METHODS: Prospectively collected data of ACLF patients from APASL-ACLF Research Consortium (AARC) was analyzed for 30-day outcomes. The models evaluated at days 0, 4, and 7 of presentation for 30-day mortality were: AARC (model and score), CLIF-C (ACLF score, and OF score), NACSELD-ACLF (model and binary), SOFA, APACHE-II, MELD, MELD-Lactate, and CTP. Evaluation parameters were discrimination (c-indices), calibration [accuracy, sensitivity, specificity, and positive/negative predictive values (PPV/NPV)], Akaike/Bayesian Information Criteria (AIC/BIC), Nagelkerke-R2, relative prediction errors, and odds ratios.
RESULTS: Thirty-day survival of the cohort (n = 2864) was 64.9% and was lowest for final-AARC-grade-III (32.8%) ACLF. Performance parameters of all models were best at day 7 than at day 4 or day 0 (p 12 had the lowest 30-day survival (5.7%).
CONCLUSIONS: APASL-ACLF is often a progressive disease, and models assessed up to day 7 of presentation reliably predict 30-day mortality. Day-7 AARC model is a statistically robust tool for classifying risk of death and accurately predicting 30-day outcomes with relatively lower prediction errors. Day-7 AARC score > 12 may be used as a futility criterion in APASL-ACLF patients.
METHODS: A total of 1402 ACLF patients, enrolled in the APASL-ACLF Research Consortium (AARC) with 90-day follow-up, were analyzed. An ACLF score was developed in a derivation cohort (n = 480) and was validated (n = 922).
RESULTS: The overall survival of ACLF patients at 28 days was 51.7%, with a median of 26.3 days. Five baseline variables, total bilirubin, creatinine, serum lactate, INR and hepatic encephalopathy, were found to be independent predictors of mortality, with AUROC in derivation and validation cohorts being 0.80 and 0.78, respectively. AARC-ACLF score (range 5-15) was found to be superior to MELD and CLIF SOFA scores in predicting mortality with an AUROC of 0.80. The point scores were categorized into grades of liver failure (Gr I: 5-7; II: 8-10; and III: 11-15 points) with 28-day cumulative mortalities of 12.7, 44.5 and 85.9%, respectively. The mortality risk could be dynamically calculated as, with each unit increase in AARC-ACLF score above 10, the risk increased by 20%. A score of ≥11 at baseline or persisting in the first week was often seen among nonsurvivors (p = 0.001).
CONCLUSIONS: The AARC-ACLF score is easy to use, dynamic and reliable, and superior to the existing prediction models. It can reliably predict the need for interventions, such as liver transplant, within the first week.
METHODS: Consecutive ACLF patients were monitored for the development of SIRS/sepsis and associated complications and followed till 90 days, liver transplant or death.
RESULTS: Of 561 patients, 201 (35.8%) had no SIRS and 360 (64.2%) had SIRS with or without infection. New onset SIRS and sepsis developed in 74.6% and 8% respectively in a median of 7 (range 4-15) days, at a rate of 11% per day. The cumulative incidence of new SIRS was 29%, 92.8%, and 100% by days 4, 7, and 15. Liver failure, that is, bilirubin > 12 mg/dL (odds ratio [OR] = 2.5 [95% confidence interval {CI} = 1.05-6.19], P = 0.04) at days 0 and 4, and renal failure at day 4 (OR = 6.74 [95%CI = 1.50-13.29], P = 0.01), independently predicted new onset SIRS. Absence of SIRS in the first week was associated with reduced incidence of organ failure (20% vs 39.4%, P = 0.003), as was the 28-day (17.6% vs 36%, P = 0.02) and 90-day (27.5% vs 51%,P = 0.002) mortality. The 90-day mortality was 61.6% in the total cohort and that for those having no SIRS and SIRS at presentation were 42.8% and 65%, respectively (P Liver failure predicts the development of SIRS. New onset SIRS in the first week is an important determinant of early sepsis, organ failure, and survival. Prompt interventions in this 'golden window' before development of sepsis may improve the outcome of ACLF.
METHODS: We identified drugs as precipitants of ACLF among prospective cohort of patients with ACLF from the Asian Pacific Association of Study of Liver (APASL) ACLF Research Consortium (AARC) database. Drugs were considered precipitants after exclusion of known causes together with a temporal association between exposure and decompensation. Outcome was defined as death from decompensation.
RESULTS: Of the 3,132 patients with ACLF, drugs were implicated as a cause in 329 (10.5%, mean age 47 years, 65% men) and other nondrug causes in 2,803 (89.5%) (group B). Complementary and alternative medications (71.7%) were the commonest insult, followed by combination antituberculosis therapy drugs (27.3%). Alcoholic liver disease (28.6%), cryptogenic liver disease (25.5%), and non-alcoholic steatohepatitis (NASH) (16.7%) were common causes of underlying liver diseases. Patients with drug-induced ACLF had jaundice (100%), ascites (88%), encephalopathy (46.5%), high Model for End-Stage Liver Disease (MELD) (30.2), and Child-Turcotte-Pugh score (12.1). The overall 90-day mortality was higher in drug-induced (46.5%) than in non-drug-induced ACLF (38.8%) (P = 0.007). The Cox regression model identified arterial lactate (P < 0.001) and total bilirubin (P = 0.008) as predictors of mortality.
DISCUSSION: Drugs are important identifiable causes of ACLF in Asia-Pacific countries, predominantly from complementary and alternative medications, followed by antituberculosis drugs. Encephalopathy, bilirubin, blood urea, lactate, and international normalized ratio (INR) predict mortality in drug-induced ACLF.
