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Oliviero Riggio,1 Stefania Gioia,1 Ilaria Pentassuglio,1 Valeria Nicoletti,1 Michele Valente,2 Giulia d’Amati2 1Department of Clinical Medicine, Center for the Diagnosis and Treatment of Portal Hypertension, 2Department of Radiological, Oncological, and Pathological Sciences, Sapienza University of Rome, Rome, Italy Abstract: The term idiopathic noncirrhotic portal hypertension (INCPH) has been recently proposed to replace terms, such as hepatoportal sclerosis, idiopathic portal hypertension, incomplete septal cirrhosis, and nodular regenerative hyperplasia, used to describe patients with a hepatic presinusoidal cause of portal hypertension of unknown etiology, characterized by features of portal hypertension (esophageal varices, nonmalignant ascites, porto-venous collaterals), splenomegaly, patent portal, and hepatic veins and no clinical and histological signs of cirrhosis. Physicians should learn to look for this condition in a number of clinical settings, including cryptogenic cirrhosis, a disease known to be associated with INCPH, drug administration, and even chronic alterations in liver function tests. Once INCPH is clinically suspected, liver histology becomes mandatory for the correct diagnosis. However, pathologists should be familiar with the histological features of INCPH, especially in cases in which histology is not only requested to exclude liver cirrhosis. Keywords: idiopathic portal hypertension, obliterative portal venopathy, esophageal varices, splenomegaly

Background and aims Obesity-related cardiometabolic risk factors associate with COVID-19 severity and outcomes. Epicardial adipose tissue (EAT) is associated with cardiometabolic disturbances, is a source of proinflammatory cytokines and a marker of visceral adiposity. We investigated the relation between EAT characteristics and outcomes in COVID-19 patients. Methods and results This post-hoc analysis of a large prospective investigation included all adult patients (≥18 years) admitted to San Raffaele University Hospital in Milan, Italy, from February 25th to April 19th, 2020 with confirmed SARS-CoV-2 infection who underwent a chest computed tomography (CT) scan for COVID-19 pneumonia and had anthropometric data available for analyses. EAT volume and attenuation (EAT-At, a marker of EAT inflammation) were measured on CT scan. Primary outcome was critical illness, defined as admission to intensive care unit (ICU), invasive ventilation or death. Cox regression and regression tree analyses were used to assess the relationship between clinical variables, EAT characteristics and critical illness. One-hundred and ninety-two patients were included (median [25th-75th percentile] age 60 years [53-70], 76% men). Co-morbidities included overweight/obesity (70%), arterial hypertension (40%), and diabetes (16%). At multivariable Cox regression analysis, EAT-At (HR 1.12 [1.04-1.21]) independently predicted critical illness, while increasing PaO2/FiO2 was protective (HR 0.996 [95% CI 0.993; 1.00]). CRP, plasma glucose on admission, EAT-At and PaO2/FiO2 identified five risk groups that significantly differed with respect to time to death or admission to ICU (log-rank p, Graphical abstract Image 1

Objectives Enlarged main pulmonary artery diameter (MPAD) resulted to be associated with pulmonary hypertension and mortality in a non-COVID-19 setting. The aim was to investigate and validate the association between MPAD enlargement and overall survival in COVID-19 patients. Methods This is a cohort study on 1469 consecutive COVID-19 patients submitted to chest CT within 72 h from admission in seven tertiary level hospitals in Northern Italy, between March 1 and April 20, 2020. Derivation cohort (n = 761) included patients from the first three participating hospitals; validation cohort (n = 633) included patients from the remaining hospitals. CT images were centrally analyzed in a core-lab blinded to clinical data. The prognostic value of MPAD on overall survival was evaluated at adjusted and multivariable Cox’s regression analysis on the derivation cohort. The final multivariable model was tested on the validation cohort. Results In the derivation cohort, the median age was 69 (IQR, 58–77) years and 537 (70.6%) were males. In the validation cohort, the median age was 69 (IQR, 59–77) years with 421 (66.5%) males. Enlarged MPAD (≥ 31 mm) was a predictor of mortality at adjusted (hazard ratio, HR [95%CI]: 1.741 [1.253–2.418], p p = 0.005), together with male gender, old age, high creatinine, low well-aerated lung volume, and high pneumonia extension (c-index [95%CI] = 0.826 [0.796–0.851]). Model discrimination was confirmed on the validation cohort (c-index [95%CI] = 0.789 [0.758–0.823]), also using CT measurements from a second reader (c-index [95%CI] = 0.790 [0.753;0.825]). Conclusion Enlarged MPAD (≥ 31 mm) at admitting chest CT is an independent predictor of mortality in COVID-19. Key Points •Enlargement of main pulmonary artery diameter at chest CT performed within 72 h from the admission was associated with a higher rate of in-hospital mortality in COVID-19 patients. •Enlargement of main pulmonary artery diameter (≥ 31 mm) was an independent predictor of death in COVID-19 patients at adjusted and multivariable regression analysis. •The combined evaluation of clinical findings, lung CT features, and main pulmonary artery diameter may be useful for risk stratification in COVID-19 patients.

Purpose To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients’ risk stratification. Material and Methods In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled. In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival. A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web–mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020). Results The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816–0.867) on wave 1 and was used to build a 0–100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402–0.8766). Conclusions AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.

Severe aortic stenosis (AS) is often associated with ascending aorta dilation (AAD). AAD is amenable to surgical correction combined with aortic valve replacement. Transcatheter aortic valve implantation (TAVI) might represent a valid therapeutic option in these patients when AAD correction Is not indicated. The aim of the present study is to evaluate the impact of concomitant AAD on early and mid-term outcomes after TAVI for symptomatic severe AS.