Assessment of short-term survival after liver transplant by the Model for End-Stage Liver Disease

Early markers of reperfusion injury after liver transplantation: association with primary dysfunction

BACKGROUND

In patients with end-stage liver disease, liver transplantation is the only available curative treatment. Although the outcome and quality of life in the patients have improved over the past decades, primary dys- or nonfunction (PDF/PNF) can occur. Early detection of PDF and PNF is crucial and could lead to individual therapies. This study was designed to identify early markers of reperfusion injury and PDF in liver biopsies taken during the first hour after reperfusion.

METHODS

Biopsies from donor livers were prospectively taken as a routine during the first hour after reperfusion. Recipient data, transaminases and outcome were routinely monitored. In total, 10 biopsy specimens taken from patients with 90-day mortality and PDF, and patients with long-term survival but without PDF were used for DNA microarrays. Markers that were significantly up- or down-regulated in the microarray were verified using quantitative real-time PCR.

RESULTS

Age, indications and labMELD score were similar in both groups. Peak-transaminases during the first week after transplantation were significantly different in the two groups. In total, 20 differentially regulated markers that correlated to PDF were identified using microarray analysis and verified with quantitative real-time PCR.

CONCLUSIONS

The markers identified in this study could predict PDF at a very early time point and might point to interventions that ameliorate reperfusion injury and thus prevent PDF. Identification of patients and organs at risk might lead to individualized therapies and could ultimately improve outcome.

Prediction of postoperative mortality in liver transplantation in the era of MELD-based liver allocation: a multivariate analysis

BACKGROUND AND AIMS

Liver transplantation is the only curative treatment for end-stage liver disease. While waiting list mortality can be predicted by the MELD-score, reliable scoring systems for the postoperative period do not exist. This study's objective was to identify risk factors that contribute to postoperative mortality.

METHODS

Between December 2006 and March 2011, 429 patients underwent liver transplantation in our department. Risk factors for postoperative mortality in 266 consecutive liver transplantations were identified using univariate and multivariate analyses. Patients who were <18 years, HU-listings, and split-, living related, combined or re-transplantations were excluded from the analysis. The correlation between number of risk factors and mortality was analyzed.

RESULTS

A labMELD ≥20, female sex, coronary heart disease, donor risk index >1.5 and donor Na+>145 mmol/L were identified to be independent predictive factors for postoperative mortality. With increasing number of these risk-factors, postoperative 90-day and 1-year mortality increased (0-1: 0 and 0%; 2: 2.9 and 17.4%; 3: 5.6 and 16.8%; 4: 22.2 and 33.3%; 5-6: 60.9 and 66.2%).

CONCLUSIONS

In this analysis, a simple score was derived that adequately identified patients at risk after liver transplantation. Opening a discussion on the inclusion of these parameters in the process of organ allocation may be a worthwhile venture.

Prediction of cardiac complications after liver transplantation

BACKGROUND

Orthotopic liver transplantation (OLT) stresses the cardiovascular system, and cardiac complications after OLT are common.

METHODS

Hundred ninety-seven patients (>or=40 years) who had OLT from 2002 to 2007 were reviewed to identify predictors of cardiac complications within 6 months after transplantation.

