Artificial Intelligence Guided Patient Selection for Atrial Fibrillation Catheter Ablation: Randomized Clinical Trial (AI-PAFA Trial)

NCT ID: NCT04997824

Last Updated: 2023-05-24

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 1000 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2021-10-07
• Study Completion Date: 2031-06-30

Brief Summary

Atrial fibrillation (AF) is a major cardiovascular disease with a prevalence of 1.7% of the total population in Korea, associated with 25% of ischemic stroke and 30% of heart failure, and is a major cardiovascular disease that doubles the risk of dementia. AF catheter ablation (AFCA) is an effective procedure that lowers the risk of heart failure mortality and cerebral infarction and improves cognitive or renal functions. However, the recurrence rate after the procedure is relatively high, especially in patients with long-standing persistent AF in which atrial remodeling has already progressed. Research on the prediction of treatment efficacy using artificial intelligence (AI) is being actively conducted around the world. We predicted the AFCA poor responders who will progress to permanent AF despite AFCA among a total of 3,372 patients included in the Yonsei AF Ablation cohort and the 2nd independent cohort with a long-term follow-up through AI with area under curve (AUC) 0.943. Therefore, in this prospective randomized clinical study, the difference between the patient selection for AFCA using AI algorithm and the clinical guidelines-based decision will be compared and evaluated in terms of long-term rhythm outcome.

Detailed Description

Study design

1. Prospective randomization (AI-based patient selection group vs. conventional guidelines-based patient selection group)

2. Target number of subjects 1000 patients (500 people in each group)

3. Rhythm follow-up schedule: 2012 ACC/AHA/ESC guidelines (basal, 3 months, then every 6 months Holter, ECG when symptomatic)

4. Anticoagulant therapy follows 2020 ESC guidelines.

5. Evaluation of all adverse events occurring in each group and comparative evaluation of hospitalization rates, major cardiovascular attacks, and mortality

Process of Patient Selection A guideline-based appropriate candidate for AFCA Randomization for AI-guide group vs. Clinical guideline-based group Poor responder selection by AI at the outpatient clinic AI-prediction outcomes should be noticed in AI-guided groups, but not in the clinical guideline-based group.

Recommendation of rate control for AI-predicted poor responders All-comer ablation in guideline-based group

Progress and rhythm/ECG tracking

1. Implemented according to 2012 American College of Cardiology(ACC)/American Heart Association(AHA)/Heart Rhythm Society(HRS) guidelines for AF management

2. Outpatient follow-up 1 to 2 weeks after the start of the procedure or drug treatment

3. Follow-up every 3 months after the procedure, and every 6 months thereafter

4. If the patient complains of arrhythmia symptoms, conduct an electrocardiogram at any time and follow the rhythm with a Holter or an event recorder.

Conditions

Atrial Fibrillation

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: TREATMENT
• Blinding Strategy: NONE

Study Groups

Artificial Intelligence-based atrial fibrillation catheter ablation

catheter ablation

Group Type: EXPERIMENTAL

Artificial Intelligence-based atrial fibrillation catheter ablation

Intervention Type: PROCEDURE

1. Patient selection for Atrial Fibrillation Catheter Ablation(AFCA)

2. AI-guided prediction for poor responder of AFCA in the outpatient clinic

3. Sharing information on the AI prediction with the patient and family member

4. AFCA after consent to the procedure

5. Pulmonary vein isolation and additional treatment for non-pulmonary vein triggers

6. Monitoring esophageal temperature

7. Evaluation of Procedure and ablation time, and complication afer procedure

8. Post-procedure rhythm follow-up is carried out according to the study design above.

Medical Therapy

catheter ablation

Group Type: ACTIVE_COMPARATOR

Medical Therapy

Intervention Type: PROCEDURE

1. Patient selection for Atrial Fibrillation Catheter Ablation(AFCA)

2. Decision for AFCA based on clinical guidelines and the experience of the attending physician

3. AFCA after consent to the procedure

4. Pulmonary vein isolation and additional treatment for non-pulmonary vein triggers

5. Monitoring esophageal temperature

6. Evaluation of Procedure and ablation time, and complication afer procedure

Interventions

Artificial Intelligence-based atrial fibrillation catheter ablation

1. Patient selection for Atrial Fibrillation Catheter Ablation(AFCA)

2. AI-guided prediction for poor responder of AFCA in the outpatient clinic

3. Sharing information on the AI prediction with the patient and family member

4. AFCA after consent to the procedure

5. Pulmonary vein isolation and additional treatment for non-pulmonary vein triggers

6. Monitoring esophageal temperature

7. Evaluation of Procedure and ablation time, and complication afer procedure

8. Post-procedure rhythm follow-up is carried out according to the study design above.

Intervention Type: PROCEDURE

Medical Therapy

1. Patient selection for Atrial Fibrillation Catheter Ablation(AFCA)

2. Decision for AFCA based on clinical guidelines and the experience of the attending physician

3. AFCA after consent to the procedure

4. Pulmonary vein isolation and additional treatment for non-pulmonary vein triggers

5. Monitoring esophageal temperature

6. Evaluation of Procedure and ablation time, and complication afer procedure

Intervention Type: PROCEDURE

Eligibility Criteria

Inclusion Criteria

1. Left atrium size < 55 mm

2. AF recurred during the administration of antiarrhythmic drugs or that antiarrhythmic drugs intolerable patients

3. Patients eligible for anticoagulant therapy (to prevent thromboembolic events)

Exclusion Criteria

1. AF associated with severe cardiac anomalies or structural heart disease with hemodynamic influence

2. Patients who have difficulty in CT imaging using a contrast medium

3. Patients with active internal bleeding

4. Inappropriate anticoagulant therapy

5. Serious comorbidities

6. Patients expected to survive less than 1 year

7. People with drug or alcohol addiction

8. Those who cannot read the consent form (illiterate, foreigners, etc.)

9. Patients who are judged unsuitable for participation in clinical research by the judgment of other investigators

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Yonsei University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Hui-Nam Pak

Role: PRINCIPAL_INVESTIGATOR

Severance Hospital, Yonsei University Health System

Locations

Severance Hospital, Yonsei University Health System

Seoul, , South Korea

Site Status: RECRUITING

Countries

South Korea

Central Contacts

Hui-Nam Pak

Role: CONTACT

hnpak@yuhs.ac

82-2-2228-8459

Facility Contacts

Hui-Nam Pak

Role: primary

hnpak@yuhs.ac

v

Other Identifiers

4-2021-0608

Identifier Type: -

Identifier Source: org_study_id