Predicting 90-day mortality in patients with HBV-ACLF: A comparison of machine learning models and logistic...

Acute-on-chronic liver failure (ACLF) is characterized by a high short-term mortality rate. Hepatitis B virus (HBV) infection is a major cause of ACLF in the Asia-Pacific region. This study aimed to compare the performance of multiple prediction models and identify a stable approach for predicting 90-day mortality in patients with HBV-associated ACLF (HBV-ACLF). Clinical data from 577 patients with HBV-ACLF admitted to 2 hospitals were retrospectively collected. Among them, 513 patients from 1 hospital were randomly divided into a training cohort and a testing cohort at a ratio of 6:4, while 64 patients from another hospital were included as an external validation cohort. Six prediction approaches were evaluated, including logistic regression (LR), least absolute shrinkage and selection operator regression (LASSO), support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN). Model performance was assessed using accuracy, the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value. The RF model achieved excellent discrimination in the training cohort, with an AUC of 1.000. However, its performance declined in the testing cohort and external validation cohort, with AUC values of 0.912 and 0.728, respectively. In contrast, the LR model demonstrated more consistent predictive performance across the training, testing, and external validation cohorts, with AUC values of 0.905, 0.928, and 0.849, respectively. Overall, the LR model showed more consistent point estimates of discrimination across the internal testing and external validation cohorts than the more complex models. Although several machine learning approaches demonstrated good predictive performance, the LR showed the most stable and consistent performance across the testing and external validation cohorts. These findings suggest that increased model complexity does not necessarily translate into superior generalizability and that simpler, more interpretable models may provide robust prognostic prediction for patients with HBV-ACLF.