Data Sheet 1_Development of a machine learning model to identify individuals with VCTE-derived at-risk MASH in a Spanish Mediterranean region.docx

The final model, named VARM-7 (Valencia At-Risk MASH), comprised age and six commonly available laboratory variables. VARM-7 was sequentially applied to the Valencian public healthcare database (n = 3,411,069) to estimate the proportion of at-risk MASH individuals in the overall population, and in target populations with type 2 diabetes mellitus (T2DM) and obesity.</p>Results<p>In the independent validation dataset, VARM-7 exhibited a significantly higher AUROC compared with Fibrosis-4 (FIB-4) for identifying at-risk MASH (0.84 vs. 0.64, respectively, p < 0.001).

When sequentially applied to the overall Valencian Community population, 11.7% of individuals were estimated to have at-risk MASH, with increasing estimates up to 29.4% and 44.0% in subjects with obesity and T2DM, respectively.</p>Conclusion<p>VARM-7 showed strong performance in identifying individuals with a VCTE-derived at-risk MASH. Our model could improve disease screening and referral pathways, but external validation and prognostic evaluation are needed before its implementation.</p>