Risk-stratified assessment of 180-day mortality in severe alcoholic hepatitis

Abstract

BACKGROUND

The clinical heterogeneity of severe alcoholic hepatitis (SAH) poses challenges in precision prognosis.

AIM

To identify distinct clinical phenotypes of SAH and develop a simplified, risk-stratified model for predicting 180-day mortality.

METHODS

A total of 1536 patients with SAH were categorized into training and testing cohorts. After comparing nine machine learning (ML) models, we employed the SHapley Additive exPlanations (SHAP) analysis to identify the optimal model and key predictors. Based on these predictors [creatinine (CRE) and total bilirubin (TBIL) levels, prothrombin time, and cholinesterase activity], we performed latent class analysis (LCA) to determine underlying phenotypes. We developed a two-step risk stratification system: Primary and secondary stratifications based on CRE and TBIL level, respectively.

RESULTS

Overall, 1536 patients were enrolled. Among the nine ML models, XGBoost demonstrated the best performance (test set area under the curve of 0.848). SHAP analysis identified CRE as the most important predictor. The subsequent LCA revealed two distinct phenotypes primarily differentiated by CRE level, each associated with markedly different 180-day survival rates: 78.5% and 47.9%, respectively. Within both CRE subtypes, TBIL level was the most significant prognostic factor. The final two-step CRE-TBIL risk assessment stratified patients into four tiers, demonstrating significant mortality differences ranging from 5% to over 50% in both the training and validation cohorts.

CONCLUSION

By integrating ML interpretability with phenotypic analysis, we developed a CRE-TBIL-based 180-day mortality risk assessment card to enable clinicians to rapidly evaluate risk and guide treatment decisions.

Keywords: Maddrey discriminant function; Model for end-stage liver disease; Machine learning; Prognosis; Predictive model

Core Tip: This study identified distinct clinical phenotypes of severe alcoholic hepatitis (SAH) using interpretable machine learning, enhancing prognosis precision. By analyzing 1536 patients, we found that creatinine and total bilirubin levels are key predictors of 180-day mortality. A novel two-step risk stratification model revealed significant mortality variations, classifying patients into four tiers with survival rates ranging from 5% to over 50%. This tool facilitates rapid risk assessment and informed treatment decisions, marking a significant advancement in managing SAH.