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.

Objectives: To develop prognosis prediction models for COVID-19 patients attending an emergency department (ED) based on initial chest X-ray (CXR), demographics, clinical and laboratory parameters.Methods: All symptomatic con rmed COVID-19 patients admitted to our hospital ED between February 24th and April 24th 2020 were recruited. CXR features, clinical and laboratory variables and CXR abnormality indices extracted by a convolutional neural network (CNN) diagnostic tool were considered potential predictors on this rst visit. The most serious individual outcome de ned the three severity level: 0) home discharge or hospitalization ≤ 3 days, 1) hospital stay >3 days and 2) intensive care requirement or death. Severity and in-hospital mortality multivariable prediction models were developed and internally validated. The Youden index was used for model selection.Results: A total of 440 patients were enrolled (median 64 years; 55.9% male); 13.6% patients were discharged, 64% hospitalized, 6.6% required intensive care and 15.7% died. The severity prediction model included oxygen saturation/inspired oxygen fraction (SatO2/FiO2), age, C-reactive protein (CRP), lymphocyte count, extent score of lung involvement on CXR (ExtScoreCXR), lactate dehydrogenase (LDH), D-dimer level and platelets count, with AUC-ROC=0.94 and AUC-PRC=0.88. The mortality prediction model included age, SatO2/FiO2, CRP, LDH, CXR extent score, lymphocyte count and D-dimer level, with AUC-ROC=0.97 and AUC-PRC=0.78. The addition of CXR CNN-based indices slightly improved the predictive metrics for mortality (AUC-ROC=0.97 and AUC-PRC=0.83). Conclusion:The developed and internally validated severity and mortality prediction models could be useful as triage tools for COVID-19 patients and they should be further validated at different ED.

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