Development and Validation of an Optimized Prediction of Mortality for Candidates Awaiting Liver Transplantation
Open accessAbstract
Since 2002, the Model for End-Stage Liver Disease (MELD) has been used to rank liver transplant candidates. However, despite numerous revisions, MELD allocation still does not allow for equitable access to all waitlisted candidates. An optimized prediction of mortality (OPOM) was developed (http://www.opom.online) utilizing machine-learning optimal classification tree models trained to predict a candidate's 3-month waitlist mortality or removal utilizing the Standard Transplant Analysis and Research (STAR) dataset. The Liver Simulated Allocation Model (LSAM) was then used to compare OPOM to MELD-based allocation. Out-of-sample area under the curve (AUC) was also calculated for candidate groups of increasing disease severity. OPOM allocation, when compared to MELD, reduced mortality on average by 417.96 (406.8-428.4) deaths every year in LSAM analysis. Improved survival was noted across all candidate demographics, diagnoses, and geographic regions. OPOM delivered a substantially higher AUC across all disease severity groups. OPOM more accurately and objectively prioritizes candidates for liver transplantation based on disease severity, allowing for more equitable allocation of livers with a resultant significant number of additional lives saved every year. These data demonstrate the potential of machine learning technology to help guide clinical practice, and potentially guide national policy.
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OPOM predicts the probability of a patient's 3-month mortality or waitlist removal given their characteristics.
“This model, named Optimal Prediction of Mortality (OPOM), predicts the probability of a patient’s 3-month mortality or waitlist removal given their characteristics.”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [8]
Bertsimas and co-workers proposed a machine learning-based model for alternative liver allocation, named OPOM.
“In 2019, Bertsimas and co-workers proposed a machine learning-based model for alternative liver allocation (8).”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [8]
Using the Standard Transplant Analysis and Research dataset, OPOM provided more accurate and objective predictions than MELD.
“Using the Standard Transplant Analysis and Research dataset (1618966 observations), OPOM provided more accurate and objective predictions than MELD.”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [8]
The OPOM simulation reduced mortality on average by 417.96 deaths for 6139 liver transplantations by assigning different priority to liver transplant candidates.
“Additionally, the OPOM simulation reduced mortality on average by 417.96 deaths for 6139 liver transplantations by assigning different priority to liver transplant candidates.”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [8]
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- Authors
- Bertsimas, D., Kung, J., Trichakis, N., Wang, Y., Hirose, R., & Vagefi, P. A.
- Affiliations
- Massachusetts Institute of Technology (US)
- University of California, San Francisco (US)
- The University of Texas Southwestern Medical Center (US)
- Corresponding author
- Bertsimas, Dimitris
- Venue
- American Journal of Transplantation
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 19
- Issue
- 4
- Pages
- 1109-1118
- ISSN
- 1600-6135 (ISSN-L)
- OpenAlex
- W2899525417
- Cited by
- 141
- References
- 18
- Citations by year
- 2026: 18 · 2025: 20 · 2024: 33 · 2023: 17 · 2022: 15 · 2021: 15 · 2020: 15 · 2019: 7
- FWCI
- 9.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- American Journal of Transplantation (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Topics
- Health Sciences › Medicine › Surgery › Organ Transplantation Techniques and Outcomes
- Health Sciences › Medicine › Hepatology › Liver Disease and Transplantation
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- liver transplantation, transplant candidate prioritization, mortality prediction, Model for End-Stage Liver Disease, classification trees, waitlist mortality, disease severity, machine learning
- MeSH terms
- Machine Learning, Female, Humans, Liver Diseases, Male, Waiting Lists, Models, Statistical, Liver Transplantation
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