Prognostic durability of liver fibrosis tests and improvement in predictive performance for mortality by combining tests.

BACKGROUND AND AIM: There is currently no recommended time interval between noninvasive fibrosis measurements for monitoring chronic liver diseases. We determined how long a single liver fibrosis evaluation may accurately predict mortality, and assessed whether combining tests improves prognostic performance.

METHODS: We included 1559 patients with chronic liver disease and available baseline liver stiffness measurement (LSM) by Fibroscan, aspartate aminotransferase to platelet ratio index (APRI), FIB-4, Hepascore, and FibroMeterV2G .

RESULTS: Median follow-up was 2.8 years during which 262 (16.8%) patients died, with 115 liver-related deaths. All fibrosis tests were able to predict mortality, although APRI (and FIB-4 for liver-related mortality) showed lower overall discriminative ability than the other tests (differences in Harrell's C-index: P < 0.050). According to time-dependent AUROCs, the time period with optimal predictive performance was 2-3 years in patients with no/mild fibrosis, 1 year in patients with significant fibrosis, and <6 months in cirrhotic patients even in those with a model of end-stage liver disease (MELD) score <15. Patients were then randomly split in training/testing sets. In the training set, blood tests and LSM were independent predictors of all-cause mortality. The best-fit multivariate model included age, sex, LSM, and FibroMeterV2G with C-index = 0.834 (95% confidence interval, 0.803-0.862). The prognostic model for liver-related mortality included the same covariates with C-index = 0.868 (0.831-0.902). In the testing set, the multivariate models had higher prognostic accuracy than FibroMeterV2G or LSM alone for all-cause mortality and FibroMeterV2G alone for liver-related mortality.

CONCLUSIONS: The prognostic durability of a single baseline fibrosis evaluation depends on the liver fibrosis level. Combining LSM with a blood fibrosis test improves mortality risk assessment.