Improved mortality prediction for pediatric acute liver failure using dynamic prediction strategy

Objectives To develop and validate a prediction tool for pediatric acute liver failure (PALF) mortality risks that captures the rapid and heterogeneous clinical course for accurate and updated prediction. Methods Data included 1144 participants with PALF enrolled during three phases of the PALF registry study over 15 years. Using joint modeling, we built a dynamic prediction tool for mortality by combining longitudinal trajectories of multiple laboratory and clinical variables. The predictive performance for 7‐day and 21‐day mortality was assessed using the area under curve (AUC) through cross‐validation and split‐by‐time validation. Results We constructed a prognostic joint model that combines the temporal trajectories of international normalized ratio, total bilirubin, hepatic encephalopathy, platelet count, and serum creatinine. Dynamic prediction using updated information improved predictive performance over static prediction using the information at enrollment (Day 0) only. In cross‐validation, AUC increased from 0.784 to 0.887 when measurements obtained between Days 1 and 2 were incorporated. AUC remained similar when we used the earlier subset of the sample for training and the later subset for testing. Conclusions Serial measurements of five variables in the first few days of PALF capture the dynamic clinical course of the disease and improve risk prediction for mortality. Continuous disease monitoring and updating risk prognosis are beneficial for timely and judicious medical decisions. Visual What is Known Mortality risk prediction for PALF is critical for medical decision‐making. However, existing prognostic tools do not effectively utilize information in the rapid and heterogeneous clinical course of PALF. What is New A prognostic model is developed to capture the dynamic clinical course of PALF by integrating the trajectories of five longitudinal variables. The model achieves favorable performance in cross‐validation and temporal‐validation, facilitating early detection of high‐risk patients.