Kamath, Patrick S.

Abstract Context. —Impairment of venous outflow manifests as zone 3 sinusoidal dilatation and congestion (SDC) in liver biopsy. However, the finding of SDC is not specific for venous outflow impairment. Objectives. —To determine the specificity of SDC in liver biopsies for venous outflow impairment and to seek an explanation for SDC in patients without clinical or radiologic features of venous outflow impairment. Design. —Liver biopsies from 51 patients with sinusoidal dilatation were reviewed. Biopsies from transplant recipients, patients with cirrhosis, and patients with hepatic neoplasms (primary or metastatic) were not included. Clinical records were reviewed for laboratory tests and final clinicopathologic diagnosis. Results. —Thirty-four patients (66.7%) had confirmed venous outflow impairment. Of the 17 cases (33.3%) without clinical and/or radiologic evidence of venous outflow impairment, vascular causes were present in 5 cases (9.8%; nodular regenerative hyperplasia in 2 cases and portal vein thrombosis, congenital absence of the portal vein, and sickle cell anemia in 1 case each). Systemic inflammatory disorders were identified in 6 patients (11.8%). These included 2 cases of Castleman disease and 1 each of sarcoidosis, Crohn disease, rheumatoid arthritis, and Still disease. Three patients (5.9%) had tumors without direct involvement of the liver (1 case each of Hodgkin lymphoma, renal cell carcinoma, and pancreatic serous pseudopapillary tumor). In the remaining 3 patients, SDC was identified in wedge liver biopsies performed at the time of surgery, including gastric bypass surgery, cholecystectomy, and splenectomy. No other disease association was apparent in these cases. Conclusion. —Sinusoidal dilatation and congestion in liver biopsy is associated with venous outflow impairment in two thirds of the cases. In the absence of clinical and/or radiological evidence of venous outflow, diagnostic considerations include other vascular conditions, such as portal vein insufficiency and nodular regenerative hyperplasia. Sinusoidal dilatation and congestion can also occur in the setting of systemic inflammatory diseases, granulomatous disorders, and neoplasms, as well as in wedge biopsies obtained during abdominal surgery.

INTRODUCTION: Readmission and death in cirrhosis are common, expensive, and difficult to predict. Our aim was to evaluate the abilities of multiple artificial intelligence (AI) techniques to predict clinical outcomes based on variables collected at admission, during hospitalization, and at discharge. METHODS: We used the multicenter North American Consortium for the Study of End-Stage Liver Disease (NACSELD) cohort of cirrhotic inpatients who are followed up through 90-days postdischarge for readmission and death. We used statistical methods to select variables that are significant for readmission and death and trained 3 AI models, including logistic regression (LR), kernel support vector machine (SVM), and random forest classifiers (RFC), to predict readmission and death. We used the area under the receiver operating characteristic curve (AUC) from 10-fold crossvalidation for evaluation to compare sexes. Data were compared with model for end-stage liver disease (MELD) at discharge. RESULTS: We included 2,170 patients (57 ± 11 years, MELD 18 ± 7, 61% men, 79% White, and 8% Hispanic). The 30-day and 90-day readmission rates were 28% and 47%, respectively, and 13% died at 90 days. Prediction for 30-day readmission resulted in 0.60 AUC for all patients with RFC, 0.57 AUC with LR for women-only subpopulation, and 0.61 AUC with LR for men-only subpopulation. For 90-day readmission, the highest AUC was achieved with kernel SVM and RFC (AUC = 0.62). We observed higher predictive value when training models with only women (AUC = 0.68 LR) vs men (AUC = 0.62 kernel SVM). Prediction for death resulted in 0.67 AUC for all patients, 0.72 for women-only subpopulation, and 0.69 for men-only subpopulation, all with LR. MELD-Na model AUC was similar to those from the AI models. DISCUSSION: Despite using multiple AI techniques, it is difficult to predict 30- and 90-day readmissions and death in cirrhosis. AI model accuracies were equivalent to models generated using only MELD-Na scores. Additional biomarkers are needed to improve our predictive capability (See also the visual abstract at http://links.lww.com/AJG/B710).

