Biomaterial Database

The CLIF Consortium Acute Decompensation score (CLIF-C ADs) for prognosis of hospitalised cirrhotic patients without acute-on-chronic liver failure. 2015

Rajiv Jalan, and Marco Pavesi, and Faouzi Saliba, and Alex Amorós, and Javier Fernandez, and Peter Holland-Fischer, and Rohit Sawhney, and Rajeshwar Mookerjee, and Paolo Caraceni, and Richard Moreau, and Pere Ginès, and Francois Durand, and Paolo Angeli, and Carlo Alessandria, and Wim Laleman, and Jonel Trebicka, and Didier Samuel, and Stefan Zeuzem, and Thierry Gustot, and Alexander L Gerbes, and Julia Wendon, and Mauro Bernardi, and Vicente Arroyo, and

Liver Failure Group, Institute for Liver and Digestive Health, Royal Free Hospital, London, United Kingdom.

OBJECTIVE Cirrhotic patients with acute decompensation frequently develop acute-on-chronic liver failure (ACLF), which is associated with high mortality rates. Recently, a specific score for these patients has been developed using the CANONIC study database. The aims of this study were to develop and validate the CLIF-C AD score, a specific prognostic score for hospitalised cirrhotic patients with acute decompensation (AD), but without ACLF, and to compare this with the Child-Pugh, MELD, and MELD-Na scores. METHODS The derivation set included 1016 CANONIC study patients without ACLF. Proportional hazards models considering liver transplantation as a competing risk were used to identify score parameters. Estimated coefficients were used as relative weights to compute the CLIF-C ADs. External validation was performed in 225 cirrhotic AD patients. CLIF-C ADs was also tested for sequential use. RESULTS Age, serum sodium, white-cell count, creatinine and INR were selected as the best predictors of mortality. The C-index for prediction of mortality was better for CLIF-C ADs compared with Child-Pugh, MELD, and MELD-Nas at predicting 3- and 12-month mortality in the derivation, internal validation and the external dataset. CLIF-C ADs improved in its ability to predict 3-month mortality using data from days 2, 3-7, and 8-15 (C-index: 0.72, 0.75, and 0.77 respectively). CONCLUSIONS The new CLIF-C ADs is more accurate than other liver scores in predicting prognosis in hospitalised cirrhotic patients without ACLF. CLIF-C ADs therefore may be used to identify a high-risk cohort for intensive management and a low-risk group that may be discharged early.

UI	MeSH Term	Description	Entries
D007958	Leukocyte Count	The number of WHITE BLOOD CELLS per unit volume in venous BLOOD. A differential leukocyte count measures the relative numbers of the different types of white cells.	Blood Cell Count, White,Differential Leukocyte Count,Leukocyte Count, Differential,Leukocyte Number,White Blood Cell Count,Count, Differential Leukocyte,Count, Leukocyte,Counts, Differential Leukocyte,Counts, Leukocyte,Differential Leukocyte Counts,Leukocyte Counts,Leukocyte Counts, Differential,Leukocyte Numbers,Number, Leukocyte,Numbers, Leukocyte
D008103	Liver Cirrhosis	Liver disease in which the normal microcirculation, the gross vascular anatomy, and the hepatic architecture have been variably destroyed and altered with fibrous septa surrounding regenerated or regenerating parenchymal nodules.	Cirrhosis, Liver,Fibrosis, Liver,Hepatic Cirrhosis,Liver Fibrosis,Cirrhosis, Hepatic
D008297	Male		Males
D008875	Middle Aged	An adult aged 45 - 64 years.	Middle Age
D011237	Predictive Value of Tests	In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.	Negative Predictive Value,Positive Predictive Value,Predictive Value Of Test,Predictive Values Of Tests,Negative Predictive Values,Positive Predictive Values,Predictive Value, Negative,Predictive Value, Positive
D011379	Prognosis	A prediction of the probable outcome of a disease based on a individual's condition and the usual course of the disease as seen in similar situations.	Prognostic Factor,Prognostic Factors,Factor, Prognostic,Factors, Prognostic,Prognoses
D012107	Research Design	A plan for collecting and utilizing data so that desired information can be obtained with sufficient precision or so that an hypothesis can be tested properly.	Experimental Design,Data Adjustment,Data Reporting,Design, Experimental,Designs, Experimental,Error Sources,Experimental Designs,Matched Groups,Methodology, Research,Problem Formulation,Research Methodology,Research Proposal,Research Strategy,Research Technics,Research Techniques,Scoring Methods,Adjustment, Data,Adjustments, Data,Data Adjustments,Design, Research,Designs, Research,Error Source,Formulation, Problem,Formulations, Problem,Group, Matched,Groups, Matched,Matched Group,Method, Scoring,Methods, Scoring,Problem Formulations,Proposal, Research,Proposals, Research,Reporting, Data,Research Designs,Research Proposals,Research Strategies,Research Technic,Research Technique,Scoring Method,Source, Error,Sources, Error,Strategies, Research,Strategy, Research,Technic, Research,Technics, Research,Technique, Research,Techniques, Research
D003404	Creatinine		Creatinine Sulfate Salt,Krebiozen,Salt, Creatinine Sulfate,Sulfate Salt, Creatinine
D005260	Female		Females
D006760	Hospitalization	The confinement of a patient in a hospital.	Hospitalizations