Machine Learning Prediction of Liver Allograft Utilization From Deceased Organ Donors Using the National Donor Management Goals Registry Article Swipe
Andrew Bishara
,
Dmytro Lituiev
,
Dieter Adelmann
,
Rishi Kothari
,
Darren Malinoski
,
Jacob Nudel
,
Mitchell B. Sally
,
Ryutaro Hirose
,
Dexter Hadley
,
Claus U. Niemann
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1097/txd.0000000000001212
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1097/txd.0000000000001212
Background. Early prediction of whether a liver allograft will be utilized for transplantation may allow better resource deployment during donor management and improve organ allocation. The national donor management goals (DMG) registry contains critical care data collected during donor management. We developed a machine learning model to predict transplantation of a liver graft based on data from the DMG registry. Methods. Several machine learning classifiers were trained to predict transplantation of a liver graft. We utilized 127 variables available in the DMG dataset. We included data from potential deceased organ donors between April 2012 and January 2019. The outcome was defined as liver recovery for transplantation in the operating room. The prediction was made based on data available 12–18 h after the time of authorization for transplantation. The data were randomly separated into training (60%), validation (20%), and test sets (20%). We compared the performance of our models to the Liver Discard Risk Index. Results. Of 13 629 donors in the dataset, 9255 (68%) livers were recovered and transplanted, 1519 recovered but used for research or discarded, 2855 were not recovered. The optimized gradient boosting machine classifier achieved an area under the curve of the receiver operator characteristic of 0.84 on the test set, outperforming all other classifiers. Conclusions. This model predicts successful liver recovery for transplantation in the operating room, using data available early during donor management. It performs favorably when compared to existing models. It may provide real-time decision support during organ donor management and transplant logistics.
Related Topics
Concepts
Medicine
Receiver operating characteristic
Transplantation
Liver transplantation
Machine learning
Classifier (UML)
Software deployment
Authorization
Living donor liver transplantation
Artificial intelligence
Training set
Gradient boosting
Surgery
Computer science
Internal medicine
Random forest
Computer security
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1097/txd.0000000000001212
- OA Status
- gold
- Cited By
- 9
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3204890349
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3204890349Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1097/txd.0000000000001212Digital Object Identifier
- Title
-
Machine Learning Prediction of Liver Allograft Utilization From Deceased Organ Donors Using the National Donor Management Goals RegistryWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-09-27Full publication date if available
- Authors
-
Andrew Bishara, Dmytro Lituiev, Dieter Adelmann, Rishi Kothari, Darren Malinoski, Jacob Nudel, Mitchell B. Sally, Ryutaro Hirose, Dexter Hadley, Claus U. NiemannList of authors in order
- Landing page
-
https://doi.org/10.1097/txd.0000000000001212Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1097/txd.0000000000001212Direct OA link when available
- Concepts
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Medicine, Receiver operating characteristic, Transplantation, Liver transplantation, Machine learning, Classifier (UML), Software deployment, Authorization, Living donor liver transplantation, Artificial intelligence, Training set, Gradient boosting, Surgery, Computer science, Internal medicine, Random forest, Computer security, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 3, 2023: 3, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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