  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, with the best model having a concordance index > 0.79 at the
evaluated time horizons. Models for predicting major bleeding events had poor
risk discrimination performance, with all models having a concordance index <
0.66. The most impactful features for the models predicting higher risk were
comorbidities indicative of poor health, older age, and therapies commonly used
in sicker patients to treat heart failure and AF.
  Conclusions: Diagnosis and medication history did not contain sufficient
information for precise risk prediction of experiencing major bleeding events.
The models for predicting the composite outcome have the potential to enable
clinicians to identify and manage high-risk patients following catheter
ablation proactively. Future research is needed to validate the usefulness of
these models in clinical practice.
