Cited by 141Organ Transplantation Techniques and OutcomesLiver Disease and TransplantationArtificial Intelligence in Healthcare

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Development and validation of an optimized prediction of mortality for candidates awaiting liver transplantation

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Bertsimas, Dimitris, Kung, Jerry, Trichakis, Nikolaos, Wang, Yuchen, Hirose, Ryutaro, Vagefi, Parsia A.·2019·American Journal of Transplantation

DOI 10.1111/ajt.15172

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Abstract

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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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
ORCID: Bertsimas, Dimitris
Trichakis, Nikolaos
Wang, Yuchen
Hirose, Ryutaro
Vagefi, Parsia A.
Venue: American Journal of Transplantation
Publisher: Elsevier BV
Published: 2019-04
Volume: 19
Issue: 4
Pages: 1109-1118
ISSN: 1600-6135 (ISSN-L)
DOI: 10.1111/ajt.15172
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: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor, 1327 d)
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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Development and validation of an optimized prediction of mortality for candidates awaiting liver transplantation

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