Interleukin-22 predicts severity and death in advanced liver cirrhosis: a prospective cohort study

Background: Interleukin-22 (IL-22), recently identified as a crucial parameter of pathology in experimental liver damage, may determine survival in clinical end-stage liver disease. Systematic analysis of serum IL-22 in relation to morbidity and mortality of patients with advanced liver cirrhosis has not been performed so far. Methods: This is a prospective cohort study including 120 liver cirrhosis patients and 40 healthy donors to analyze systemic levels of IL-22 in relation to survival and hepatic complications. Results: A total of 71% of patients displayed liver cirrhosis-related complications at study inclusion. A total of 23% of the patients died during a mean follow-up of 196 +/- 165 days. Systemic IL-22 was detectable in 74% of patients but only in 10% of healthy donors (P 18 pg/ml, n = 57) showed significantly reduced survival compared to patients with regular ([less than or equal to]18 pg/ml) levels of IL-22 (321 days versus 526 days, P = 0.003). Other factors associated with overall survival were high CRP ([greater than or equal to]2.9 mg/dl, P = 0.005, hazard ratio (HR) 0.314, confidence interval (CI) (0.141 to 0.702)), elevated serum creatinine (P = 0.05, HR 0.453, CI (0.203 to 1.012)), presence of liver-related complications (P = 0.028, HR 0.258 CI (0.077 to 0.862)), model of end stage liver disease (MELD) score [greater than or equal to]20 (P = 0.017, HR 0.364, CI (0.159 to 0.835)) and age (P = 0.011, HR 1.047, CI (1.011 to 1.085)). Adjusted multivariate Cox proportional-hazards analysis identified elevated systemic IL-22 levels as independent predictors of reduced survival (P = 0.007, HR 0.218, CI (0.072 to 0.662)). Conclusions: In patients with liver cirrhosis, elevated systemic IL-22 levels are predictive for reduced survival independently from age, liver-related complications, CRP, creatinine and the MELD score. Thus, processes that lead to a rise in systemic interleukin-22 may be relevant for prognosis of advanced liver cirrhosis

MELD 3.0 adequately predicts mortality and renal replacement therapy requirements in patients with alcohol-associated hepatitis

Alcoholic hepatitis; Cirrhosis; Outcome predictionHepatitis alcohòlica; Cirrosi; Predicció de resultatsHepatitis alcohólica; Cirrosis; Predicción de resultadosBackground & aims: Model for End-Stage Liver Disease (MELD) score better predicts mortality in alcohol-associated hepatitis (AH) but could underestimate severity in women and malnourished patients. Using a global cohort, we assessed the ability of the MELD 3.0 score to predict short-term mortality in AH. Methods: This was a retrospective cohort study of patients admitted to hospital with AH from 2009 to 2019. The main outcome was all-cause 30-day mortality. We compared the AUC using DeLong's method and also performed a time-dependent AUC with competing risks analysis. Results: A total of 2,124 patients were included from 28 centres from 10 countries on three continents (median age 47.2 ± 11.2 years, 29.9% women, 71.3% with underlying cirrhosis). The median MELD 3.0 score at admission was 25 (20-33), with an estimated survival of 73.7% at 30 days. The MELD 3.0 score had a better performance in predicting 30-day mortality (AUC:0.761, 95%CI:0.732-0.791) compared with MELD sodium (MELD-Na; AUC: 0.744, 95% CI: 0.713-0.775; p = 0.042) and Maddrey's discriminant function (mDF) (AUC: 0.724, 95% CI: 0.691-0.757; p = 0.013). However, MELD 3.0 did not perform better than traditional MELD (AUC: 0.753, 95% CI: 0.723-0.783; p = 0.300) and Age-Bilirubin-International Normalised Ratio-Creatinine (ABIC) (AUC:0.757, 95% CI: 0.727-0.788; p = 0.765). These results were consistent in competing-risk analysis, where MELD 3.0 (AUC: 0.757, 95% CI: 0.724-0.790) predicted better 30-day mortality compared with MELD-Na (AUC: 0.739, 95% CI: 0.708-0.770; p = 0.028) and mDF (AUC:0.717, 95% CI: 0.687-0.748; p = 0.042). The MELD 3.0 score was significantly better in predicting renal replacement therapy requirements during admission compared with the other scores (AUC: 0.844, 95% CI: 0.805-0.883). Conclusions: MELD 3.0 demonstrated better performance compared with MELD-Na and mDF in predicting 30-day and 90-day mortality, and was the best predictor of renal replacement therapy requirements during admission for AH. However, further prospective studies are needed to validate its extensive use in AH. Impact and implications: Severe AH has high short-term mortality. The establishment of treatments and liver transplantation depends on mortality prediction. We evaluated the performance of the new MELD 3.0 score to predict short-term mortality in AH in a large global cohort. MELD 3.0 performed better in predicting 30- and 90-day mortality compared with MELD-Na and mDF, but was similar to MELD and ABIC scores. MELD 3.0 was the best predictor of renal replacement therapy requirements. Thus, further prospective studies are needed to support the wide use of MELD 3.0 in AH.JPA and MA receive support from the Chilean Government through the Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT 1200227 to JPA and 1191145 to MA). RB is a recipient of NIAAA U01AA021908 and U01AA020821

