1NAFLD Research Center, Division of Gastroenterology. University of California at San Diego, La Jolla, CA, USA.
2Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
3Department of Gastroenterology and Hepatology, Musashino Red Cross Hospital Tokyo, Japan.
4Department of Gastroenterology and Hepatology, Yokohama City University, Yokohama, Japan.
5Ankara University School of Medicine, Department of Gastroenterology, Ankara Turkey.
6Ankara University School of Medicine, Department of Radiology, Ankara Turkey.
7Department of Gastroenterology and Hepatology, Cedars Sinai, Los Angeles, CA, USA.
8Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA.
9School of Public Health, University of California, San Diego.
Background and aims: Magnetic resonance elastography (MRE) is an accurate, continuous biomarker of liver fibrosis, however, the optimal combination with clinical factors to predict the risk of incident hepatic decompensation is unknown. Therefore, we aimed to develop and validate an MRE-based prediction model for hepatic decompensation for patients with non-alcoholic fatty liver disease (NAFLD).
Approach results: This international multi-center cohort study included participants with NAFLD undergoing MRE from six hospitals. A total of 1,254 participants were randomly assigned as training (n = 627) and validation (n = 627) cohorts. The primary endpoint was hepatic decompensation, defined as the first occurrence of variceal hemorrhage, ascites, or hepatic encephalopathy. Covariates associated with hepatic decompensation on Cox-regression were combined with MRE to construct a risk prediction model in the training cohort then tested in the validation cohort. The median (IQR) age and MRE values were 61 (18) years and 3.5 (2.5) kPa in the training cohort and 60 (20) years and 3.4 (2.5) kPa in the validation cohort. The MRE-based multivariable model included age, MRE, albumin, AST and platelets had excellent discrimination for the 3- and 5-year risk of hepatic decompensation, c-statistic 0.912 and 0.891 respectively, in the training cohort. The diagnostic accuracy remained consistent in the validation cohort with a c-statistic of 0.871 and 0.876 for hepatic decompensation at 3- and 5-years respectively and was superior to FIB-4 in both cohorts (p < 0.05).
Conclusions: An MRE-based prediction model allows for accurate prediction of hepatic decompensation and assists in the risk stratification of patients with NAFLD.