Prediction Models Versus Fibrosis-4 Index (FIB-4) for Advanced Fibrosis in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD): A Verification-Audited Systematic Review and Meta-Analysis

Researchers increasingly propose data-driven prediction models for non-invasive fibrosis assessment in metabolic dysfunction-associated steatotic liver disease (MASLD).However, strong predictive performance alone does not show whether a model performs better than Fibrosis-4 Index (FIB-4), a simple and widely used fibrosis score based on routinely available clinical data.This review evaluated whether prediction models provide greater diagnostic discrimination than FIB-4 when both are assessed in the same patients for advanced fibrosis (≥F3).PubMed, Europe PMC, OpenAlex, and Semantic Scholar were searched for studies evaluating prediction models and conventional non-invasive fibrosis tests in adults with MASLD, MASH, NAFLD, or NASH.Eligible head-to-head comparisons required the model and FIB-4 to be evaluated in the same cohort for the same ≥F3 endpoint with verifiable test-specific areas under the receiver operating characteristic curve (AUROCs).We used automation-based screening and quantitative validation, followed by full-text verification, targeted evidence recovery, and final cohort-level adjudication.Separate-cohort biopsy-validation evidence formed the primary analysis, while internal holdout evidence was included secondarily.The effect measure was the within-study difference in AUROC (ΔAUROC), pooled using random-effects restricted maximum likelihood with Hartung-Knapp confidence intervals (CIs).Of 23,420 deduplicated records, 2,892 reports were assessed for eligibility, and 44 potential same-cohort comparisons were identified.Final verification produced three eligible head-to-head studies.The primary separatecohort evidence comprised two studies, with study-level ΔAUROCs of 0.053 and 0.140, respectively.The pooled primary ΔAUROC was 0.086 (95% Hartung-Knapp CI -0.448 to 0.621).Adding the internal holdout study, which reported a ΔAUROC of 0.165, increased the pooled estimate to 0.126 (95% CI -0.023 to 0.276).All three point estimates were positive, but both pooled CIs included no difference.A 500-record reference assessment of automated exclusions identified no false negatives, corresponding to a negative predictive value of 100.0%(95% CI 99.2%-100.0%).The available head-to-head evidence is too sparse and imprecise to establish that prediction models outperform FIB-4.Moreover, all included evaluations were conducted by the original model-development teams rather than by independent third-party investigators.The findings should therefore be considered preliminary and do not support replacing FIB-4.Future studies should prioritise third-party multicentre validation, calibration, clinically actionable thresholds, and direct assessment of net clinical benefit.