Using Machine Learning to Enhance Prediction of Atrial Fibrillation Recurrence after Catheter Ablation

Abstract

Background: Traditional prognostic models for atrial fibrillation (AF) recurrence following catheter ablation utilize readily-available clinical and echocardiographic variables. This study generates a recurrence risk model using clinical variables combined with left atrial volume index (LAVi) derived from cardiac computed tomography (cCT) by means of a novel deep learning algorithm.

Methods: The retrospective study included 653 consecutive patients who underwent catheter ablation. All patients underwent pre-ablation cCT, and LAVi was computed using a novel deep learning algorithm. Clinical data including early recurrence (within three months) and late recurrence (after 3 months) was obtained.

Results: The most important variables contributing to late recurrence by RSF analysis included LAVi and early recurrence. In total, 5 covariates were identified as independent predictors of late recurrence in the multivariable regression: LAVi [HR 1.01 (1.01-1.02); p<0.001], early recurrence [HR 2.42 (1.90-3.09); p<0.001], statin use [HR 1.38 (1.09-1.75); p=0.007], beta blocker use [HR 1.29 (1.01-1.65); p=0.043], and adjunctive cavotricuspid isthmus ablation for atrial flutter [HR 0.74 (0.57-0.96); p=0.02]. Survival analysis revealed that patients with both LAVi >66.7 cm3/m2 and early recurrence had the highest late recurrence risk as compared to patients with LAVi <66.7 cm3/m2 and no early recurrence [HR 4.52 (3.36-6.08), p<0.001)].

Conclusions: Deep learning-derived, full volumetric LAVi from cCT was the most important pre-procedural variable for predicting late AF recurrence following catheter ablation. The combination of increased LAVi and early recurrence confers more than a four-fold increased risk of late recurrence.  Thus, machine learning methods can help risk stratify patients undergoing AF catheter ablation.

Note:

Funding Information: MA and SEP acknowledge support from the CAP-AI programme (led by Capital Enterprise in partnership with Barts Health NHS Trust and Digital Catapult and funded by the European Regional Development Fund and Barts Charity) and Health Data Research UK (HDR UK—an initiative funded by UK Research and Innovation, Department of Health and Social Care (England) and the devolved administrations, and leading medical research charities; www.hdruk.ac.uk). SEP acknowledges support from the National Institute for Health Research (NIHR) Biomedical Research Centre at Barts, from the SmartHeart EPSRC programme grant (www.nihr.ac.uk; EP/P001009/1) and the London Medical Imaging and AI Center for Value Based Healthcare. SEP has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 825903 (euCanSHare project).

Declaration of Interests: SEP provides consultancy to and owns stock of Circle Cardiovascular Imaging Inc., Calgary, Alberta, Canada. No other competing interests.

Ethics Approval Statement: This study was approved by Georgetown-Medstar Institutional Review Board with waiver of informed consent.

Keywords: atrial fibrillation, catheter ablation, recurrence risk, left atrial volume index, Cardiac computed tomography, machine learning