Development and validation of a dynamic survival prediction model for patients with acute-on-chronic liver failure

Abstract Background & Aims Acute-on-Chronic-Liver Failure (ACLF) involves an acute deterioration of liver function in patients with chronic liver disease. ACLF is usually associated with a precipitating event and results in the failure of other organ systems and high short-term mortality. Currently-used prediction models fail to adequately estimate prognosis and need for liver transplantation (LT) in ACLF. This study develops and validates a dynamic prediction model for ACLF patients, that uses both longitudinal and survival data. Methods Adult patients on the UNOS waitlist for LT between 11.01.2016-31.12.2019 were included. Repeated model for end-stage liver disease sodium (MELD-Na) measurements were jointly-modeled with Cox survival analysis to develop the ACLF joint model (ACLF-JM). Model validation was done in separate testing data with area under curve (AUC) and prediction errors. An online ACLF-JM tool was created for clinical application. Results In total, 30,533 patients were included. ACLF grade 1 to 3 was present in respectively 16.4, 10.4 and 6.2% of the patients. The ACLF-JM predicted survival significantly (p Conclusions The ACLF-JM dynamically predicts outcome based on current and past disease severity. Prediction performance is excellent over time, even in ACLF-3 patients. Therefore, the ACLF-JM could be used as clinical tool in the evaluation of prognosis and treatment in patients with ACLF. Lay summary Acute-on-chronic liver failure (ACLF) rapidly changes and leads to death. Liver transplantation is used as treatment and the sickest patients are treated first. This study develops a model that predicts survival in ACLF and shows that the newly-developed model performs better than the currently-used model that ranks patients for liver transplant treatment.