Prediction of Atrial Fibrillation Recurrence after Thoracoscopic Surgical Ablation Using Machine Learning Techniques.
Sarah W E BaalmanRicardo R LopesLucas A RamosJolien NeefsAntoine H G DriessenWimJan P van BovenBas A J M de MolHenk A MarqueringJoris R de GrootPublished in: Diagnostics (Basel, Switzerland) (2021)
Thoracoscopic surgical ablation (SA) for atrial fibrillation (AF) has shown to be an effective treatment to restore sinus rhythm in patients with advanced AF. Identifying patients who will not benefit from this procedure would be valuable to improve personalized AF therapy. Machine learning (ML) techniques may assist in the improvement of clinical prediction models for patient selection. The aim of this study is to investigate how available baseline characteristics predict AF recurrence after SA using ML techniques. One-hundred-sixty clinical baseline variables were collected from 446 AF patients undergoing SA in our tertiary referral center. Multiple ML models were trained on five outcome measurements, including either all or a number of key variables selected by using the least absolute shrinkage and selection operator (LASSO). There was no difference in model performance between different ML techniques or outcome measurements. Variable selection significantly improved model performance (AUC: 0.73, 95% CI: 0.68-0.77). Subgroup analysis showed a higher model performance in younger patients (<55 years, AUC: 0.82 vs. >55 years, AUC 0.66). Recurrences of AF after SA can be predicted best when using a selection of baseline characteristics, particularly in young patients.
Keyphrases
- atrial fibrillation
- catheter ablation
- oral anticoagulants
- left atrial
- left atrial appendage
- machine learning
- ejection fraction
- patients undergoing
- direct oral anticoagulants
- heart failure
- newly diagnosed
- primary care
- prognostic factors
- percutaneous coronary intervention
- stem cells
- clinical trial
- venous thromboembolism
- coronary artery disease
- case report
- body composition
- open label
- combination therapy
- phase iii