Acute Budd-Chiari Syndrome with Complete Portal Vein Thrombosis Complicated by Hepato-Renal Syndrome Treated Successfully by Emergent TIPS with Rheolytic Thrombectomy.

We described a case of a 73-year-old female admitted to the emergency department with acute hepatic and renal failure (hepato-renal syndrome, HRS) due to acute Budd-Chiari syndrome associated with complete portal vein thrombosis (BCS-PVT) for an unknown cause. Despite the initial therapy with anticoagulants, a sudden impairment of the renal function requiring hemodialysis was observed. The hepatic transplant was excluded for patient age and clinical conditions. Thus, the patient was successfully treated by emergent transjugular intrahepatic portosystemic shunt (TIPS) previous rheolytic thrombectomy of the PVT with AngioJet Ultra PE Thrombectomy System (Boston Scientific, Marlborough, MA, USA). After the procedure, the sudden resolution of the HRS was observed, and the patient is alive 13 months after hospital dismission with no TIPS dysfunction. In conclusion, emergent extended TIPS with the usage of rheolytic thrombectomy device in patient with acute BCS-PVT complicated by HRS is feasible by experienced operators and provide resolution of the HRS.

Role of visceral pleural invasion and tumor sizing at CT of resected NSCLC in clinical-radiological and pathological T agreement.

OBJECTIVE: To describe in non-small cell lung cancer (NSCLC) the impact of visceral pleural invasion (VPI) and of tumor sizing assessed at computed tomography (CT) on the agreement between clinical-radiological and pathological T staging and its prognostic value. METHODS: Patients affected by NSCLC treated by surgery in the period from January 2017 to September 2020 were retrospectively evaluated. Exclusion criteria were: (1) baseline CT not performed in our hospital; (2) failure of software segmentation at CT of the primary lesion. Clinical-radiological T (cT) was assessed at baseline CT, evaluating in particular T size by semi-automatic tool and VPI (cVPI) visually. Pathological T (pT) and VPI (pVPI) were recorded by pathological report and obtained after formalin-fixation and eventual elastic stain on surgical specimen. The agreement between cT and pT was evaluated by calculating the weighted kappa by Cohen (κw); the association between progression free survival (PFS) with both cT and pT was assessed by the Cox regression analysis. RESULTS: The study included 84 NSCLC in 82 patients (median age 71 years, IQR 63-76 years; females 22/82, 27%). The agreement between cT and pT was poor (κw 0.302, 95%CI 0.158-0.447). The main causes of disagreement were CT oversizing (21%) and false positive cVPI (29%). A significant association was found between PFS and pT2-T3 (HR 2.75, 95%CI 1.21-6.25, p=0.015) but not with cT2-T3 (not retained in the model). CONCLUSIONS: False positive cVPI and oversizing at CT are causes of disagreement between cT and pT in around one-third of resected NSCLC. PFS was significantly associated with pT but not with cT.

Comparison of admission chest computed tomography and lung ultrasound performance for diagnosis of COVID-19 pneumonia in populations with different disease prevalence.

PURPOSE: Chest computed tomography (CT) is considered a reliable imaging tool for COVID-19 pneumonia diagnosis, while lung ultrasound (LUS) has emerged as a potential alternative to characterize lung involvement. The aim of the study was to compare diagnostic performance of admission chest CT and LUS for the diagnosis of COVID-19. METHODS: We included patients admitted to emergency department between February 21-March 6, 2020 (high prevalence group, HP) and between March 30-April 13, 2020 (moderate prevalence group, MP) undergoing LUS and chest CT within 12 h. Chest CT was considered positive in case of "indeterminate"/"typical" pattern for COVID-19 by RSNA classification system. At LUS, thickened pleural line with ≥ three B-lines at least in one zone of the 12 explored was considered positive. Sensitivity, specificity, PPV, NPV, and AUC were calculated for CT and LUS against real-time reverse transcriptase polymerase chain reaction (RT-PCR) and serology as reference standard. RESULTS: The study included 486 patients (males 61 %; median age, 70 years): 247 patients in HP (COVID-19 prevalence 94 %) and 239 patients in MP (COVID-19 prevalence 45 %). In HP and MP respectively, sensitivity, specificity, PPV, and NPV were 90-95 %, 43-69 %, 96-72 %, 20-95 % for CT and 94-93 %, 7-31 %, 94-52 %, 7-83 % for LUS. CT demonstrated better performance than LUS in diagnosis of COVID-19, both in HP (AUC 0.75 vs 0.51; P < 0.001) and MP (AUC 0.85 vs 0.62; P < 0.001). CONCLUSIONS: Admission chest CT shows better performance than LUS for COVID-19 diagnosis, at varying disease prevalence. LUS is highly sensitive, but not specific for COVID-19.

