The additive value of sarcopenia, myosteatosis and hepatic encephalopathy in the predictivity of model for end-stage liver disease. Issue 11 (November 2019)

• Record Type: Journal Article
• Authors: Lattanzi, Barbara; Nardelli, Silvia; Pigliacelli, Alessandra; Di Cola, Simone; Farcomeni, Alessio; D'Ambrosio, Daria; Gioia, Stefania; Ginanni Corradini, Stefano; Lucidi, Cristina; Mennini, Gianluca; Rossi, Massimo; Merli, Manuela; Riggio, Oliviero
• Is Part Of: Digestive and liver disease. Volume 51:Issue 11(2019)
• Page Start: 1508
• Page End: 1512
• Publication Date: 2019-11
• Subjects: Hepatic encephalopathy -- Model for end-stage liver disease -- Myosteatosis -- Sarcopenia; Digestive organs -- Diseases -- Periodicals; Liver -- Diseases -- Periodicals; 616.33005
• DOI: 10.1016/j.dld.2019.09.004
• Languages: English
• ISSNs: 1590-8658

Abstract

Abstract: Background: Since the use of the Model for End-Stage Liver Disease (MELD) score for establishing the prognosis of cirrhotic patients has been introduced, questions have been raised whether complications of liver cirrhosis would provide additional information. Myosteatosis, sarcopenia and hepatic encephalopathy (HE) are frequent in cirrhosis and may affect prognosis. Aim of the study was analyzing if these factors are independently related to survival and may improve the accuracy of MELD. Methods: 249 cirrhotics that underwent abdominal CT-scan were enrolled. For each patient, information about previous episodes of HE and muscle alterations were obtained. Patients were followed until transplantation or death. Results: History of HE, MELD, sarcopenia and myosteatosis were independently associated with mortality. The MELD-Sarco-Myo-HE score added accuracy to the MELD score alone for 6- and 3-months mortality. By removing HE, as the only not quantifiable parameter of the model, no relevant decrease in accuracy for 6- and 3-months mortality detection was observed. Conclusions: The accuracy of MELD in predicting 3- and 6-months mortality may be improved by considering the muscle alterations. A model considering the above parameters may classify more accurately over 30% of the patients.