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An integrated MELD model including serum sodium and age improves the prediction of early mortality in patients with cirrhosis

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LIVER TRANSPLANTATION
卷 13, 期 8, 页码 1174-1180

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WILEY
DOI: 10.1002/lt.21197

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The Model for End-Stage Liver Disease (MELD) is widely used to predict the short-term mortality in patients with cirrhosis, but potential limitations of this score have been reported. The aim of this study was to improve the score's prognostic accuracy by assessing new objective variables. Data of 310 consecutive patients with cirrhosis who underwent elective transjugular intrahepatic portosystemic shunt placement between July 1995 and March 2005 were analyzed retrospectively. Bivariate and multivariate analyses were performed by proportional hazard Cox regression models. The area under the receiver operating characteristic curve (auROC) and the likelihood ratio test were used to evaluate the performance of the models for predicting early mortality. Findings were validated in a cohort of 451 consecutive patients with cirrhosis on waiting list for liver transplantation. Bivariate analyses showed that the following variables correlated with time to death: age, serum bilirubin, serum creatinine, international normalized ratio of prothrombin time, serum albumin, serum sodium, and MELD. Multivariate analysis revealed that MELD, serum sodium, and age were independently associated with the risk of death. The integrated MELD model (iMELD, incorporating serum sodium and age) was better than original MELD in predicting 12-month mortality: auROC increased by 13.4% and the likelihood ratio statistic from 23.5 to 48.2. The improved accuracy of iMELD was confirmed in the validation sample of 451 patients with cirrhosis on the waiting list for liver transplantation by increasing auROC (+8%) and likelihood ratio statistic (from 41.4 to 82.0). This study shows that in patients with cirrhosis, serum sodium and age are predictors of mortality independent of the MELD score. The incorporation of these variables into the original MELD formula improves the predictive accuracy of time to death.

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