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

NCT06647914 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 322

Last updated 2024-10-18

No results posted yet for this study

Summary

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.

Conditions

  • Atrial Fibrillation (AF)

Sponsors & Collaborators

  • Federico II University

    collaborator OTHER

  • Irccs Sdn

    collaborator OTHER

  • Marche Polytechnic university, Ancona, Italy

    collaborator UNKNOWN

  • IRCCS Policlinico S. Donato

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-09-03
Primary Completion
2026-03-01
Completion
2026-08-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06647914 on ClinicalTrials.gov