Predicting 90-day mortality in patients with HBV-ACLF using machine learning tools

Background Acute chronic liver failure (ACLF) is characterized by a systemic inflammatory response, mainly associated with hepatitis B virus (HBV) in the Asia-Pacific region, and has a high mortality rate. We aimed to develop a stable and feasible prognostic prediction model based on machine learning (ML) tools to predict 90-day mortality in patients with hepatitis B virus-associated acute-on-chronic liver failure (HBV-ACLF). Method Clinical data from 573 patients with HBV-ACLF across two hospitals were retrospectively collected. Prognostic models of HBV-ACLF were constructed using support vector machine (SVM), decision tree (DT), random forest (RF), K nearest neighbour (KNN), least absolute shrinkage selection operator (LASSO), and logistic regression (LR). Model performance metrics included accuracy, area under the (AUC) receiver operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results In the training cohort, the RF prediction model demonstrated significantly higher AUC, sensitivity, specificity, PPV, and NPV than the LASSO, LR, SVM, DT, and KNN prediction models. However, the AUC of RF in the validation cohort was 0.728, with a decline in accuracy, specificity, and PPV to 0.688, 0.545, and 0.655, respectively. In the training cohort, the LASSO model had the lowest PPV at 0.739, while the KNN model had the lowest sensitivity at 0.694. In the testing and validation cohorts, the SVM and DT models exhibited the lowest sensitivity, both at 0.581. Although LR performed less effectively than RF in the training cohort, it outperformed the RF model in the testing and validation cohorts. Conclusions In summary, the LR predictive model demonstrates higher predictive efficacy and greater stability, making it more practical for clinical treatment decision-making.