Predicting Disease Progression in Atrial Fibrillation: A Multiparametric Approach for Prognostic Marker Identification and Personalized Patient Management
- Conditions
- Atrial Fibrillation (AF)
- Registration Number
- NCT06647914
- Lead Sponsor
- IRCCS Policlinico S. Donato
- Brief 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.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 322
- History of paroxysmal or persistent atrial fibrillation
- Clinical indication for Atrial Fibrosis (AF) ablation according to the 2020 ESC Guidelines
- Age below 18 years old
- Refusal to sign consent
- Noncompliance with the study protocol
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method A composite clinical point used to evaluate the Atrial Fibrosis Score prognostic capability 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
- Secondary Outcome Measures
Name Time Method