RESUMO
Introduction: A prognostic model to predict liver severity in people with metabolic dysfunction-associated steatotic liver disease (MASLD) is very important, but the accuracy of the most commonly used tools is not yet well established. Objective: The meta-analysis aimed to assess the accuracy of different prognostic serological biomarkers in predicting liver fibrosis severity in people with MASLD. Methods: Adults ≥18 years of age with MASLD were included, with the following: liver biopsy and aspartate aminotransferase-to-platelet ratio (APRI), fibrosis index-4 (FIB-4), non-alcoholic fatty liver disease fibrosis score (NFS), body mass index, aspartate aminotransferase/alanine aminotransferase ratio, diabetes score (BARD score), FibroMeter, FibroTest, enhanced liver fibrosis (ELF), Forns score, and Hepascore. Meta-analyses were performed using a random effects model based on the DerSimonian and Laird methods. The study's risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. Results: In total, 138 articles were included, of which 86 studies with 46,514 participants met the criteria for the meta-analysis. The results for the summary area under the receiver operating characteristic (sAUROC) curve, according to the prognostic models, were as follows: APRI: advanced fibrosis (AF): 0.78, any fibrosis (AnF): 0.76, significant fibrosis (SF): 0.76, cirrhosis: 0.72; FIB-4: cirrhosis: 0.83, AF: 0.81, AnF: 0.77, SF: 0.75; NFS: SF: 0.81, AF: 0.81, AnF: 0.71, cirrhosis: 0.69; BARD score: SF: 0.77, AF: 0.73; FibroMeter: SF: 0.88, AF: 0.84; FibroTest: SF: 0.86, AF: 0.78; and ELF: AF: 0.87. Conclusion: The results of this meta-analysis suggest that, when comparing the scores of serological biomarkers with liver biopsies, the following models showed better diagnostic accuracy in predicting liver fibrosis severity in people with MASLD: FIB-4 for any fibrosis, FibroMeter for significant fibrosis, ELF for advanced fibrosis, and FIB-4 for cirrhosis.Clinical trial registration: [https://clinicaltrials.gov/], identifier [CRD 42020180525].