Predicting adverse outcomes following catheter ablation treatment for atrial fibrillation
Juan C. Quiroz, David Brieger, Louisa Jorm, Raymond W Sy, Benjumin, Hsu, Blanca Gallego

TL;DR
This study develops and evaluates survival models to predict adverse outcomes after catheter ablation for atrial fibrillation, finding high accuracy for composite events but limited prediction for major bleeding.
Contribution
Introduces prognostic models using linked health data to predict post-ablation adverse outcomes, highlighting the potential for clinical risk management.
Findings
Models accurately predict composite adverse outcomes with concordance > 0.79.
Models poorly predict major bleeding events with concordance < 0.66.
Key predictors include comorbidities, age, and specific therapies.
Abstract
Objective: To develop prognostic survival models for predicting adverse outcomes after catheter ablation treatment for non-valvular atrial fibrillation (AF). Methods: We used a linked dataset including hospital administrative data, prescription medicine claims, emergency department presentations, and death registrations of patients in New South Wales, Australia. The cohort included patients who received catheter ablation for AF. Traditional and deep survival models were trained to predict major bleeding events and a composite of heart failure, stroke, cardiac arrest, and death. Results: Out of a total of 3285 patients in the cohort, 177 (5.3%) experienced the composite outcome (heart failure, stroke, cardiac arrest, death) and 167 (5.1%) experienced major bleeding events after catheter ablation treatment. Models predicting the composite outcome had high risk discrimination accuracy,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAtrial Fibrillation Management and Outcomes · Health Systems, Economic Evaluations, Quality of Life · Pharmaceutical Economics and Policy
