The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652
Model for end-stage liver disease (MELD) and allocation of donor livers
Wiesner, R. H., Edwards, E. B., Freeman, R. B., Harper, A., Kim, W. R., Kamath, P. S., Kremers, W. K., Lake, J. R., Howard, T. K., Merion, R. M., et al. (2003) · Gastroenterology · doi:10.1053/gast.2003.50016 · Cited by 2,524
Bertsimas, D., & Dunn, J. (2017) · Machine Learning · doi:10.1007/s10994-017-5633-9 · Cited by 649
Self-citationMachine Learning and the Profession of Medicine
Darcy, A. M., Louie, A. K., & Roberts, L. W. (2016) · JAMA · doi:10.1001/jama.2015.18421 · Cited by 448
Disparities in Liver Transplantation Before and After Introduction of the MELD Score
Moylan, C. A. (2008) · JAMA · doi:10.1001/jama.2008.720 · Cited by 363
From predictive methods to missing data imputation
Bertsimas, D., Pawlowski, C., & Zhuo, Y. D. (2017) · DSpace@MIT (Massachusetts Institute of Technology) · doi:10.5555/3122009.3242053 · Cited by 203
Self-citationMELD Exceptions and Rates of Waiting List Outcomes
Massie, A. B., Caffo, B. S., Gentry, S. E., Hall, E. C., Axelrod, D. A., Lentine, K. L., Schnitzler, M. A., Gheorghian, A., Salvalaggio, P. R. D. O., & Segev, D. L. (2011) · American Journal of Transplantation · doi:10.1111/j.1600-6143.2011.03735.x · Cited by 201
Cholongitas, Ε., Marelli, L., Kerry, A., Goodier, D., Nair, D. R., Thomas, M., Patch, D., & Burroughs, A. K. (2007) · American Journal of Transplantation · doi:10.1111/j.1600-6143.2007.01666.x · Cited by 164
Heimbach, J. K., Hirose, R., Stock, P. G., Schladt, D. P., Xiong, H., Liu, J., Olthoff, K. M., Harper, A., Snyder, J. J., Israni, A. K., et al. (2015) · Hepatology · doi:10.1002/hep.27704 · Cited by 129
Self-citationCurrent Status of Liver Allocation in the United States.
Elwir, S., & Lake, J. R. (2016) · PubMed · Cited by 83
Berry, K., & Ioannou, G. N. (2015) · Gastroenterology · doi:10.1053/j.gastro.2015.05.025 · Cited by 76
Standardizing MELD Exceptions: Current Challenges and Future Directions
Goldberg, D. S., & Olthoff, K. M. (2014) · Current Transplantation Reports · doi:10.1007/s40472-014-0027-4 · Cited by 50
Liver Simulated Allocation Modeling
Goel, A., Kim, W. R., Pyke, J., Schladt, D. P., Kasiske, B. L., Snyder, J. J., Lake, J. R., & Israni, A. K. (2018) · Transplantation · doi:10.1097/tp.0000000000002079 · Cited by 23
Pycobra: A Python Toolbox for Ensemble Learning and VisualisationPreprint
Guedj, B., & Desikan, B. S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.00558 · Cited by 21
Mathematical Models and Behavior: Assessing Delta MELD for Liver Allocation
Freeman, R. B. (2004) · American Journal of Transplantation · doi:10.1111/j.1600-6143.2004.00644.x · Cited by 18
Developing concepts on MELD: delta and cutoffs
D’Amico, G. (2005) · Journal of Hepatology · doi:10.1016/j.jhep.2005.03.009 · Cited by 16
Flagged first, then by citations
Cited by
Sorted by citations
Artificial intelligence, machine learning, and deep learning in liver transplantation
Bhat, M., Rabindranath, M., Chara, B. S., & Simonetto, D. A. (2023) · Journal of Hepatology · doi:10.1016/j.jhep.2023.01.006 · Cited by 235
Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review
Spann, A., Yasodhara, A., Kang, J., Watt, K. D. S., Wang, B., Goldenberg, A., & Bhat, M. (2020) · Hepatology · doi:10.1002/hep.31103 · Cited by 209
Artificial intelligence in gastroenterology: A state-of-the-art review
Kröner, P. T., Engels, M. M. L., Glicksberg, B. S., Johnson, K. W., Mzaik, O., van Hooft, J. E., Wallace, M. B., El‐Serag, H. B., & Krittanawong, C. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i40.6794 · Cited by 175
AI-driven 3D bioprinting for regenerative medicine: From bench to bedside
Zhang, Z., Zhou, X., Fang, Y., Xiong, Z., & Zhang, T. (2024) · Bioactive Materials · doi:10.1016/j.bioactmat.2024.11.021 · Cited by 126
The decreasing predictive power of MELD in an era of changing etiology of liver disease
Godfrey, E. L., Malik, T., Lai, J. C., Mindikoglu, A. L., Galván, N. T. N., Cotton, R. T., O'Mahony, C. A., Goss, J. A., & Rana, A. A. (2019) · American Journal of Transplantation · doi:10.1111/ajt.15559 · Cited by 94
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
