Predictive modeling and clinical decision tools for risk stratification in steatotic liver disease.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a leading cause of hepatic decompensation and liver-related death, but most patients with MASLD do not develop these liver-related complications. Risk prediction and care pathways are crucial to identify which patients with MASLD are highest risk and link them to appropriate care. Risk prediction is usually done with blood-based algorithms such as Fibrosis-4, AST-platelet ratio index, and more recent scores such as steatosis-associated fibrosis estimator and LiverRisk Score. Second-line tests include enhanced liver fibrosis and imaging-based tests such as vibration-controlled transient elastography, shear wave elastography, or magnetic resonance elastography. We propose a consensus risk stratification care pathway that can be adapted for different clinical settings. We also discuss key needs to improve upon the state of the art: improving diagnosis/prognostic accuracy, especially of blood-based models, optimizing calibration, and ensuring interpretability of predictive models. Finally, we discuss recent advances in clinical decision support systems including best practice advisories, dashboards, and dynamic guidelines. We highlight factors critical to clinical decision support systems, including integration with existing systems and clinician workflows, minimizing additional burden to clinicians, provision of decision support and recommendations at the time/place of decision-making, and continuous evaluation and local user involvement.