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Machine Learning and Artificial Intelligence for Early Detection of Stroke and Atrial Fibrillation

Machine Learning and Artificial Intelligence for Early Detection of Stroke and Atrial Fibrillation - MAESTRIA-AFNET 10

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
NL-OMON
Registry ID
NL-OMON56279
Enrollment
150
Registered
2023-12-08
Start date
2024-04-12
Completion date
Unknown
Last updated
2024-12-02

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

Conditions

atrial fibrilation

Interventions

None listed

Sponsors

Kompetenznetz Vorhofflimmern e.V./Atrial Fibrillation NETwork (AFNET)
Lead Sponsor

Eligibility

Age
18 Years to 99 Years

Inclusion criteria

Inclusion criteria: 1. Patients with paroxysmal AF (clinically defined as AF episodes less than one week), or patients with persistent AF (clinically defined as AF episodes longer than one week), or patients with permanent AF (no documented sinus rhythm or possibility to restore sinus rhythm by any means). 2. Patient (or legally acceptable representative if applicable) provides written Informed Consent to participate in the study. The patient has the option to give separate consent to donate extra volume of blood during routine blood collection, that can be used for biomedical research. 3. Patient is at least 18 years of age. 4. Patient must own a Smartphone with Apple iOS Version 14.5 (or higher) or with Android Version 8.0 (or higher).

Exclusion criteria

Exclusion criteria: 1. Any disease that limits life expectancy to less than 1 year. 2. All persons unable to provide informed consent. 3. All persons exempt from participation in a study or trial by law. 4. Any medical or psychiatric condition which, in the Investigator*s opinion, would preclude the participant from adhering to the protocol or completing the study per protocol.

Design outcomes

Primary

MeasureTime frame
The general objectives of the MAESTRIA-AFNET 10 study on clinical epidemiology and medical management of atrial fibrillation (AF) are summarized as follows: - Enrolment of a representative cross-section of AF patients in Europe. - Detailed analysis of clinical and relevant parameters (digitalised ECG, cardiac imaging, blood biomarkers) that could be used during clinical practise for the diagnosis of atrial cardiomyopathy and patient*s outcome. - The data sets will be assessed using Artificial Intelligence (AI) algorithms to characterise specific subgroups of AF or define novel outcome predictors.

Secondary

MeasureTime frame
Potential Outcome Parameters * AA burden and vascular stiffness (measured by Preventicus Heartbeats and a wearable with photoplethysmographic -PPG- sensor to be coupled with a smartphone for continuous heart rhythm monitoring for 12 months). * MoCA Cognitive function test. * EQ-5D-5L Quality of Life questionnaire. * Ischaemic events (systemic, myocardial and cerebral) at 12 months. * Clinically relevant changes in CT/MRI for patients in which CT or MRI is clinically indicated. - ECG analysis - All variables from ECGs, CTs, MRIs & echos will be integrated for final assessment.

Countries

Netherlands

Outcome results

None listed

Source: NL-OMON (via WHO ICTRP)