Non-Invasive Prediction Models of First Decompensation in Compensated Advanced Chronic Liver Disease - A Meta-Analysis.

Abstract

Background and aims: This review aimed to critically appraise available data to determine the accuracy of non-invasive prediction models (NITs) in identifying patients with cACLD who are at increased risk of a first episode of hepatic decompensation.

Methods: This systematic review and meta-analysis was conducted from all published articles until February 2025. Studies were included if they evaluated performance of a NIT, defined as two or more individual non-invasive markers that had been combined into a prognostic model. Studies were excluded if analysis was done on a population that included those without cACLD, and studies examining only singular prognostic markers (including baseline liver stiffness in isolation). Summary data was extracted from published reports consisting of the c-statistic and/or AUROC and model calibration. This review was registered with PROSPERO (CRD42024608001).

Results: Of 6,540 screened articles, 30 were included consisting of 47,647 participants with cACLD. The articles described 39 prognostic models; of which 19 were suitable for meta-analysis. Random effects meta-analysis found models specifically developed and validated in cACLD provide the best prediction, including the SAVE score (summary c-statistic 0.87, 95% CI 0.82-0.93) and ABC score (0.85, 0.80-0.89). There was limited validation and calibration of all models. Sensitivity analysis of cohorts using an invasive diagnosis for cACLD provided the least heterogeneous outcomes, with all assessed models having an I² < 50%.

Conclusions: CACLD specific NITs offer the best option in predicting decompensation, and future studies should focus on robust validation, calibration and external validation with standardised endpoints to ensure that models are reliable for guiding clinical practice in applicable populations.