Affiliation:
1. Department of Gastroenterology The Third Affiliated Hospital of Sun Yat‐Sen University No. 600 Tianhe Road Guangzhou 510630 China
2. Guangdong Provincial Key Laboratory of Liver Disease Research Guangzhou China
3. Department of Gastrointestinal Endoscopy Center, The Eighth Affiliated Hospital Sun Yat‐sen University 518033 Shenzhen China
Abstract
AbstractBackground and AimLiver stiffness measurements (LSMs) are promising for monitoring disease progression or regression. We assessed the prognostic significance of dynamic changes in LSM over time on liver‐related events (LREs) and death in patients with chronic hepatitis B (CHB) and compensated advanced chronic liver disease (cACLD).MethodsThis retrospective study included 1272 patients with CHB and cACLD who underwent at least two measurements, including LSM and fibrosis score based on four factors (FIB‐4). ΔLSM was defined as [(follow‐up LSM − baseline LSM)/baseline LSM × 100]. We recorded LREs and all‐cause mortality during a median follow‐up time of 46 months. Hazard ratios (HRs) and confidence intervals (CIs) for outcomes were calculated using Cox regression.ResultsBaseline FIB‐4, baseline LSM, ΔFIB‐4, ΔLSM, and ΔLSM/year were independently and simultaneously associated with LREs (adjusted HR, 1.04, 95% CI, 1.00–1.07; 1.02, 95% CI, 1.01–1.03; 1.06, 95% CI, 1.03–1.09; 1.96, 95% CI, 1.63–2.35, 1.02, 95% CI, 1.01–1.04, respectively). The baseline LSM combined with the ΔLSM achieved the highest Harrell's C (0.751), integrated AUC (0.776), and time‐dependent AUC (0.737) for LREs. Using baseline LSM and ΔLSM, we proposed a risk stratification method to improve clinical applications. The risk proposed stratification based on LSM performed well in terms of prognosis: low risk (n = 390; reference), intermediate risk (n = 446; HR = 3.38), high risk (n = 272; HR = 5.64), and extremely high risk (n = 164; HR = 11.11).ConclusionsBaseline and repeated noninvasive tests measurement allow risk stratification of patients with CHB and cACLD. Combining baseline and dynamic changes in the LSM improves prognostic prediction.
Funder
Science Fund for Distinguished Young Scholars of Guangdong Province
National Natural Science Foundation of China
Natural Science Foundation of Guangdong Province
National Key Research and Development Program of China