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Background: Patients who have undergone the Fontan procedure are at risk of developing hepatic dysfunction. However, broad recommendations regarding liver monitoring are limited. The purpose of this study was to characterize the frequency of liver disease in adult Fontan patients using multimodality imaging (hepatic magnetic resonance imaging [MRI], acoustic radiation force impulse [ARFI] elastography, or hepatic ultrasound). Methods: In a prospective cross-sectional analysis of adult patients palliated with a Fontan procedure, hepatic MRI, ARFI, and hepatic ultrasound were used to assess for liver disease. The protocol compared (1) varying prevalence of liver disease based on each imaging technique, (2) agreement between different techniques, and (3) association between noninvasive imaging diagnosis of liver disease and clinical variables, including specific liver disease biomarkers. Results: Thirty-seven patients were enrolled. The ARFI results showed high wave propagation velocity in 35 patients (94.6%). All patients had some abnormality in the hepatic MRI. Specifically, 8 patients (21.6%) showed signs of chronic liver disease, 10 patients (27%) had significant liver fibrosis, and 27 patients (73%) had congestion. No correlation was found between liver stiffness measured as propagation velocity and hepatic MRI findings. Only 7 patients had an abnormal hepatic ultrasound study. Conclusions: There is an inherent liver injury in adult Fontan patients. Signs of liver disease were observed in most patients by both hepatic MRI and ARFI elastography but not by ultrasound imaging. Increased liver stiffness did not identify specific disease patterns from MRI, supporting the need for multimodality imaging to characterize liver disease in Fontan patients.

[EN] Background: Diagnostic Task Force Criteria (TFC) for arrhythmogenic cardiomyopathy (AC) exhibit poor performance for left dominant forms. TFC only include right ventricular (RV) dysfunction (akinesia, dyssynchrony, volumes and ejection fraction). Moreover, cardiac magnetic resonance imaging (CMRI) assessment of left ventricular (LV) dyssynchrony has hitherto not been described. Thus, we aimed to comprehensively characterize LVCMRI behavior in AC patients. Methods: Thirty-five AC patients with LV involvement and twenty-three non-affected family members (controls) were enrolled. Feature-tracking analysis was applied to cine CMRI to assess LV ejection fraction (LVEF), LV end-systolic and end-diastolic volume indexes, strain values and dyssynchrony. Regions with more frequent strain and dyssynchrony impairment were also studied. Results: Radial dyssynchrony and LVEF were selected (sensitivities 54.3% and 48.6%, respectively at 100% specificity), with a threshold of 70 ms for radial dyssynchrony and 48.5% for LVEF. 71.4% of patients exceeded these thresholds (31.4% both, 22.9% only dyssynchrony and 17.1% only LVEF). Considering these cut-off values as a novel combined criterion, 30% of patients with 'borderline' or 'possible' AC following 2010 TFC would move to a 'definite' AC diagnosis. Strain was globally impaired whereas dyssynchronous regions were more often apical and located at the inferolateral wall. Conclusions: Mirroring the RV evaluation, we suggest including LVEF and LV dyssynchrony to improve the diagnosis of AC. Two independent mechanisms can be claimed in AC patients with LV involvement: 1) decreased myocardial deformation with global LV affectation and 2) delayed myocardial contraction at localized regions., This work was supported by grants from the "Ministerio de Economia y Competitividad" [DPI2015-70821-R], "Instituto de Salud Carlos III" and FEDER "Union Europea, Una forma de hacer Europa" [RD12/0042/0029, PI14/01477, PI18/01582 and La Fe Biobank PT17/0015/0043].

[EN] Background and objective: A heterogenous expression characterizes arrhythmogenic cardiomyopathy (AC). The evaluation of regional wall movement included in the current Task Force Criteria is only qualitative and restricted to the right ventricle. However, a strain-based approach could precisely quantify myocardial deformation in both ventricles. We aim to define and modelize the strain behavior of the left ventricle in AC patients with left ventricular (LV) involvement by applying algorithms such as Principal Component Analysis (PCA), clustering and naive Bayes (NB) classifiers. Methods: Thirty-six AC patients with LV involvement and twenty-three non-affected family members (controls) were enrolled. Feature-tracking analysis was applied to cine cardiac magnetic resonance imaging to assess strain time series from a 3D approach, to which PCA was applied. A Two-Step clustering algorithm separated the patients' group into clusters according to their level of LV strain impairment. A statistical characterization between controls and the new AC subgroups was done. Finally, a NB classifier was built and new data from a small evolutive dataset was predicted. Results: 60% of AC-LV patients showed mildly affected strain and 40% severely affected strain. Both groups and controls exhibited statistically significant differences, especially when comparing controls and severely affected AC-LV patients. The classification accuracy of the strain NB classifier reached 82.76%. The model performance was as good as to classify the individuals with a 100% sensitivity and specificity for severely impaired strain patients, 85.7% and 81.1% for mildly impaired strain patients, and 69.9% and 91.4% for normal strain, respectively. Even when the severely affected LV-AC group was excluded, LV strain showed a good accuracy to differentiate patients and controls. The prediction of the evolutive dataset revealed a progressive alteration of strain in time. Conclusions: Our LV strain classification model may help to identify AC patients with LV involvement, at least in a setting of a high pretest probability, such as family screening., This work was supported by grants from the "Ministerio de Economia y Competitividad"[DPI2015-70821-R], "Instituto de Salud Carlos III " and FEDER "Union Europea, Una forma de hacer Europa"[PI14/01477, PI15/00748, PI18/01582, CIBERCV] and La Fe Biobank [PT17/0 015/0043].