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Aims Right ventricular (RV) function assessment is crucial in congenital heart disease patients, especially in atrial septal defect (ASD) and repaired Tetralogy of Fallot (TOF) patients with pulmonary regurgitation (PR). In this study, we aimed to analyse both 3D RV shape and deformation to better characterize RV function in ASD and TOF-PR. Methods and results We prospectively included 110 patients (≥16 years old) into this case–control study: 27 ASD patients, 28 with TOF, and 55 sex- and age-matched healthy controls. Endocardial tracking was performed on 3D transthoracic RV echocardiographic sequences and output RV meshes were post-processed to extract local curvature and deformation. Differences in shape and deformation patterns between subgroups were quantified both globally and locally. Curvature highlights differences in RV shape between controls and patients while ASD and TOF-PR patients are similar. Conversely, strain highlights differences between controls and TOF-PR patients while ASD and controls are similar [global area strain: −31.5 ± 5.8% (controls), −34.1 ± 7.9% (ASD), −24.8 ± 5.7% (TOF-PR), P Conclusion Chronic RV volume loading results in similar RV shape remodelling in both ASD and TOF patients while strain analysis demonstrated that RV strain is only reduced in the TOF group. This suggests a fundamentally different RV remodelling process between both conditions.

International audience; Right heart catheterisation is considered as the gold standard for the assessment of patients with suspected pulmonary hyper-tension. It provides clinicians with meaningful data, such as pulmonary capillary wedge pressure and pulmonary vascular resistance, however its usage is limited due to its invasive nature. Non-invasive alternatives, like Doppler echocardiography could present insightful measurements of right heart but lack detailed information related to pulmonary vascu-lature. In order to explore non-invasive means, we studied a dataset of 95 pulmonary hypertension patients, which includes measurements from echocardiography and from right-heart catheterisation. We used data extracted from echocardiography to conduct cardiac circulation model per-sonalisation and tested its prediction power of catheter data. Standard machine learning methods were also investigated for pulmonary artery pressure prediction. Our preliminary results demonstrated the potential prediction power of both data-driven and model-based approaches.