{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T06:05:02Z","timestamp":1790057102149,"version":"4.0.1"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats: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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>\n                      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                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20092,267) 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)\u2009\u2265\u2009275\u2005dB\/m together with a liver stiffness measurement (LSM)\u2009&amp;gt;\u20098\u2005kPa and\/or FibroScan-AST score (FAST)\u2009&amp;gt;\u20090.35. 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                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20093,411,069) 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:italic>vs<\/jats:italic>\n                      . 0.64, respectively,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001). 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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats: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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1829881","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T05:40:13Z","timestamp":1790055613000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a machine learning model to identify individuals with VCTE-derived at-risk MASH in a Spanish Mediterranean region"],"prefix":"10.3389","volume":"8","author":[{"given":"Marta","family":"Cedenilla","sequence":"first","affiliation":[{"name":"Value & Implementation, Global Medical & Scientific Affairs, Merck Sharp & Dohme Spain","place":["Madrid, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Marti-Aguado","sequence":"additional","affiliation":[{"name":"Data Science Platform Biomedical Research Institute","place":["Valencia, Spain"]},{"name":"Digestive Disease Department, Clinic University Hospital of Valencia","place":["Valencia, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josep","family":"Redon","sequence":"additional","affiliation":[{"name":"Data Science Platform Biomedical Research Institute","place":["Valencia, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inma","family":"Sauri-Ferrer","sequence":"additional","affiliation":[{"name":"Data Science Platform Biomedical Research Institute","place":["Valencia, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorge","family":"Navarro","sequence":"additional","affiliation":[{"name":"Data Science Platform Biomedical Research Institute","place":["Valencia, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Desamparados","family":"Escudero","sequence":"additional","affiliation":[{"name":"Data Science 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