The burden of cirrhosis hospitalizations is increasing. The admission Model for End‐Stage Liver Disease–lactate (MELD‐lactate) was recently demonstrated to be a superior predictor of in‐hospital mortality compared with MELD in limited cohorts. We identified specific classes of hospitalizations where MELD‐lactate may be especially useful and evaluated the predictive role of lactate clearance. This was a retrospective cohort study of 1036 cirrhosis hospitalizations for gastrointestinal bleeding, infection, or other portal hypertension–related indications in the Veterans Health Administration where MELD‐lactate was measured on admission. Performance characteristics for in‐hospital mortality were compared between MELD‐lactate and MELD/MELD‐sodium (MELD‐Na), with stratified analyses of MELD categories (≤15, >15 to <25, ≥25) and reason for admission. We also incorporated day 3 lactate levels into modeling and tested for an interaction between day 1 MELD‐lactate and day 3 lactate clearance. MELD‐lactate had superior discrimination for in‐hospital mortality compared with MELD or MELD‐Na (area under the curve [AUC] 0.789 versus 0.776 versus 0.760, respectively; P < 0.001) and superior calibration. MELD‐lactate had higher discrimination among hospitalizations with MELD ≤15 (AUC 0.763 versus 0.608 for MELD, global P = 0.01) and hospitalizations for infection (AUC 0.791 versus 0.674 for MELD, global P < 0.001). We found a significant interaction between day 1 MELD‐lactate and day 3 lactate clearance; heat maps were created as clinical tools to risk‐stratify patients based on these clinical data. MELD‐lactate had significantly superior performance in predicting in‐hospital mortality among patients hospitalized for infection and/or with MELD ≤15 when compared with MELD or MELD‐Na. Incorporating day 3 lactate clearance may further improve prognostication.

INTRODUCTION: The purpose of this study was to find out whether 3-dimensional (3D)–printed models improved the learners' ability to identify liver segments. METHODS: A total of 116 physicians from 3 disciplines were tested in a cross-over trial at baseline and after teaching with 3D models and 2-dimensional (2D) images. Adjusted multilevel-mixed models were used to compare scores at baseline and after 3D and 2D. RESULTS: Accuracy in identifying hepatic segments was higher with 3D first than 2D (77% vs 69%; P = 0.05) and not significantly improved by a combination of 3D and 2D. Increased confidence in segment identification was highest in trainees after 3D ( P = 0.04). DISCUSSION: 3D-printed models facilitate learning hepatic segmental anatomy.

AbstractBackground & AimsPhosphodiesterase‐5 inhibitors (PDE‐5‐I) are used for treatment of erectile dysfunction (ED), which is common in patients with cirrhosis. They may improve portal hypertension (PH), but contradictory data on efficacy and side‐effects have been reported. Non‐selective beta blockers (NSBB) reduce portal pressure, but might aggravate ED. Thus, we evaluated the combination of PDE‐5‐I with NSBB and its impact on PH and ED in experimental cirrhosis.MethodsED was assessed in cirrhotic patients (n = 86) using standardized questionnaire. Experimental cirrhosis was induced by bile‐duct‐ligation or carbon‐tetrachloride intoxication in rats. Corpus cavernosum pressure – a surrogate of ED ‐, as well as systemic and portal haemodynamics, were measured in vivo and in situ after acute administration of udenafil alone or in combination with propranolol. mRNA and protein levels of PDE‐5 signalling were analysed using PCR and western Blot.ResultsED in humans was related to severity of liver disease and to NSBB treatment. PDE‐5 was mainly expressed in hepatic stellate cells and upregulated in human and experimental cirrhosis. Propranolol reduced corpus cavernosum pressure in cirrhotic rats and it was restored by udenafil. Even though udenafil treatment improved PH, it led to a reduction of mean arterial pressure. The combination of udenafil and propranolol reduced portal pressure and hepatic resistance without systemic side‐effects.ConclusionsED is common with advanced cirrhosis and concomitant NSBB treatment. The combination of PDE‐5‐I and NSBB improves ED and PH in experimental cirrhosis.