2026-10-14T14:18:29Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33962083 2026-09-22T05:38:31Z category_932 portal_316 item_type_3 month_year_09_2026

Data Sheet 1_Development of a machine learning model to identify individuals with VCTE-derived at-risk MASH in a Spanish Mediterranean region.docx Marta Cedenilla (25087825) David Marti-Aguado (16739571) Josep Redon (179759) Inma Sauri-Ferrer (25087828) Jorge Navarro (213451) Desamparados Escudero (25087831) Anton Gomez (25087834) Luis Cea-Calvo (5231261) Carlos J. Martos-Rodriguez (25087837) Javier Díaz Carnicero (25087840) Health Informatics artificial intelligence machine learning metabolic dysfunction-associated steatohepatitis metabolic dysfunction–associated steatotic liver disease non-invasive test Introduction<p>While current guidelines recommend screening for liver disease in target populations, existing non-invasive tests have limitations in identifying at-risk metabolic dysfunction-associated steatohepatitis (MASH).</p>Materials and methods<p>This retrospective study used two large datasets from the general population in the Valencian Community region (Spain). The primary goal was to develop a machine learning model to identify individuals with at-risk MASH. The model was constructed using a well-characterized dataset (n = 2,267) with available vibration-controlled transient elastography (VCTE). Supervised machine learning models (Random Forest and XGBoost) were trained using a 10-fold cross-validation to classify at-risk MASH individuals, defined as those with a controlled attenuation parameter (CAP) ≥ 275 dB/m together with a liver stiffness measurement (LSM) > 8 kPa and/or FibroScan-AST score (FAST) > 0.35. 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> 2026-09-22T05:38:31Z Dataset Dataset 10.3389/fdgth.2026.1829881.s001 https://figshare.com/articles/dataset/Data_Sheet_1_Development_of_a_machine_learning_model_to_identify_individuals_with_VCTE-derived_at-risk_MASH_in_a_Spanish_Mediterranean_region_docx/33962083 CC BY 4.0