Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models.
Caroline H RoneyIain SimJin YuMarianne BeachArihant MehtaJosé Alonso Solís-LemusIrum KotadiaJohn WhitakerCesare CorradoOrod RazeghiEdward J VigmondSanjiv M NarayanMark D O'NeillSteven E WilliamsSteven A NiedererPublished in: Circulation. Arrhythmia and electrophysiology (2022)
A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation.
Keyphrases
- left atrial
- atrial fibrillation
- catheter ablation
- oral anticoagulants
- mitral valve
- left atrial appendage
- end stage renal disease
- left ventricular
- direct oral anticoagulants
- chronic kidney disease
- ejection fraction
- heart failure
- newly diagnosed
- electronic health record
- percutaneous coronary intervention
- liver failure
- prognostic factors
- respiratory failure
- data analysis
- venous thromboembolism
- acute coronary syndrome
- coronary artery disease
- artificial intelligence
- patient reported
- radiofrequency ablation