Delisting From Clinical Improvement in Liver Cirrhosis: A Machine Learning Decision Tree Analysis
Year: 2026
Type: article
Study design: Observational Study
Abstract: 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.
Source: Transplantation
Authors: Nicole Shu Ying Tang, Margaret L.P. Teng, Asvin Selvakumar, Caleb Wei Hao Ng, N. Apoorva Sasikumar, Ming-Yao Pierre Lim, Donghyun Ko, Howard Tzu-Hao Lee, Alfred Kow, Pojsakorn Danpanichkul, Mark D. Muthiah, Karn Wijarnpreecha, Won-Mook Choi, Hideki Fujii, Cheng Han Ng, Nicholas Syn, Kavish R. Patidar, Emmanuel A. Tsochatzis, Mohammad Shadab Siddiqui, Eunice Xiang-Xuan Tan
Institutions: National University of Singapore, Alexandra Hospital, National University Hospital, Kurume University, National University Health System, Ngee Ann Polytechnic, Bridgeport Hospital, Baylor College of Medicine, Texas Tech University, Texas Tech University Health Sciences Center, University of Arizona College of Medicine- Phoenix, Asan Medical Center, University of Ulsan, Osaka Metropolitan University, Houston Methodist, Houston Methodist Research Institute, University College London, Virginia Commonwealth University, Hunter Holmes McGuire VA Medical Center, Virginia Commonwealth University Medical Center
Language: English
Subfield: Hepatology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7164879787