Plasma ammonia levels predict hospitalisation with liver-related complications and mortality in clinically stable outpatients with cirrhosis

BACKGROUND AND AIMS: Hyperammonaemia is central in the pathogenesis of hepatic encephalopathy, but also has pleiotropic deleterious effects on several organ systems, impacting on immune function, sarcopenia, energy metabolism and portal hypertension. This study was performed to test the hypothesis that severity of hyperammonaemia is a risk factor for liver-related complications in clinically stable outpatients with cirrhosis. METHODS: We collected data from 754 clinically stable outpatients with cirrhosis from 3 independent liver units. Baseline ammonia levels were corrected to the upper limit of normal (AMM-ULN) for the reference laboratory. The primary endpoint was hospitalisation with liver-related complications (a composite endpoint of bacterial infection, variceal bleeding, overt hepatic encephalopathy, or new onset or worsening of ascites). Multivariable competing risk frailty analysis and fast unified random forest were performed to predict complications and mortality. External validation was carried out using prospective data from 130 cirrhotic patients in an independent tertiary liver centre. RESULTS: Overall, 260 (35%) patients were hospitalised with liver-related complications. On multivariable analysis, AMM-ULN was an independent predictor of both liver-related complications (HR=2.13; 95%CI=1.89-2.40; p<0.001) and mortality (HR=1.45; 95%CI=1.20-1.76; p<0.001). AUROC of AMM-ULN was 77.9% for 1-year complications, higher than traditional severity scores. Statistical differences in survival were found between high and low levels of AMM-ULN both for complications and mortality (p<0.001) using 1.4 as the optimal cut-off from the training set. AMM-ULN remained a key variable for the prediction of complications within the random forests model in the derivation cohort and upon external validation. CONCLUSION: Ammonia is an independent predictor of hospitalisation with liver-related complications and mortality in clinically stable outpatients with cirrhosis and performs better than traditional prognostic scores in predicting complications. LAY SUMMARY: We conducted a prospective cohort study evaluating the association of blood ammonia levels with the risk of adverse outcomes in 754 patients with stable cirrhosis across 3 independent liver units. We found that ammonia is a key determinant that helps to predict which patients will be hospitalised, develop liver-related complications and die; this was confirmed in an independent cohort of patients

Random survival forests

We introduce random survival forests, a random forests method for the analysis of right-censored survival data. New survival splitting rules for growing survival trees are introduced, as is a new missing data algorithm for imputing missing data. A conservation-of-events principle for survival forests is introduced and used to define ensemble mortality, a simple interpretable measure of mortality that can be used as a predicted outcome. Several illustrative examples are given, including a case study of the prognostic implications of body mass for individuals with coronary artery disease. Computations for all examples were implemented using the freely available R-software package, randomSurvivalForest.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS169 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Prognostic relevance of gene-expression signatures

Cancer prognosis can be regarded as estimating the risk of future outcomes from multiple variables. In prognostic signatures, these variables represent expressions of genes that are summed up to calculate a risk score. However, it is a natural phenomenon in living systems that the whole is more than the sum of its parts. We hypothesize that the prognostic power of signatures is fundamentally limited without incorporating emergent effects. Convergent evidence from a set of unprecedented size (ca. 10,000 signatures) implicates a maximum prognostic power. We show that a signature can correctly discriminate patients' prognoses in no more than 80% of the time. Using a simple simulation, we show that more than 50% of the potentially available information is still missing at this value.Comment: 27 pages, 6 figures, supporting informatio

Optimal survival trees ensemble

Selection of accurate and diverse trees based on individual and collective performance in an ensemble has recently been studied for classification and regression problems. Following this notion, the possibility of selecting optimal survival trees is considered in this work. Initially, a large set of survival trees are grown by the method of random survival forest. Using out-of-bag observations for each corresponding survival tree, the trees grown are ranked in ascending order with respect to their prediction errors. A certain number of the top ranked survival trees are selected to be assessed for their collective performance in an ensemble. An ensemble is initiated from the top ranked selected survival tree and further trees are tested one by one by adding them to the ensemble. A survival tree is selected for the final ensemble if it improves the performance by assessing on an independent training data. This ensemble is called optimal survival trees ensemble (OSTE). The proposed method is checked on 17 benchmark datasets and the results are compared with those of random survival forest, conditional inference forest, bagging and Cox proportional hazard model. In addition to improved predictive performance, the proposed method also reduces the number of survival trees in the ensemble as compared to the other tree based methods. Furthermore, the method is implemented in an $R$ package called "OSTE''