AI ML-based nomogram for mortality risk stratification in cirrhotic patients

The AI/ML-based nomogram developed in this study serves as a predictive tool for estimating in-hospital mortality risk among cirrhotic patients with sepsis. This retrospective single-center study analyzed data from 264 patients admitted between January 2018 and July 2025, dividing them into Survivor and Non-survivor groups, with 28.4% succumbing to the condition during hospitalization. To ensure robust model performance, the dataset was split into a training set (70%) and validation set (30%).

Key predictors of mortality identified included alcoholic cirrhosis, Child-Pugh score, mechanical ventilation, total bilirubin (TBiL), and heart rate (HR). These factors were selected using LASSO regression and further optimized with multivariate logistic regression. A nomogram was constructed to visually quantify individual mortality risks based on the weighted contribution of these predictors. The model demonstrated excellent discrimination with an AUC of 0.81 in the training set and 0.83 in the validation set, while calibration plots showed strong agreement between predicted and observed outcomes.

Alcoholic cirrhosis was a significant risk factor, with a 3.7 times higher mortality rate compared to non-alcoholic cases. Higher Child-Pugh scores indicated worsening prognosis, mechanical ventilation doubled mortality risk, and elevated TBiL levels reflected impaired hepatic function. Extreme heart rate abnormalities also correlated with poor outcomes. Infection complications like spontaneous bacterial peritonitis and esophageal variceal bleeding were more frequent among non-survivors.

The nomogram provides clinicians with a quantitative tool for mortality risk stratification, enabling early intervention strategies. Its findings align with global trends in cirrhosis-related mortality, emphasizing its clinical relevance and utility in managing high-risk patients effectively.