Performance of non-invasive tests and histology for the prediction of clinical outcomes in patients with non-alcoholic fatty liver disease: an individual participant data meta-analysis

BackgroundHistologically assessed liver fibrosis stage has prognostic significance in patients with non-alcoholic fatty liver disease (NAFLD) and is accepted as a surrogate endpoint in clinical trials for non-cirrhotic NAFLD. Our aim was to compare the prognostic performance of non-invasive tests with liver histology in patients with NAFLD.MethodsThis was an individual participant data meta-analysis of the prognostic performance of histologically assessed fibrosis stage (F0–4), liver stiffness measured by vibration-controlled transient elastography (LSM-VCTE), fibrosis-4 index (FIB-4), and NAFLD fibrosis score (NFS) in patients with NAFLD. The literature was searched for a previously published systematic review on the diagnostic accuracy of imaging and simple non-invasive tests and updated to Jan 12, 2022 for this study. Studies were identified through PubMed/MEDLINE, EMBASE, and CENTRAL, and authors were contacted for individual participant data, including outcome data, with a minimum of 12 months of follow-up. The primary outcome was a composite endpoint of all-cause mortality, hepatocellular carcinoma, liver transplantation, or cirrhosis complications (ie, ascites, variceal bleeding, hepatic encephalopathy, or progression to a MELD score ≥15). We calculated aggregated survival curves for trichotomised groups and compared them using stratified log-rank tests (histology: F0–2 vs F3 vs F4; LSM: 2·67; NFS: 0·676), calculated areas under the time-dependent receiver operating characteristic curves (tAUC), and performed Cox proportional-hazards regression to adjust for confounding. This study was registered with PROSPERO, CRD42022312226.FindingsOf 65 eligible studies, we included data on 2518 patients with biopsy-proven NAFLD from 25 studies (1126 [44·7%] were female, median age was 54 years [IQR 44–63), and 1161 [46·1%] had type 2 diabetes). After a median follow-up of 57 months [IQR 33–91], the composite endpoint was observed in 145 (5·8%) patients. Stratified log-rank tests showed significant differences between the trichotomised patient groups (p<0·0001 for all comparisons). The tAUC at 5 years were 0·72 (95% CI 0·62–0·81) for histology, 0·76 (0·70–0·83) for LSM-VCTE, 0·74 (0·64–0·82) for FIB-4, and 0·70 (0·63–0·80) for NFS. All index tests were significant predictors of the primary outcome after adjustment for confounders in the Cox regression.InterpretationSimple non-invasive tests performed as well as histologically assessed fibrosis in predicting clinical outcomes in patients with NAFLD and could be considered as alternatives to liver biopsy in some cases

Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study

Background and aims: Detecting 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 techniques, with clinical data and biomarkers to stage and grade patients with NAFLD. Approach and results: Learning data were collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH CRN. Conditions of interest were the 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 imputations. Data were randomly split into training/validation (75/25) sets. A gradient boosting machine was applied to develop 2 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 gradient boosting machine 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 machine learning models for each component, using only clinical predictors. Adding biomarkers only improved the accuracy of fibrosis