Atrial Fibrillation Risk Estimation With Single-lead Handheld Electrocardiograms

NCT07468123 | Enrolling By Invitation

• 1 other identifier: 2024P002767
• Study Type: interventional
• Target: 200
• Locations: 1 country
• Sites: 1

Brief Summary

The goal of this prospective, non-randomized pilot study is to learn whether predictions from a previously validated 12-lead ECG-based artificial intelligence (AI) algorithm (ECG-AI) identify people more likely to have undiagnosed atrial fibrillation (AF). The main questions it aims to answer are: Do people predicted to have high risk of AF using ECG-AI have a higher rate of new AF diagnosis using 1L ECG screening compared with people predicted to have a low risk? Do AI-based AF risk estimates from the 12-lead ECG correlate with AF risk estimates from the 1L ECG? Do people find 1L ECG screening for AF acceptable and useful? Participants will: Undergo screening with 1L ECG mailed to their home Complete a survey assessing attitudes toward 1L ECG screening Complete a 14-day patch monitor on 1 or 2 occasions depending on 1L ECG results

Conditions

Atrial Fibrillation (AF)

Keywords

atrial fibrillation; digital health; screening

Outcome Measures

Primary Outcomes (3)

• New AF diagnosis (%)

  Rate of new AF diagnosis

  1 year

• Acceptability and usefulness

  Survey-based acceptability and usefulness of 1L ECG screening process

  0

• AI-based AF risk correlation

  Correlation between 12-lead ECG-based AF risk and 1L ECG-based AF risk using AI model

  0

Study Arms (2)

Low-risk

ACTIVE COMPARATOR

Low estimated risk for AF (\<1% 1-year AF risk)

Diagnostic Test: 1L ECG screening; Diagnostic Test: Patch monitor

High-risk

ACTIVE COMPARATOR

Low estimated risk for AF (\>10% 1-year AF risk)

Diagnostic Test: 1L ECG screening; Diagnostic Test: Patch monitor

Interventions

1L ECG screening DIAGNOSTIC_TEST

Individuals will undergo 1L ECG screening using the AliveCor KardiaMobile 1L ECG device

High-risk; Low-risk

Patch monitor DIAGNOSTIC_TEST

Individuals who are found to have evidence of AF on 1L ECG will undergo assessment with 14-day patch monitor at the time of initial screen. Otherwise all study participants will undergo 14-day patch monitor at the 1-year timepoint.

High-risk; Low-risk

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Men and women aged 50-90 who are new or established patients in an MGH primary care or ambulatory cardiology practice
• Willing to provide consent to participate in the study to access data from electronic health records (EHR)
• At least 1 12-lead ECG obtained within 5 years prior to study start date for AF risk estimation
• Have access to a smart phone or tablet to use with the AliveCor KardiaMobile 1L ECG device

You may not qualify if:

• History of atrial fibrillation or atrial flutter as documented in the patient's current electronic health record medical problem list or self-reported diagnosis
• Implanted cardiac devices (pacemakers, implantable cardiac defibrillators, or cardiac resynchronization therapy, and implantable loop recorders)
• History of allergy to adhesive

Sponsors & Collaborators

• Massachusetts General Hospital: lead

Study Sites (1)

Mass General Brigham

Boston, Massachusetts, 02114, United States

Location

Related Publications (2)

• Khurshid S, Friedman SF, Al-Alusi MA, Kany S, Sommers T, Anderson CD, Ho JE, McManus DD, Borowsky LH, Ashburner JM, Lubitz SA, Atlas SJ, Maddah M, Singer DE, Ellinor PT. Artificial intelligence-enabled analysis of handheld single-lead electrocardiograms to predict incident atrial fibrillation: an analysis of the VITAL-AF randomized trial. NPJ Digit Med. 2025 Nov 26;8(1):776. doi: 10.1038/s41746-025-02164-2.

  PMID: 41299008 BACKGROUND

• Khurshid S, Friedman S, Reeder C, Di Achille P, Diamant N, Singh P, Harrington LX, Wang X, Al-Alusi MA, Sarma G, Foulkes AS, Ellinor PT, Anderson CD, Ho JE, Philippakis AA, Batra P, Lubitz SA. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation. 2022 Jan 11;145(2):122-133. doi: 10.1161/CIRCULATIONAHA.121.057480. Epub 2021 Nov 8.

  PMID: 34743566 BACKGROUND

MeSH Terms

Conditions

Atrial Fibrillation

Condition Hierarchy (Ancestors)

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

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor of Medicine

Model Details: High-risk versus low-risk comparison

Study Record Dates

• First Submitted: March 9, 2026
• First Posted: March 12, 2026
• Study Start: July 30, 2025
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: March 12, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)