Background & Aims: Fibrosis is the strongest predictor for long‐term clinical outcomes among patients with non‐alcoholic fatty liver disease (NAFLD). There is growing interest in employing non‐invasive methods for risk stratification based on prognosis. FIB‐4, NFS and APRI are models commonly used for detecting fibrosis among NAFLD patients. We aimed to synthesize existing literature on the ability of these models in prognosticating NAFLD‐related events.Methods: A sensitive search was conducted in two medical databases to retrieve studies evaluating the prognostic accuracy of FIB‐4, NFS and APRI among NAFLD patients. Target events were change in fibrosis, liver‐related event and mortality. Two reviewers independently performed reference screening, data extraction and quality assessment (QUAPAS tool).Results: A total of 13 studies (FIB‐4:12, NFS: 11, APRI: 10), published between 2013 and 2019, were retrieved. All studies were conducted in a secondary or tertiary care setting, with follow‐up ranging from 1 to 20 years. All three markers showed consistently good prognostication of liver‐related events (AUC from 0.69 to 0.92). For mortality, FIB‐4 (AUC of 0.67‐0.82) and NFS (AUC of 0.70‐0.83) outperformed APRI (AUC of 0.52‐0.73) in all studies. All markers had inconsistent performance for predicting change in fibrosis stage.Conclusions: FIB‐4, NFS, and APRI have demonstrated ability to risk stratify patients for liver‐related morbidity and mortality, with comparable performance to a liver biopsy, although more head‐to‐head studies are needed to validate this. More refined models to prognosticate NAFLD‐events may further enhance performance and clinical utility of non‐invasive markers.

The Enhanced Liver Fibrosis (ELF) test is a non-invasive biomarker, suggested as an appropriate test for advanced liver fibrosis in non-alcoholic fatty liver disease (NAFLD). This systematic review aimed to provide summary estimates of the accuracy of this test against biopsy. In this systematic review, we searched MEDLINE, Embase, Web of Science and the Cochrane Library, for studies included NAFLD patients and undertook both liver biopsy as the reference standard and the ELF test. Two authors independently screened the references, extracted the data and assessed the quality of included studies. Due to the variation in reported thresholds, we used a multiple thresholds random effects model for meta-analysis (diagmeta R-package). The meta-analysis of 11 studies reporting advanced fibrosis and five studies reporting significant fibrosis showed sensitivity of >0.90 of the ELF test for excluding fibrosis at threshold of 7.7. However, as a diagnostic test at high thresholds, the test showed specificity and positive predictive value >0.80, only in very high-prevalence settings (>50%). Desiring specificity of 0.90 for advanced and significant fibrosis resulted in thresholds of 10.18 (sensitivity: 0.57) and 9.86 (sensitivity: 0.55), respectively. The ELF test showed high sensitivity but limited specificity to exclude advanced and significant fibrosis at low cutoffs. The diagnostic performance of the test at higher thresholds was found to be more limited in low prevalence settings. We conclude that clinicians should carefully consider the likely disease prevalence in their practice setting and adopt suitable test thresholds to achieve the desired test performance.

Association between elevated cytokeratin 18 (CK-18) levels and hepatocyte death has made circulating CK-18 a candidate biomarker to differentiate non-alcoholic fatty liver from non-alcoholic steatohepatitis (NASH). Yet studies produced variable diagnostic performance. We aimed to provide summary estimates with increased precision for the accuracy of CK-18 (M30, M65) in detecting NASH and fibrosis among non-alcoholic fatty liver disease (NAFLD) adults. We searched five databases to retrieve studies evaluating CK-18 against a liver biopsy in NAFLD adults. Reference screening, data extraction and quality assessment (QUADAS-2) were independently conducted by two authors. Meta-analyses were performed for five groups based on the CK-18 antigens and target conditions, using one of two methods: linear mixed-effects multiple thresholds model or bivariate logit-normal random-effects model. We included 41 studies, with data on 5,815 participants. A wide range of disease prevalence was observed. No study reported a pre-defined cut-off. Thirty of 41 studies provided sufficient data for inclusion in any of the meta-analyses. Summary AUC [95% CI] were: 0.75 [0.69–0.82] (M30) and 0.82 [0.69–0.91] (M65) for NASH; 0.73 [0.57–0.85] (M30) for fibrotic NASH; 0.68 (M30) for significant (F2-4) fibrosis; and 0.75 (M30) for advanced (F3-4) fibrosis. Thirteen studies used CK-18 as a component of a multimarker model. For M30 we found lower diagnostic accuracy to detect NASH compared to previous meta-analyses, indicating a limited ability to act as a stand-alone test, with better performance for M65. Additional external validation studies are needed to obtain credible estimates of the diagnostic accuracy of multimarker models.

