Atrial Fibrillation (AF)
Conditions
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.
Interventions
None listed
Sponsors
Federico II University
Irccs Sdn
Marche Polytechnic university, Ancona, Italy
IRCCS Policlinico S. Donato
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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 |
Contacts
Primary ContactCarlo Pappone, MD, PHD, FACC
Outcome results
None listed