Qualitative and quantitative chest CT parameters as predictors of specific mortality in COVID-19 patients.

PURPOSE: To test the association between death and both qualitative and quantitative CT parameters obtained visually and by software in coronavirus disease (COVID-19) early outbreak. METHODS: The study analyzed retrospectively patients underwent chest CT at hospital admission for COVID-19 pneumonia suspicion, between February 21 and March 6, 2020. CT was performed in case of hypoxemia or moderate-to-severe dyspnea. CT scans were analyzed for quantitative and qualitative features obtained visually and by software. Cox proportional hazards regression analysis examined the association between variables and overall survival (OS). Three models were built for stratification of mortality risk: clinical, clinical/visual CT evaluation, and clinical/software-based CT assessment. AUC for each model was used to assess performance in predicting death. RESULTS: The study included 248 patients (70% males, median age 68 years). Death occurred in 78/248 (32%) patients. Visual pneumonia extent > 40% (HR 2.15, 95% CI 1.2-3.85, P = 0.01), %high attenuation area - 700 HU > 35% (HR 2.17, 95% CI 1.2-3.94, P = 0.01), exudative consolidations (HR 2.85-2.93, 95% CI 1.61-5.05/1.66-5.16, P < 0.001), visual CAC score > 1 (HR 2.76-3.32, 95% CI 1.4-5.45/1.71-6.46, P < 0.01/P < 0.001), and CT classified as COVID-19 and other disease (HR 1.92-2.03, 95% CI 1.01-3.67/1.06-3.9, P = 0.04/P = 0.03) were significantly associated with shorter OS. Models including CT parameters (AUC 0.911-0.913, 95% CI 0.873-0.95/0.875-0.952) were better predictors of death as compared to clinical model (AUC 0.869, 95% CI 0.816-0.922; P = 0.04 for both models). CONCLUSIONS: In COVID-19 patients, qualitative and quantitative chest CT parameters obtained visually or by software are predictors of mortality. Predictive models including CT metrics were better predictors of death in comparison to clinical model.

Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia.

Background CT of patients with severe acute respiratory syndrome coronavirus 2 disease depicts the extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia. Purpose To determine the value of quantification of the well-aerated lung (WAL) obtained at admission chest CT to determine prognosis in patients with COVID-19 pneumonia. Materials and Methods Imaging of patients admitted at the emergency department between February 17 and March 10, 2020 who underwent chest CT were retrospectively analyzed. Patients with negative results of reverse-transcription polymerase chain reaction for severe acute respiratory syndrome coronavirus 2 at nasal-pharyngeal swabbing, negative chest CT findings, and incomplete clinical data were excluded. CT images were analyzed for quantification of WAL visually (%V-WAL), with open-source software (%S-WAL), and with absolute volume (VOL-WAL). Clinical parameters included patient characteristics, comorbidities, symptom type and duration, oxygen saturation, and laboratory values. Logistic regression was used to evaluate the relationship between clinical parameters and CT metrics versus patient outcome (intensive care unit [ICU] admission or death vs no ICU admission or death). The area under the receiver operating characteristic curve (AUC) was calculated to determine model performance. Results The study included 236 patients (59 of 123 [25%] were female; median age, 68 years). A %V-WAL less than 73% (odds ratio [OR], 5.4; 95% confidence interval [CI]: 2.7, 10.8; P < .001), %S-WAL less than 71% (OR, 3.8; 95% CI: 1.9, 7.5; P < .001), and VOL-WAL less than 2.9 L (OR, 2.6; 95% CI: 1.2, 5.8; P < .01) were predictors of ICU admission or death. In comparison with clinical models containing only clinical parameters (AUC = 0.83), all three quantitative models showed better diagnostic performance (AUC = 0.86 for all models). The models containing %V-WAL less than 73% and VOL-WAL less than 2.9 L were superior in terms of performance as compared with the models containing only clinical parameters (P = .04 for both models). Conclusion In patients with confirmed coronavirus disease 2019 pneumonia, visual or software quantification of the extent of CT lung abnormality were predictors of intensive care unit admission or death. © RSNA, 2020 Online supplemental material is available for this article.