ArticleTransplantation2017
Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation.
Article in Transplantation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 4 of them syntheses that pooled it.
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Who cites it
81 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Investigating the role of artificial intelligence in predicting risk factors after liver transplantation: A systematic review.Canadian liver journal · 2026Pooled it
- Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Machine learning and artificial intelligence in cardiac transplantation: A systematic review.Artificial organs · 2022Pooled it
- Machine learning to guide clinical decision-making in abdominal surgery-a systematic literature review.Langenbeck's archives of surgery · 2022Pooled it
- Leveraging machine learning to predict de novo skin malignancy following lung transplantation.Lung cancer management · 2026Article
- XGBoost Outperforms Traditional Transplant Regulatory Models Based on Regression.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2026Article
- Intuitive Risk Equation for Post-Transplant Bloodstream Infection Prediction: A Symbolic Regression Approach.Biomedicines · 2026Article
- From prediction to practice: artificial intelligence as an enabling layer in liver transplant care - reflections on ILTS 2025.Frontiers in transplantation · 2026Article
- Transforming organ donation and transplantation: challenges, innovations, and future directions.Frontiers in transplantation · 2026Review
- Machine Learning for Predicting 12-Month Survival after Liver Transplantation: Temporal Validation, Calibration, and Decision-Analytic Evaluation.Computational and structural biotechnology journal · 2026Article
- Emerging role of artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2025Review
- A prediction model for genetic cholestatic disease in infancy using the machine learning approach.Journal of pediatric gastroenterology and nutrition · 2025Article
- Predicting Surgical Site Infection after Lumbar Laminectomy and Discectomy: A Cutting-edge Algorithmic Approach by Incorporating Ensembled Stacking into the Current State-of-the-art for Automated Machine Learning.Neurosurgical review · 2025Article
- An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.Journal of translational medicine · 2025Review
- Sarcopenia and frailty: An in-depth analysis of the pathophysiology and effect on liver transplant candidates.World journal of hepatology · 2025Review
- Article
- Ethics and Algorithms to Navigate AI's Emerging Role in Organ Transplantation.Journal of clinical medicine · 2025Review
- Advancements in the diagnosis of biliopancreatic diseases: A comparative review and study on future insights.World journal of gastrointestinal endoscopy · 2025Review
- Association of Functional, Academic, Motor, and Cognitive Deficits in Graft Failure in Pediatric Liver Transplantation.Clinical transplantation · 2025Article
- Machine learning in solid organ transplantation: Charting the evolving landscape.World journal of transplantation · 2025Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe ability to predict graft failure or primary nonfunction at liver transplant decision time assists utilization of scarce resource of donor livers, while ensuring that patients who are urgently requiring a liver transplant are prioritized. An index that is derived to predict graft failure using donor and recipient factors, based on local data sets, will be more beneficial in the Australian context.
methodsLiver transplant data from the Austin Hospital, Melbourne, Australia, from 2010 to 2013 has been included in the study. The top 15 donor, recipient, and transplant factors influencing the outcome of graft failure within 30 days were selected using a machine learning methodology. An algorithm predicting the outcome of interest was developed using those factors.
resultsDonor Risk Index predicts the outcome with an area under the receiver operating characteristic curve (AUC-ROC) value of 0.680 (95% confidence interval [CI], 0.669-0.690). The combination of the factors used in Donor Risk Index with the model for end-stage liver disease score yields an AUC-ROC of 0.764 (95% CI, 0.756-0.771), whereas survival outcomes after liver transplantation score obtains an AUC-ROC of 0.638 (95% CI, 0.632-0.645). The top 15 donor and recipient characteristics within random forests results in an AUC-ROC of 0.818 (95% CI, 0.812-0.824).
conclusionsUsing donor, transplant, and recipient characteristics known at the decision time of a transplant, high accuracy in matching donors and recipients can be achieved, potentially providing assistance with clinical decision making.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.