Machine learning-based risk factors for acute-on-chronic liver failure in alcohol-associated liver disease.

BACKGROUND

In patients with alcohol-associated liver disease (ALD), heavy and prolonged alcohol consumption can trigger acute-on-chronic liver failure (ACLF), a condition associated with high early mortality and significant clinical challenges. Early identification of patients at risk is critical for improving outcomes.

AIMS: We conducted a retrospective observational study of patients diagnosed with ALD between January 2000 and December 2024 to develop a predictive model for ACLF.

METHODS

Key clinical indicators were selected using LASSO-regularized logistic regression (LR). The final LR model was visualized as a nomogram and compared with four additional machine learning algorithms. Model performance was evaluated using ten-fold cross-validation and area under the curve (AUC), while feature importance was assessed with Shapley Additive exPlanations values.

RESULTS

Among 210 patients with ALD, LASSO identified four independent predictors of ACLF: total bilirubin (TBIL), folate, vitamin B12 (VitB12) and the difference from the normal value of prothrombin time (ΔPT). The LR model achieved an AUC of 0.970, indicating excellent predictive accuracy.

CONCLUSION

We developed a robust clinical prediction model combining LR and machine learning approaches. TBIL, folate, VitB12 and ΔPT are key prognostic markers that may enable early risk stratification and timely intervention, potentially reducing ACLF incidence in ALD patients.