A Novel Backward Stepwise Logistic Regression and Classification and Regression Tree Model to Predict 180-day Clinical Outcomes in Hepatitis B Virus-Acute-on-Chronic Liver Failure Patients

As the era of precision and personalized medicine is gaining exponential positive gain in the field of medicine, there is a positive shift towards a more evidence-based patient care approach for patients with hepatological diseases. One factor that is crucial in any physician’s decision-making efforts involves the application of novel innovative approaches that can enhance predicting survival outcome. Acute-on-chronic liver failure (ACLF) is a perfect example of how liver can rapidly deteriorate, and the hepatitis B virus (HBV) is one crucial culprit. Patients can experience organ failure that leads to their mortality, and in this article the authors clearly described the use of backward stepwise logistic regression (LR) and classification and regression tree (CART) analysis to derive two predictive models and then compared them with the model of end-stage liver disease (MELD) score for novel prognostic models of the 180-day outcome for patients with HBV-ACLF