Machine Learning-Based Algorithms for the Prediction of 90-Day Survival in Patients with Liver Failure Receiving Artificial Liver Therapy

Bo Deng^1,2*Chengzhi Bai¹Huaqian Xu¹Xue Zhang¹Ying Deng³

• ¹Chinese People's Liberation Army Western Theater General Hospital, Chengdu, China
• ²Chengdu Medical College, Chengdu, China
• ³West China Hospital of Sichuan University, Chengdu, China

Background Liver failure is associated with high short-term mortality, and the predictive value of clinical factors for patients undergoing artificial liver therapy is uncertain. We aim to develop prognostic models using several machine learning algorithms to predict 90-day survival in patients with liver failure undergoing artificial liver therapy. Methods We retrospectively enrolled hospitalized patients with liver failure who received artificial liver therapy in our center between December 2017 and December 2021. Prognostic characteristics were chosen by the least absolute shrinkage and selection operator (LASSO) regression and independent predictors by stepwise logistic regression analysis. Five machine learning algorithms—logistic regression (LR), random forest (RF), support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and k-nearest neighbor (KNN)—were used to build and validate models to predict 90-day survival following ALSS. Model performance was assessed by the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Results A total of 197 patients were included in this study. LASSO regression, based on patient admission data, identified the top 15 prognostic features, and stepwise logistic regression analysis determined that age, direct bilirubin, retinol, alpha-fetoprotein, and thrombin time were independent predictors. Among the five machine learning models, LR achieved the highest predictive performance with an AUC of 0.884, accuracy of 75.0%, followed by RF (AUC = 0.797), KNN (AUC = 0.788), XGBoost (AUC = 0.769) and SVM (AUC = 0.732). The predictive performance of LR models based on longitudinal data, using patient characteristics from the day before treatment, had an AUC of 0.869, and from the day after treatment, an AUC of 0.859. Conclusion Machine learning models showed promising performance in predicting 90-day survival in liver failure patients receiving artificial liver support therapy, potentially supporting individualized prognostic assessment.