The fatty liver index (FLI) had the highest diagnostic accuracy for detecting MAFLD among five non-invasive models, with an AUROC of 0.793, sensitivity of 57.0%, and specificity of 83.8%.
Cross-Sectional
Yes
What is the diagnostic accuracy of five common hepatic steatosis algorithms for detecting metabolic-associated fatty liver disease (MAFLD) in a general population?
Non-invasive models, particularly the fatty liver index (FLI), offer satisfactory diagnostic performance for detecting MAFLD in the general population, though their accuracy is reduced in individuals with normal BMI.
Effect estimate: AUROC 0.793
BACKGROUND AND AIM: Non-invasive hepatic steatosis algorithms are recommended in detecting metabolic-associated fatty liver disease (MAFLD) in epidemiological studies. However, the diagnostic accuracy of these models is unclear. This study aimed to evaluate the diagnostic efficiency of five common models in a national survey population. METHODS: The Third National Health and Nutrition Examination Survey (NHANES III) datasets were used in this study. The fatty liver index (FLI), hepatic steatosis index (HSI), non-alcoholic liver disease-liver fat score (NAFLD-LFS), the steato text (ST), and visceral adiposity index (VAI) were evaluated. RESULTS: The prevalence of MAFLD in the general population was 31.2%. The proportion of MAFLD estimated using the NAFLD-LFS (30.8%) was the closest to the real number, whereas the ST model (66.1%) significantly overestimated the prevalence of MAFLD in this cohort. The FLI (36.9%) and HSI models (38.5%) also slightly overestimated the prevalence of MAFLD in the study population. The FLI had the highest area under the receiver operating characteristic (AUROC) value (0.793) among all models, with a sensitivity of 57.0%, a specificity of 83.8%, a positive predictive value (PPV) of 67.3%, and a negative predictive value (NPV) of 77.0%. The combination of the original algorithm with additional metabolic dysfunction criteria did not improve the diagnostic efficiency. The discriminative ability for MAFLD in all models was lower in participants with a normal body mass index (BMI). CONCLUSIONS: Non-invasive models, especially the FLI, have satisfactory diagnostic performance in detecting MAFLD. However, models in people with normal BMIs require further development.
Liu et al. (Thu,) conducted a cross-sectional in Metabolic-associated fatty liver disease (MAFLD). Non-invasive hepatic steatosis algorithms (FLI, HSI, NAFLD-LFS, ST, VAI) was evaluated on Diagnostic efficiency for MAFLD (AUROC 0.793). The fatty liver index (FLI) had the highest diagnostic accuracy for detecting MAFLD among five non-invasive models, with an AUROC of 0.793, sensitivity of 57.0%, and specificity of 83.8%.