ArticleHepatology international2026
A simple clinical model for predicting multiple liver histologic injuries in obese individuals undergoing bariatric surgery.
Article in Hepatology international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundMetabolic dysfunction-associated steatotic liver disease (MASLD) is common among obese individuals undergoing bariatric surgery, but liver biopsy is invasive and existing noninvasive tools generally assess single histologic features. We aimed to develop and externally validate a simple framework for predicting multiple liver histologic injuries.
methodsWe included 909 obese individuals undergoing sleeve gastrectomy with intraoperative liver biopsy in the training cohort and 186 temporally separated patients in the internal test cohort. Six separate outcome-specific binary classification models were developed, one for each histologic outcome: steatosis, hepatocyte ballooning, significant fibrosis, metabolic dysfunction-associated steatohepatitis (MASH), NAS ≥5, and at-risk MASH. Multiple machine-learning algorithms were evaluated, and the best-performing algorithm was selected for each outcome. A simplified three-variable TabPFN framework using alanine aminotransferase, homeostasis model assessment of insulin resistance, and peripheral monocyte ratio was developed, with one binary model for each outcome. External validation included two biopsy-confirmed cohorts (n = 103 and 217) and the U.S. NHANES cohort (n = 4,299) using elastography-based assessments.
resultsThe models demonstrated good discrimination for multiple histologic liver injuries in the test cohort, with ROC-AUCs ranging from 0.73 to 0.86 across outcomes. The simplified three-variable framework showed consistent external performance, achieving ROC-AUCs of 0.84 for ballooning, 0.86 for steatosis, 0.79 for significant fibrosis, and 0.75 for MASH. The framework demonstrated generalizability in the NHANES population.
conclusionsThis externally validated framework may support noninvasive identification of multiple liver histologic injuries and perioperative liver risk stratification in individuals undergoing bariatric surgery, thereby improving the clinical accessibility of liver pathology assessment.
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