The use of artificial intelligence and mobile health technologies to identify patients at the highest risk of atrial fibrillation (irregular and often abnormally fast heart rate)

Application of machine learning algorithm to identify patients at highest risk of atrial fibrillation for targeted screening

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ISRCTN

Registry ID

ISRCTN17993837

Enrollment

1800

Registered

2021-11-22

Start date

2022-04-11

Completion date

Unknown

Last updated

2022-04-25

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Atrial fibrillation (AF) Circulatory System

Interventions

Current intervention as of 29/11/2021: Participants will be identified using the AF risk prediction machine learning algorithm from the Hounslow primary care networks.

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Current inclusion criteria as of 29/11/2021: 1. Aged 18 years or above 2. Identified as high-risk for AF by our 'AF risk prediction machine learning algorithm 3. Access to smartphone depending on allocated screening group _____ Previous inclusion criteria: 1. Aged 18 years old or above 2. Identified as high-risk for AF by our PULsE AI machine learning algorithm 3. Access to smartphone depending on allocated screening group

Exclusion criteria

Exclusion criteria: 1. Have already a diagnosis of atrial fibrillation prior to study enrolment 2. Below the age of 18 years old 3. Presence of cardiac electronic implantable device

Design outcomes

Primary

Measure	Time frame
Detection of atrial fibrillation with a one-off ECG	—

Secondary

Measure	Time frame
There are no secondary outcome measures	—

Countries

England, United Kingdom

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Feb 4, 2026