Development and validation of a new nomogram to screen for MAFLD.

BACKGROUND AND

aimMetabolic dysfunction-associated fatty liver disease (MAFLD) poses significant health and economic burdens on all nations. Thus, identifying patients at risk early and managing them appropriately is essential. This study's goal was to develop a new predictive model for MAFLD. Additionally, to improve the new model's clinical utility, researchers limited the variables to readily available simple clinical and laboratory measures.

methodsBased on the National Health and Nutrition Examination Survey (NHANES) cycle 2017-2020.3, the study was a retrospective cross-sectional study involving 7300 participants. By least absolute shrinkage and selection operator (LASSO) regression, significant indicators independently associated with MAFLD were identified, and a predictive model called the MAFLD prediction nomogram (MPN) was developed. The study then compared the MPN with six existing predictive models for MAFLD. The model was evaluated by measuring the area under receiver operating characteristic curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), calibration curve, and decision curve analysis (DCA) curve.

resultsIn this study, researchers identified nine predictors from 33 variables, including age, race, arm circumference (AC), waist circumference (WC), body mass index (BMI), alanine aminotransferase (ALT)-to-aspartate aminotransferase (AST) ratio, triglyceride-glucose index (TyG), hypertension, and diabetes. The diagnostic accuracy of the MPN for MAFLD was significantly better than that of the other six existing models in both the training and validation cohorts (AUC 0.868, 95% confidence interval (CI) 0.858-0.877, and AUC 0.863, 95% CI 0.848-0.878, respectively). The MPN showed a higher net benefit than the other existing models.

conclusionsThis nonimaging-assisted nomogram based on demographics, laboratory factors, anthropometrics, and comorbidities better predicted MAFLD than the other six existing predictive models. Using this model, the general population with MAFLD can be assessed rapidly.