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Open Access 16-10-2023 | Hepatocellular Carcinoma | Original Article―Liver, Pancreas, and Biliary Tract
Prediction of outcomes in patients with metabolic dysfunction-associated steatotic liver disease based on initial measurements and subsequent changes in magnetic resonance elastography
Published in: Journal of Gastroenterology | Issue 1/2024
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Background
The prognosis of metabolic dysfunction-associated steatotic liver disease (MASLD) is strongly associated with liver fibrosis. We aimed to investigate whether liver stiffness measurement (LSM) and changes in LSM (ΔLSM) on magnetic resonance elastography (MRE) can predict clinical events in patients with MASLD.
Methods
We included 405 patients with MASLD who underwent at least two MREs. The patients were divided into five groups corresponding to fibrosis stages (0–4) based on initial LSM and classified as progressors (ΔLSM ≥ 19%) or non-progressors (ΔLSM < 19%) based on the difference between the first and last LSM.
Results
The mean follow-up period was 72.6 months, and the mean interval between MREs was 23.5 months. There were 52 (12.8%) progressors and 353 (87.2%) non-progressors. The initial LSM was significantly associated with the cumulative probabilities of decompensated cirrhosis, hepatocellular carcinoma (HCC), liver-related events, extrahepatic malignancies, and overall mortality but not with cardiovascular disease. Progressors had significantly higher hazard ratios (HRs) for decompensated cirrhosis, HCC, and liver-related events but not for extrahepatic malignancies, cardiovascular disease, or overall mortality. Among patients without cirrhosis, the HR for developing cirrhosis among progressors was 60.15. Progressors had a significantly higher risk of liver-related events, even in the low initial LSM (fibrosis stage 0–2) subgroups.
Conclusions
Both initial LSM and ΔLSM can predict liver-related events in patients with MASLD, even for low initial LSM. This integrated assessment can allow more detailed risk stratification compared with single LSM assessments and identify high-risk patients with MASLD among those previously considered as low risk.