Analysis of risk factors for 90-day mortality in patients with acute-on-chronic liver failure and machine learning application

Abstract

Abstract: Objective To explore the risk factors for 90-day mortality in patients with acute-on-chronic liver failure (ACLF) and construct a machine learning-based prediction model. Methods A single-center retrospective cohort study was conducted, including 534 ACLF patients admitted to Beijing Ditan Hospital from June 1, 2020, to May 31, 2024. The dataset was divided into training and test sets at a 7∶3 ratio. Variables were screened via Lasso regression and 10-fold cross-validation, and a random forest algorithm was used to build the model, which was validated by Kaplan-Meier curves, time-dependent ROC curves, and SHAP value analysis. Results Prothrombin activity (PTA), neutrophils, serum phosphorus, blood ammonia, and neutrophil-to-lymphocyte ratio (NLR) were identified as independent risk factors. The area under the curve (AUC) of the random forest time-dependent ROC curve model for predicting 28-day and 90-day prognosis was 0.89 and 0.88, respectively, with the AUC of the validation set being 0.88 and 0.82, respectively. Determined by the maximum goodness-of-fit method via the surv_cutpoint function, the optimal cut-off value for the model score was 10.32. Patients were divided into high-risk and low-risk groups based on this value, and the survival difference between the two groups was statistically significant (P<0.001). Compared with other scoring systems, the forest plot model showed better AUC values (0.89, 0.83) in predicting 28-day and 90-day prognosis. SHAP value visualization indicated that the weight of each factor was in the order of: NLR > blood ammonia > PTA > neutrophils > serum phosphorus; in the high-risk group, the SHAP weight values of NLR, blood ammonia, neutrophil count, and serum phosphorus increased by 6.56, 3.73, 1.42, and 0.32, respectively, while the SHAP weight value of PTA decreased by 1.37, all of which were associated with an increased risk of death. Conclusion The study confirms that PTA, neutrophils, serum phosphorus, blood ammonia, and NLR are core factors affecting the short-term prognosis of ACLF patients. It also verifies the advantages of machine learning in prognostic prediction, providing a new means for precise clinical intervention.

Key words: Acute-on-chronic liver failure, 90-day mortality, Risk factors, Machine learning, Prediction model

GUO He-bing, LIU Jing-yuan, WAN Gang, LI Ang. Analysis of risk factors for 90-day mortality in patients with acute-on-chronic liver failure and machine learning application[J]. Chinese Hepatolgy, 2026, 31(4): 569-577.

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