Prediction of thrombosis in atrial fibrillation with compact atrial shape representation. (19th May 2022)

• Record Type: Journal Article
• Authors: Harrison, J; Lorenzi, M; Legghe, B; Iriart, X; Cochet, H; Sermesant, M
• Is Part Of: Europace. Volume 24:Supplement 1(2022)
• Publication Date: 2022-05-19
• Subjects: Arrhythmia -- Treatment -- Periodicals; Cardiac pacing -- Periodicals; Catheter ablation -- Periodicals; Heart -- Physiology -- Periodicals; Electrophysiology -- Periodicals; 617.4120645
• DOI: 10.1093/europace/euac053.562
• Languages: English
• ISSNs: 1099-5129

Abstract

Abstract: Funding Acknowledgements: Type of funding sources: Public grant(s) – EU funding. Main funding source(s): IHU LIRYC ANR-10-IAHU-04 ERACoSysMed PARIS Project Background: The impact of LA shape on clot formation in AF is yet to be understood Purpose: To analyze the impact of LA, PVs and LAA orientations on clot formation Methods: Pts with history of AF and an indication for LAA closure underwent pre-operative cardiac CT. Those with either a prior history of embolism or a clot detected on arterial and venous-enhanced CT images were categorized as clot positive. On CT images, the LA was segmented and meshed. Automated labelling of PVs and LAA was employed to build a compact skeleton representation of the LA (by registering labels from a template to all cases, and by connecting the center of mass of each labels). LA skeletons were used to train a novel Neural Network model for joint classification of heterogeneous data, such as images taken at different phases of the cardiac cycle. Such model consists of one encoder per dataset followed by a common classifier, enforcing a joint representation of all datasets in the same latent space. This is made possible by writing the task as a variational problem to optimise, we call this model a multi-channel variational classifier. The model was trained to predict clot positive patients and its generalizability was assessed on a test population. Results: 237 pts were included (age 74±8, 70% males, CHADS-VASc 4.3±1.2). CTs were acquired during systole in 117 and diastole in 120. 100 (42%) patients were considered clot positive. Segmentation, labeling and skeletization was successfully achieved in all pts. The model was trained using a 10-Fold cross validation. We compared the results after training on diastole and systole cases independently and by jointly training on both datasets. At testing, our model reached a 0.72 overall accuracy for the prediction of LAA clots (0.83 for systole and 0.61 for diastole), while training separately on systole and diastole was prone to strong overfitting and mode collapse with 0.52 and 0.48 accuracy, respectively. Conclusion: While LA shape, LA size, PV and LAA orientations alone may not be sufficient for robust clot prediction, we introduce a compressed representation of the global anatomy that closely relates to LAA thrombosis, possibly identifying global features related to adverse hemodynamics. The method is reproducible and introduces a novel approach to accommodate heterogeneous diastole and systole datasets.