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NCT Number: NCT06647914

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

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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Key information

Conditions

Atrial Fibrillation (AF) Arrhythmias, Cardiac Atrial Fibrillation Cardiovascular Diseases Heart Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

  • Age below 18 years old
  • Refusal to sign consent
  • Noncompliance with the study protocol

Treatment and study plan

Primary outcomes

  1. A composite clinical point used to evaluate the Atrial Fibrosis Score prognostic capability

    Time frame: Partecipants will be assessed at the baseline and at 6 months and 12 months time points. The AFS Prediction Model Testing will start at 9 months after the beginning of the patients enrollment.

    The clinical endpoint will be composed by rehospitalization, cardiac morbidity and total mortality data

Study contacts

Contact information is provided by the study sponsor or research team.

Carlo Pappone, MD, PHD, FACC

CONTACT

[email protected]

+39-0252774260 ext. +390252774260

Sponsors and collaborators

Lead sponsor

IRCCS Policlinico S. Donato

Other

Collaborators

  • Federico II University
  • Irccs Sdn
  • Marche Polytechnic university, Ancona, Italy

Registry information

Acronym: PAMP FA

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Oct 18, 2024
Registry last updated
Oct 18, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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