Delisting From Clinical Improvement in Liver Cirrhosis: A Machine Learning Decision Tree Analysis.

BACKGROUND: Following therapeutic advancements, recompensation has gained increasing recognition in patients on waitlist for liver transplantation (LT). Identifying key predictors of waitlist removal because of improvement can enhance prognostication and resource allocation. We hence examined predictors of improvement-related waitlist removal using a machine learning-based approach with data from the United Network for Organ Sharing database. METHODS: In this retrospective cohort study, adult LT waitlist candidates from 2000 to 2025 in the United Network for Organ Sharing registry were included. A random survival forest model was applied to examine key predictors associated with improvement-related waitlist removal, while accounting for death and LT as competing risks. Variable importance (VIMP) measure and minimal depth were used to guide variable selection. Model performance was evaluated using the concordance index, Brier scores, and time-dependent area under the curve. RESULTS: The cohort included 127 978 individuals listed for LT. Eight thousand four hundred ninety-three (6.6%) were delisted because of clinical improvement. The random survival forest model demonstrated strong performance and discriminatory ability overall at 1, 5, and 15 y (concordance index was 0.777, 0.771, and 0.781; time-dependent area under the curve was 0.78, 0.78, and 0.80). Brier scores were reduced relative to the reference. Strong predictors of recovery highlighted in both VIMP and minimal depth-based assessments of VIMP included diagnosis, age, and serum albumin. CONCLUSIONS: Identified variables could inform the development of robust predictive models to guide individualized decision-making for LT. With further validation and integration into clinical workflows, such models could enhance prognostication of patient trajectory on the LT waitlist and facilitate appropriate resource allocation.