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

Nicole Shu Ying Tang¹, Margaret L P Teng^1,2,3, Asvin Selvakumar²

¹Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.

Transplantation

|June 16, 2026

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Summary

Predicting clinical improvement in liver transplant candidates is crucial. Diagnosis, age, and serum albumin are key factors influencing waitlist removal due to recovery, aiding resource allocation.

Area of Science:

Hepatology and Transplant Surgery
Medical Informatics and Machine Learning

Background:

Recompensation is increasingly recognized in liver transplant (LT) candidates due to therapeutic advances.
Identifying predictors of improvement-related waitlist removal is vital for prognostication and resource allocation.

Purpose of the Study:

To examine key predictors of improvement-related waitlist removal using a machine learning approach.
To enhance prognostication and resource allocation for LT candidates.

Main Methods:

Retrospective cohort study of adult LT waitlist candidates (2000-2025) from the United Network for Organ Sharing registry.
Random survival forest model applied to identify predictors of improvement-related waitlist removal, accounting for competing risks (death, LT).
Variable importance (VIMP) and minimal depth used for variable selection; model performance assessed via concordance index, Brier scores, and time-dependent AUC.

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Main Results:

The cohort comprised 127,978 individuals; 6.6% were delisted due to clinical improvement.
The random survival forest model showed strong performance (concordance index ~0.77-0.78, time-dependent AUC ~0.78-0.80).
Key predictors of recovery included diagnosis, age, and serum albumin.

Conclusions:

Identified variables can inform predictive models for individualized LT decision-making.
Validated predictive models could improve prognostication of patient trajectories on the LT waitlist.
Enhanced prognostication and resource allocation can be facilitated by integrating these models into clinical workflows.