Predicting Disease Progression in Atrial Fibrillation: A Multiparametric Approach for Prognostic Marker Identification and Personalized Patient Management

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

322 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-09-03

Study Completion Date

2026-08-31

Brief Summary

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This project leverages artificial intelligence (AI) to decipher Atrial Fibrillation (AF) progression and optimize treatment strategies. By recruiting a diverse cohort of 322 AF patients, we will gather a robust multiparametric dataset including clinical, genetic, electrocardiographic, and echocardiographic data. Harnessing AI, we will extract and correlate hidden components within ECG-obtained P-wave data and echocardiographic studies with atrial fibrosis, culminating in an atrial fibrosis score (AFS). The AFS will non-invasively predict fibrosis extent and AF clinical progression, including metrics like rehospitalization, cardiac morbidity, and mortality. Ultimately, this endeavor aims to improve AF patient management, significantly reducing healthcare costs, and enhancing patient quality of life.

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Detailed Description

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Conditions

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Atrial Fibrillation (AF)

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

* History of paroxysmal or persistent atrial fibrillation
* Clinical indication for Atrial Fibrosis (AF) ablation according to the 2020 ESC Guidelines