Toward a novel predictive analysis framework for new-generation clinical decision support systems

The idea of developing automated tools able to deal with the complexity of clinical information processing dates back to the late 60s: since then, there has been scope for improving medical care due to the rapid growth of medical knowledge, and the need to explore new ways of delivering this due to the shortage of physicians. Clinical decision support systems (CDSS) are able to aid in the acquisition of patient data and to suggest appropriate decisions on the basis of the data thus acquired. Many improvements are envisaged due to the adoption of such systems including: reduction of costs by faster diagnosis, reduction of unnecessary examinations, reduction of risk of adverse events and medication errors, increase in the available time for direct patient care, improved medications and examination prescriptions, improved patient satisfaction, and better compliance to gold-standard up-to-date clinical pathways and guidelines. Logistic regression is a widely used algorithm which frequently appears in medical literature for building clinical decision support systems: however, published studies frequently have not followed commonly recommended procedures for using logistic regression and substantial shortcomings in the reporting of logistic regression results have been noted. Published literature has often accepted conclusions from studies which have not addressed the appropriateness and accuracy of the statistical analyses and other methodological issues, leading to design flaws in those models and to possible inconsistencies in the novel clinical knowledge based on such results. The main objective of this interdisciplinary work is to design a sound framework for the development of clinical decision support systems. We propose a framework that supports the proper development of such systems, and in particular the underlying predictive models, identifying best practices for each stage of the model’s development. This framework is composed of a number of subsequent stages: 1) dataset preparation insures that appropriate variables are presented to the model in a consistent format, 2) the model construction stage builds the actual regression (or logistic regression) model determining its coefficients and selecting statistically significant variables; this phase is generally preceded by a pre-modelling stage during which model functional forms are hypothesized based on a priori knowledge 3) the further model validation stage investigates whether the model could suffer from overfitting, i.e., the model has a good accuracy on training data but significantly lower accuracy on unseen data, 4) the evaluation stage gives a measure of the predictive power of the model (making use of the ROC curve, which allows to evaluate the predictive power of the model without any assumptions on error costs, and possibly R2 from regressions), 5) misclassification analysis could suggest useful insights into determining where the model could be unreliable, 6) implementation stage. The proposed framework has been applied to three applications on different domains, with a view to improve previous research studies. The first developed model predicts mortality within 28 days of patients suffering from acute alcoholic hepatitis. The aim of this application is to build a new predictive model that can be used in clinical practice to identify patients at greatest risk of mortality in 28 days as they may benefit from aggressive intervention, and to monitor their progress while in hospital. A comparison generated by state of the art tools shows an improved predictive power, demonstrating how an appropriate variables inclusion may result in an overall better accuracy of the model, which increased by 25% following an appropriate variables selection process. The second proposed predictive model is designed to aid the diagnosis of dementia, as clinicians often experience difficulties in the diagnosis of dementia due to the intrinsic complexity of the process and lack of comprehensive diagnostic tools. The aim of this application is to improve on the performance of a recent application of Bayesian belief networks using an alternative approach based on logistic regression. The approach based on statistical variables selection outperformed the model which used variables selected by domain experts in previous studies. Obtained results outperform considered benchmarks by 15%. The third built model predicts the probability of experiencing a certain symptom among common side-effects in patients receiving chemotherapy. The newly developed model includes a pre-modelling stage (which was based on previous research studies) and a subsequent regression. The computed accuracy of results (computed on a daily basis for each cycle of therapy) shows that the newly proposed approach has increased its predictive power by 19% when compared to the previously developed model: this has been obtained by an appropriate usage of available a priori knowledge to pre-model the functional forms. As shown by the proposed applications, different aspects of CDSS development are subject to substantial improvements: the application of the proposed framework to different domains leads to more accurate models than the existing state-of-the-art proposals. The developed framework is capable of helping researchers to identify and overcome possible pitfalls in their ongoing research works, by providing them with best practices for each step of the development process. An impact on the development of future clinical decision support systems is envisaged: the usage of an appropriate procedure in model development will produce more reliable and accurate systems, and will have a positive impact on the newly produced medical knowledge which may eventually be included in standard clinical practice

Portal hypertension and cirrhosis: the role of inflammation and nitric oxide.

