictors of Pharmacological Cardioversion Using Aad Therapy to Determine the Best Candidates for Use of the Platform, and 3) Perform Longitudinal Follow Up of Study Participants for Usability and Efficacy Data.
Committed
$2.0 Million
Paid out
$1.8M
88%
Committed, not yet paid
$232.6K
12%
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