OBJECTIVE Acute-on-chronic liver failure (ACLF) is usually associated with a precipitating event and results in the failure of other organ systems and high short-term mortality. Current 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 patients with ACLF 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 modelled with Cox survival analysis to develop the ACLF joint model (ACLF-JM). Model validation was carried out using 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 16.4%, 10.4% and 6.2% of patients, respectively. The ACLF-JM predicted survival significantly (p <0.001) better than the MELD-Na score, both at baseline and during follow-up. For 28- and 90-day predictions, ACLF-JM AUCs ranged between 0.840-0.871 and 0.833-875, respectively. Compared to MELD-Na, AUCs and prediction errors were improved by 23.1%-62.0% and 5%-37.6% respectively. Also, the ACLF-JM could have prioritized patients with relatively low MELD-Na scores but with a 4-fold higher rate of waiting list mortality. CONCLUSIONS The ACLF-JM dynamically predicts outcome based on current and past disease severity. Prediction performance is excellent over time, even in patients with ACLF-3. Therefore, the ACLF-JM could be used as a clinical tool in the evaluation of prognosis and treatment in patients with ACLF. BACKGROUND Acute-on-chronic liver failure (ACLF) progresses rapidly and often leads to death. Liver transplantation is used as a treatment and the sickest patients are treated first. In this study, we develop a model that predicts survival in ACLF and we show that the newly developed model performs better than the currently used model for ranking patients on the liver transplant waiting list.