METHODS: Data was collected from 13 Asian countries on patients with CLD, known or newly diagnosed, with confirmed COVID-19.
RESULTS: Altogether, 228 patients [185 CLD without cirrhosis and 43 with cirrhosis] were enrolled, with comorbidities in nearly 80%. Metabolism associated fatty liver disease (113, 61%) and viral etiology (26, 60%) were common. In CLD without cirrhosis, diabetes [57.7% vs 39.7%, OR = 2.1 (1.1-3.7), p = 0.01] and in cirrhotics, obesity, [64.3% vs. 17.2%, OR = 8.1 (1.9-38.8), p = 0.002] predisposed more to liver injury than those without these. Forty three percent of CLD without cirrhosis presented as acute liver injury and 20% cirrhotics presented with either acute-on-chronic liver failure [5 (11.6%)] or acute decompensation [4 (9%)]. Liver related complications increased (p liver disease; a Child-Turcotte Pugh score of 9 or more at presentation predicted high mortality [AUROC 0.94, HR = 19.2 (95 CI 2.3-163.3), p liver injury was progressive in 57% patients, with 43% mortality. Rising bilirubin and AST/ALT ratio predicted mortality among cirrhosis patients.
CONCLUSIONS: SARS-Cov-2 infection causes significant liver injury in CLD patients, decompensating one fifth of cirrhosis, and worsening the clinical status of the already decompensated. The CLD patients with diabetes and obesity are more vulnerable and should be closely monitored.
METHOD: The method based on constructing atlases for the portal and the hepatic veins bifurcations, the atlas is used to localize the corresponding vein in each segmented vasculature using atlas matching. Point-based registration is used to deform the mesh of atlas to the vein branch. Three-dimensional distance map of the hepatic veins is constructed; the fast marching scheme is applied to extract the centerlines. The centerlines of the labeled major veins are extracted by defining the starting and the ending points of each labeled vein. Centerline is extracted by finding the shortest path between the two points. The extracted centerline is used to define the trajectories to plot the required planes between the anatomical segments.
RESULTS: The proposed approach is validated on the IRCAD database. Using visual inspection, the method succeeded to extract the major veins centerlines. Based on that, the anatomic segments are defined according to Couinaud segmental anatomy.
CONCLUSION: Automatic liver segmental anatomy identification assists the surgeons for liver analysis in a robust and reproducible way. The anatomic segments with other liver structures construct a 3D visualization tool that is used by the surgeons to study clearly the liver anatomy and the extension of the cancer inside the liver.
METHODOLOGY/PRINCIPAL FINDINGS: The in vitro study demonstrated that T. indica fruit pulp had significant amount of phenolic (244.9 ± 10.1 mg GAE/extract) and flavonoid (93.9 ± 2.6 mg RE/g extract) content and possessed antioxidant activities. In the in vivo study, hamsters fed with high-cholesterol diet for ten weeks showed elevated serum triglyceride, total cholesterol, HDL-C and LDL-C levels. Administration of T. indica fruit pulp to hypercholesterolaemic hamsters significantly lowered serum triglyceride, total cholesterol and LDL-C levels but had no effect on the HDL-C level. The lipid-lowering effect was accompanied with significant increase in the expression of Apo A1, Abcg5 and LDL receptor genes and significant decrease in the expression of HMG-CoA reductase and Mtp genes. Administration of T. indica fruit pulp to hypercholesterolaemic hamsters also protected against oxidative damage by increasing hepatic antioxidant enzymes, antioxidant activities and preventing hepatic lipid peroxidation.
CONCLUSION/SIGNIFICANCE: It is postulated that tamarind fruit pulp exerts its hypocholesterolaemic effect by increasing cholesterol efflux, enhancing LDL-C uptake and clearance, suppressing triglyceride accumulation and inhibiting cholesterol biosynthesis. T. indica fruit pulp has potential antioxidative effects and is potentially protective against diet-induced hypercholesterolaemia.
MATERIALS AND METHODS: This was a cross-sectional, single center study. A total of 110 subjects between 18 to 65 years of age and diagnosed with OSA following sleep study examinations were recruited. Exclusion criteria included seropositive Hepatitis B or Hepatitis C, and significant alcohol intake.
RESULT: The prevalence of NAFLD was 81.8%. The mean CIMT (0.08±0.03 vs 0.06±0.01 cm, p = 0.001), ICAM-1 (334.53±72.86 vs 265.46±102.92 ng/mL, p = 0.001) and Lp(a) (85.41±52.56 vs 23.55±23.66 nmol/L, p<0.001) were significantly higher in the NAFLD group compared to the non-NAFLD group. Comparisons between the different groups showed significantly increasing levels of CIMT, ICAM-1 and Lp(a), lowest within the non-NAFLD, followed by the NAFLD 1 and NAFLD 2+3 groups. There was a significant positive correlation between degree of steatosis and the severity of OSA (r = 0.453, p<0.001). Logistic regression analysis revealed that patients with apnea/hypopnea index (AHI) of >30 were 52.77 (CI 6.34, 439.14) times more likely to have NAFLD compared to those with mild AHI (p<0.001).
CONCLUSION: The prevalence of NAFLD is alarmingly high in this group of OSA patients. The degree of steatosis in patients with NAFLD was significantly correlated with severity of OSA, CIMT measurements, ICAM-1 and Lp(a). Our findings underscore screening for NAFLD in patients with OSA to ensure prompt risk stratification and management.