DOI 10.1097/hep.0000000000001841
Shear wave elastography demonstrates similar risk stratification performance to vibration-controlled transient elastography in FIB-4-based two-step algorithms for MASLD.
Аннотация
BACKGROUND AND AIMS: The role of shear wave elastography (SWE) in two-step MASLD risk stratification remains unclear. We compared SWE with vibration-controlled transient elastography (VCTE) using American Gastroenterological Association (AGA) and European Association for the Study of the Liver (EASL) algorithms.
APPROACH AND
RESULTS
Patients with MASLD who underwent both VCTE and SWE during the same session between 2019 and 2025 were included. Risk stratification was performed using AGA-based and EASL-based two-step approaches. The primary endpoint was liver-related events (LREs). Among 2817 patients, the AGA-SWE algorithm classified 2196, 140, and 481 patients as low-risk, intermediate-risk, and high-risk, respectively. Using the AGA-SWE algorithm, both intermediate-risk and high-risk groups showed increased risks of LREs compared with the low-risk group [subdistribution hazard ratio (sHR) 7.60 and 9.86, respectively]. The EASL-SWE algorithm also stratified LRE risk relative to the low-risk group, with higher sHRs observed for the intermediate-low, intermediate-high, and high-risk groups (sHR 2.99, 10.83, and 18.87, respectively). The integrated time-dependent area under the curve through 60 months was 0.770 [95% confidence interval (CI): 0.709, 0.827] and 0.775 (95% CI: 0.712, 0.835) for AGA-SWE and AGA-VCTE, respectively, and 0.804 (95% CI: 0.753, 0.849) and 0.805 (95% CI: 0.750, 0.852) for EASL-SWE and EASL-VCTE, respectively, with no significant differences between SWE and VCTE in either algorithm. No significant net reclassification improvement was observed between SWE-based and VCTE-based AGA algorithms. SWE and VCTE demonstrated a strong correlation (Spearman ρ = 0.713, 95% CI: 0.691-0.735, p <0.001).
CONCLUSIONS
SWE-based two-step risk stratification showed similar predictive performance to VCTE-based models for predicting LREs in MASLD, although these findings do not establish formal equivalence.
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