Patients with cirrhosis characteristically develop haemodynamic changes which include increased cardiac output, decreased systemic vascular resistance and paradoxically, increased portal pressure. Studies to date largely in animal models, have suggested that a decrease in hepatic nitric oxide may be important. The studies described in this thesis provide evidence for significantly elevated portal pressure in alcoholic hepatitis patients, who were shown to have a marked additional inflammatory response on the background of cirrhosis, and a more severe expression of disease. Studies in patients and a galactosamine rodent model confirmed a decrease in endothelial nitric oxide synthase (NOS) activity in the context of inflammatory liver injury. Following on from these observations, further studies explored the role of potential regulators of NOS which may have accounted for its decrease in activity in liver disease. Studies of asymmetric dimethylarginine (ADMA), an endogenous NOS inhibitor, showed that it was markedly increased in liver failure in both patients and in an animal model. The data suggested this may result from a decreased metabolism (through reduced expression of its metabolizing enzyme, dimethylarginine-dimethylaminohydrolase) or/and increased synthesis by protein arginine methyltransferases. ADMA was shown to correlate with severity of portal pressure, and with increased organ failure and death in decompensated cirrhosis, suggesting that it may have a potential use as a biomarker of disease severity. Other novel regulators of endothelial NOS were also explored including the recently described potential NOS inhibitor, NOSTRIN (nitric oxide synthase traffic inducer). It was demonstrated that NOSTRIN was up-regulated at both message and protein levels in liver disease patients and this was most marked in those with additional inflammation. A further novel observation was the identification of a variant of NOSTRIN which was only found in cirrhosis patients and not in normal liver tissue. The findings from these studies provide a better understanding of the importance of inflammation in the context of vascular dysfunction in cirrhosis and highlight some potential novel therapeutic targets

Genomics and metabonomics in severe alcoholic hepatitis

Severe alcoholic hepatitis is a florid presentation of alcohol-related liver disease and is associated with very high short-term mortality, in excess of 20% within 28 days. Severe alcoholic hepatitis occurs in a minority of patients who develop alcohol-related liver disease. A combination of genetic and environmental factors is likely to predispose to severe alcoholic hepatitis. To date the clinical phenotype has not been extensively examined in candidate gene studies and has been the subject of a single, small genome-wide association study. A genome-wide association study of severe alcoholic hepatitis identified two loci potentially associated with the risk of developing severe alcoholic hepatitis: i) A strong association with PNPLA3, a well-recognised risk locus for alcohol-related liver disease, and ii) a novel but weaker association with SLC38A4, an amino acid transporter. The primary genetic variant at each locus was evaluated to determine whether there was an influence on disease phenotype or outcome. The primary variant in PNPLA3, rs738409, is a missense variant. Analyses indicated a deleterious effect of homozygosity on medium-term survival in addition to more severe disease on baseline histology and a slower recovery in liver function over the short-term period; consistent with established literature in alcohol-related cirrhosis. In contrast the primary variant in SLC38A4, rs11183620, is intronic with no clear evidence for an effect on gene expression or function. Analyses did not indicate an influence on histology, clinical phenotypes or outcomes. In light of the locus’ novelty further work was undertaken to determine any potential contribution to disease pathogenesis. SLC38A4 was down-regulated in whole liver tissue in severe alcoholic hepatitis. Experiments with cell lines in culture suggested the pro-inflammatory cytokine IL-1 as a potential driver. SLC38A4 knockdown resulted in upregulation of some cellular responses associated with nutrient deprivation. There was no influence of the variant on serum amino acid profiles. The functional significance of SLC38A4 down-regulation remains the subject of ongoing work.Open Acces

Acute-on-chronic liver failure: Consensus recommendations of the Asian pacific association for the study of the liver (APASL): An update

The first consensus report of the working party of the Asian Pacific Association for the Study of the Liver (APASL) set up in 2004 on acute-on-chronic liver failure (ACLF) was published in 2009. With international groups volunteering to join, the APASL ACLF Research Consortium (AARC) was formed in 2012, which continued to collect prospective ACLF patient data. Based on the prospective data analysis of nearly 1400 patients, the AARC consensus was published in 2014. In the past nearly four-and-a-half years, the AARC database has been enriched to about 5200 cases by major hepatology centers across Asia. The data published during the interim period were carefully analyzed and areas of contention and new developments in the field of ACLF were prioritized in a systematic manner. The AARC database was also approached for answering some of the issues where published data were limited, such as liver failure grading, its impact on the \u27Golden Therapeutic Window\u27, extrahepatic organ dysfunction and failure, development of sepsis, distinctive features of acute decompensation from ACLF and pediatric ACLF and the issues were analyzed. These initiatives concluded in a two-day meeting in October 2018 at New Delhi with finalization of the new AARC consensus. Only those statements, which were based on evidence using the Grade System and were unanimously recommended, were accepted. Finalized statements were again circulated to all the experts and subsequently presented at the AARC investigators meeting at the AASLD in November 2018. The suggestions from the experts were used to revise and finalize the consensus. After detailed deliberations and data analysis, the original definition of ACLF was found to withstand the test of time and be able to identify a homogenous group of patients presenting with liver failure. New management options including the algorithms for the management of coagulation disorders, renal replacement therapy, sepsis, variceal bleed, antivirals and criteria for liver transplantation for ACLF patients were proposed. The final consensus statements along with the relevant background information and areas requiring future studies are presented here