RESULTS

Median age was 56 years (40-75 years); 69% men. Reasons for OLT were hepatitis C virus (HCV) 45.5%, alcohol 22%, hepatocellular carcinoma (HCC) 8%, primary biliary cirrhosis 10%, and others 14.5%. Eighty-two patients suffered one or more cardiac complications within 6 months after OLT (pulmonary edema=61 [overt heart failure=7], arrhythmia=13, pulmonary hypertension=7, pericardial effusion=2, and right atrial thrombus=1). Cardiac causes were the leading cause of death (n=5; 23.8% of all mortality). By multivariate analysis, after adjusting for age and sex, independent predictors were adverse intraoperative cardiovascular events (adjusted odds ratio; 95% confidence interval: 5.89, 1.82-19.14), history of cardiac disease (2.42, 0.89-6.6), and i-MELD (integrated model for end-stage liver disease) score (1.08, 1.02-1.14), whereas adverse intraoperative cardiovascular events (5.73, 1.96-16.78) and i-MELD (1.07, 1.01-1.13) predicted pulmonary edema. None of the following variables predicted complications: age, sex, OLT indication, body mass index, blood pressure, alcohol and smoking history, pre-OLT investigations (chest X-ray, electrocardiogram, echocardiography, coronary angiography, pulmonary arterial pressure, and 2-methoxy isobutyl isonitrile scan), immunosuppressive treatment, or intraoperative variables (transfusion amount, cadaveric vs. living graft or cold ischemia and rewarming times).

CONCLUSIONS

Cardiac complications after OLT are common and were the leading cause of death after surgery. Adverse intraoperative cardiovascular events, previous cardiac disease, and advanced liver disease as quantified by i-MELD score predicted postoperative cardiac complications.

Prediction of survival after liver transplantation by pre-transplant parameters

OBJECTIVE

Score-based medical urgency criteria are used for necessity-oriented liver transplantation (OLT) but lead to an increasing number of complications in patients with reduced post-OLT survival. A prediction of outcome would improve preoperative patient selection and management.

MATERIAL AND METHODS

One-hundred-and-thirty-three consecutive adult patients (63.9% men, mean age 47.4+/-11.2 years) given transplants between May 2004 and November 2005 at the Hannover Medical School were analysed retrospectively using univariate and multivariate methods.

RESULTS

Indications were: 27.1% viral hepatitis, 19.6% primary sclerosing cholangitis, 15.0% alcoholic liver disease, 7.5% metabolic liver disease, 6.8% primary biliary cirrhosis. Overall, 12-month patient survival was 81.2%. The mean MELD score at OLT was 14.5+/-5.3 and 12-month survival with MELD >16 (71.7%) and <16 (86.2%) differed significantly (p=0.041). Predictors of 12-month mortality included age (53.2+/-9.4 versus 46.1+/-11.2 years; p=0.004), lower cholinesterase (2.9+/-1.88 versus 3.7+/-2.02 kU/l; p=0.026) and serum creatinine (160.4+/-186.8 versus 77.7+/-31.6 micromol/l; p=0.007), with creatinine and cholinesterase as independent parameters. Based on these parameters, a model for predicting patient survival after liver transplantation was calculated and validated in a second independent cohort of 87 OLT patients. This score identified a high-risk group and a low-risk group (overall survival 47.4 versus 91.2%; p<0.001) with a specificity of 87.3% and a sensitivity of 68.75%.

CONCLUSION

Age, pre-OLT creatinine and cholinesterase are predictors of short-term post-OLT survival and may be helpful as a bedside score in pre-OLT clinical management, outcome prediction and decision-making.

Models for prediction of mortality from cirrhosis with special reference to artificial neural network: a critical review

Prediction of mortality of patients with cirrhosis of liver, a common and potentially fatal disease, is important for timely listing of patients for liver transplantation. The Child-Pugh scoring system has been widely used for predicting the outcome of liver cirrhosis. The Model for End-Stage Liver Disease (MELD) score has recently become popular for prediction of short-term mortality for organ allocation. A few studies that evaluated artificial neural network (ANN)-based model for prediction of outcome of cirrhosis of liver in terms of mortality have consistently shown it to be superior to Child-Pugh scoring and logistic regression-based models; it is worth noting that MELD score is also derived using the logistic regression model. Due to the inherent ability of neural network-based systems in identifying complex nonlinear interactions, ANN-based models are expected to perform better than most linear models, such as regression-based models. More studies are needed on ANN-based models for prediction of mortality of patients with cirrhosis of liver and its value in prioritization of organ allocation for treatment of patients with cirrhosis of liver.