Acute-on-chronic liver failure (ACLF) defines a heterogeneous syndrome involving acute decompensation in patients with pre-existing liver disease accompanied by (multi-)organ failure. This study aimed to develop a simple, reliable machine learning (ML) model to predict mortality in ACLF patients receiving intensive care unit (ICU) treatment. Data from 206 patients admitted to the ICU at RWTH Aachen University Hospital between 2015 and 2021 were retrospectively analyzed with ICU mortality as the primary outcome. An ICU mortality prediction model was developed by logistic regression and validated by 5-fold cross validation. Performance metrics were assessed to evaluate the model's accuracy and compare to existing mortality scores. ICU mortality was 60%. The chronic-liver-failure-consortium ACLF score (CLIF-C ACLFs) was the best predictor of ICU mortality. ML generated seven models using five to thirteen features. The best-performing model included CLIF-C ACLFs, number of organ failures, Horovitz quotient (FiO2/PaO2), FiO2 and lactate. The newly developed Aachen ACLF ICU (ACICU) score demonstrated exceptional predictive accuracy for ICU mortality (AUROC 0.96), underscoring its potential for mortality and futility assessment in critically ill ACLF patients complementing existing prognostic tools. The ACICU score www.acicu-score.com is an easy-to-use tool for predicting ICU mortality in patients with ACLF offering high predictive performance.
Keywords: ACLF; Machine learning; Mortality; Prognosis.
© 2024. The Author(s).