(1) Background: FibroTest™ is a multi-marker panel, suggested by guidelines as one of the surrogate markers with acceptable performance for detecting fibrosis in patients with non-alcoholic fatty liver disease (NAFLD). A number of studies evaluating this test have been published after publication of the guidelines. This study aims to produce summary estimates of FibroTest™ diagnostic accuracy. (2) Methods: Five databases were searched for studies that evaluated FibroTest™ against liver biopsy as the reference standard in NAFLD patients. Two authors independently screened the references, extracted data, and assessed the quality of included studies. Meta-analyses of the accuracy in detecting different levels of fibrosis were performed using the bivariate random-effects model and the linear mixed-effects multiple thresholds model. (3) Results: From ten included studies, seven were eligible for inclusion in our meta-analysis. Five studies were included in the meta-analysis of FibroTest™ in detecting advanced fibrosis and five in significant fibrosis, resulting in an AUC of 0.77 for both target conditions. The meta-analysis of three studies resulted in an AUC of 0.69 in detecting any fibrosis, while analysis of three other studies showed higher accuracy in cirrhosis (AUC: 0.92). (4) Conclusions: Our meta-analysis showed acceptable performance (AUC > 0.80) of FibroTest™ only in detecting cirrhosis. We observed more limited performance of the test in detecting significant and advanced fibrosis in NAFLD patients. Further primary studies with high methodological quality are required to validate the reliability of the test for detecting different fibrosis levels and to compare the performance of the test in different settings.

Background & Aims: Detecting non-alcoholic steatohepatitis (NASH) remains challenging, while at-risk NASH (steatohepatitis and F≥ 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning (ML) techniques, with clinical data and biomarkers to stage and grade non-alcoholic fatty liver disease (NAFLD) patients. Approach & Results: Learning data were collected in the LITMUS Metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH-CRN. Conditions of interest were clinical trial definition of NASH (NAS≥ 4;53%), at-risk NASH (NASH with F≥ 2;35%), significant (F≥ 2;47%) and advanced fibrosis (F≥ 3;28%). Thirty-five predictors were included. Missing data were handled by multiple imputation. Data were randomly split into training/validation (75/25) sets. Gradient boosting machine (GBM) was applied to develop two models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models. Clinical GBM models for steatosis/inflammation/ballooning had AUCs of 0.94/0.79/0.72. There were no improvements when biomarkers were included. The direct NASH model produced AUCs (clinical/extended) of 0.61/0.65. The composite NASH model performed significantly better (0.71) for both variants. The composite at-risk NASH model had an AUC of 0.83 (clinical and extended), an improvement over the direct model. Significant fibrosis models had AUCs (clinical/extended) of 0.76/0.78. The extended advanced fibrosis model (0.86) performed significantly better than the clinical version (0.82). Conclusions: Detection of NASH and at-risk NASH can be improved by constructing independent ML models for each component, using only clinical predictors. Adding biomarkers only improved accuracy for fibrosis. Export

The prevalence and severity of non-alcoholic fatty liver disease (NAFLD) is increasing, yet adequately validated tests for care paths are limited and non-invasive markers of disease progression are urgently needed. The aim of this work was to summarize the performance of Pro-C3, a biomarker of active fibrogenesis, in detecting significant fibrosis (F ≥ 2), advanced fibrosis (F ≥ 3), cirrhosis (F4) and non-alcoholic steatohepatitis (NASH) in patients with NAFLD. A sensitive search of five databases was performed in July 2021. Studies reporting Pro-C3 measurements and liver histology in adults with NAFLD without co-existing liver diseases were eligible. Meta-analysis was conducted by applying a bivariate random effects model to produce summary estimates of Pro-C3 accuracy. From 35 evaluated reports, eight studies met our inclusion criteria; 1568 patients were included in our meta-analysis of significant fibrosis and 2058 in that of advanced fibrosis. The area under the summary curve was 0.81 (95% CI 0.77–0.84) in detecting significant fibrosis and 0.79 (95% CI 0.73–0.82) for advanced fibrosis. Our results support Pro-C3 as an important candidate biomarker for non-invasive assessment of liver fibrosis in NAFLD. Further direct comparisons with currently recommended non-invasive tests will demonstrate whether Pro-C3 panels can outperform these tests, and improve care paths for patients with NAFLD.