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International audience; BACKGROUND Markers of left atrial (LA) shape may improve the prediction of postablation outcomes in atrial fibrillation (AF). Correlations to LA volume and AF persistence limit their incremental value over current clinical predictors. OBJECTIVE To develop a shape score independent from AF persistence and LA volume using shape-based statistics, and to test its ability to predict postablation outcome. METHODS Preablation computed tomography (CT) images from 141 patients with paroxysmal (57%) or persistent (43%) AF were segmented. Deformation of an average LA shape into each patient encoded patient-specific shape. Local analysis investigates regional differences between patient groups. Linear regression was used to remove shape variations related to LA volume and AF persistence, and to build a shape score to predict postablation outcome. Cross-validation was performed to evaluate its accuracy. RESULTS Ablation failure rate was 23% over a median 12-month follow-up. Regions associated with ablation failure mostly consisted of a large area on posteroinferior LA, mitral isthmus, and left inferior vein. On univariate analysis, strongest predictors were AF persistence (P 5 .005), LA indexed volume (P 5 .02), and the proposed shape score (P 5 .001). On multivariate analysis, all 3 were independent predictors of ablation failure, with the LA shape score showing the highest predictive value (odds ratio [OR] 5 6.2 [2.5-15.8], P , .001), followed by LA indexed volume (OR 5 3.1 [1.2-7.9], P 5 .019) and AF persistence (OR 5 2.9 [1.2-7.6], P 5 .022). CONCLUSION Posteroinferior LA, mitral isthmus, and left inferior vein are the regions whose shape have the highest impact on outcome. LA shape predicts AF ablation failure independently from, and more accurately than, atrial volume and AF persistence.

International audience; Atrial fibrillation (AF) is a complex cardiac disease impact-ing an ever-growing population and increases 6-fold the risk of thrombusformation. However, image based bio-markers to predict thrombosis inpresence of AF are not well known. This lack of knowledge comes fromthe difficulty to analyse and compare the shape of the Left Atrium (LA)as well as the insufficiency of data that limits the complexity of modelswe can use. Conducting data analysis in cardiology exacerbates the smalldataset problem because the heart cycle renders impossible to compareimages taken at systole and diastole time. To address these issues, wefirst propose a graph representation of the LA, to focus on the impactof pulmonary veins (PV) and LA Appendage (LAA) positions, givinga simple object easy to analyse. Secondly, we propose a meta-learningframework for heterogeneous datasets based on the consistent represen-tation of each dataset in a common latent space. We show that sucha model is analogous to a meta-classifier, where each dataset is charac-terised by specific projection in a common latent space, while sharing thesame separating boundary. We apply this model to the graph represen-tation of the LA and interpret the model to give novel time-dependantbio-markers related to PV and LAA configurations for the prediction ofthrombosis.

Comunicació presentada a: FIMH 2021 11th International Conference, celebrada del 21 al 25 de juny de 2021 a Stanford, CA, USA. Atrial fibrillation (AF) is considered the most common human arrhythmia. Around 99% of thrombi in non-valvular AF are formed in the left atrial appendage (LAA). Studies suggest that abnormal LAA haemodynamics and the subsequently stagnated flow are the factors triggering clot formation. However, the relation between LAA morphology, the blood pattern and the triggering is not fully understood. Moreover, the impact of structures such as the pulmonary veins (PVs) on LA haemodynamics has not been thoroughly studied due to the difficulties of acquiring appropriate data. On the other hand, in-silico studies and flow simulations allow a thorough analysis of haemodynamics, analysing the 4D nature of blood flow patterns under different boundary conditions. However, the reduced number of cases reported on the literature of these studies has been a limitation. The main goal of this work was to study the influence of PVs on left atrium (LA) and LAA haemodynamics. Computational fluid dynamics simulations were run on 52 patients, the largest cohort so far in the literature, where different parameters were individually studied: pulmonary veins orientation and configuration; LAA and LA volumes and its ratio; and flow velocities. Our computational analysis showed how the right pulmonary vein height and angulation have a great influence on LA haemodynamics. Additionally, we found that LAA with great bending with its tip pointing towards the mitral valve could contribute to favour flow stagnation. This work was supported by the Agency for Management of University and Research Grants of the Generalitat de Catalunya under the the Grants for the Contracting of New Research Staff Programme - FI (2020 FI B 00608) and the Spanish Ministry of Economy and Competitiveness under the Programme for the Formation of Doctors (PRE2018-084062), the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) and the Retos Investigaci´on project (RTI2018-101193-B-I00). Additionally, this work was supported by the H2020 EU SimCardioTest project (Digital transformation in Health and Care SC1- DTH-06-2020; grant agreement No. 101016496) and the European project PARIS (ID35).