Personalised prediction of the individual risk of liver-related events in MASLD using the dynamics of non-invasive tests.

Background

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Aims

The evolution of non-invasive tests of liver fibrosis during follow-up of patients with metabolic dysfunction-associated steatotic liver disease (MASLD) remains difficult to interpret in clinical practice. We aimed to translate the dynamics of non-invasive tests into a personalized prediction of liver-related events (LRE) in MASLD.

Methods

We used the international multicentre VCTE-Prognosis cohort including adult patients with MASLD who underwent liver stiffness measurements (LSM) by vibration-controlled transient elastography. The study outcome was LRE, a composite endpoint including cirrhosis decompensation or hepatocellular carcinoma. A joint latent class model (JLCM) was used to compute dynamic predictions resulting in a personalized estimation of the risk of LRE (0-100% at chosen time horizons).

Results

13,627 patients were included, with 238 LRE occurring during the median follow-up of 4.0 years (IQR: 2.1-5.9). LSM trajectory adjusted on FIB-4 and sex (longitudinal part) and age with platelets (survival part) were selected by the JLCM for LRE prediction. Calibration plots showed very good agreement between the predicted and the observed risk of LRE. The JLCM provided excellent discrimination for LRE with integrated AUROCs increasing from 88.2% at baseline to 92.2% at the 5-year follow-up visit. At the different study visits, 61-80% of the patients who experienced LRE were identified as high risk by the JLCM, versus 39-56% with LSM and 32-60% with FIB-4.

Conclusion

By automatically integrating and interpreting the dynamics of non-invasive tests of liver fibrosis, JLCM enables the personalised prediction of the risk of hard outcomes, rather than the imperfect evaluation of histological surrogates.