Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool
Introduction: Predicting the outcome of a kidney transplant involving a living donor advances donor decision-making donors for clinicians and patients. However, the discriminative or calibration capacity of the currently employed models are limited. We set out to apply artificial intelligence (AI) a...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2431147 |
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| author | Hatem Ali Arun Shroff Tibor Fülöp Miklos Z. Molnar Adnan Sharif Bernard Burke Sunil Shroff David Briggs Nithya Krishnan |
| author_facet | Hatem Ali Arun Shroff Tibor Fülöp Miklos Z. Molnar Adnan Sharif Bernard Burke Sunil Shroff David Briggs Nithya Krishnan |
| author_sort | Hatem Ali |
| collection | DOAJ |
| description | Introduction: Predicting the outcome of a kidney transplant involving a living donor advances donor decision-making donors for clinicians and patients. However, the discriminative or calibration capacity of the currently employed models are limited. We set out to apply artificial intelligence (AI) algorithms to create a highly predictive risk stratification indicator, applicable to the UK’s transplant selection process.Methodology: Pre-transplant characteristics from 12,661 live-donor kidney transplants (performed between 2007 and 2022) from the United Kingdom Transplant Registry database were analyzed. The transplants were randomly divided into training (70%) and validation (30%) sets. Death-censored graft survival was the primary performance indicator. We experimented with four machine learning (ML) models assessed for calibration and discrimination [integrated Brier score (IBS) and Harrell’s concordance index]. We assessed the potential clinical utility using decision curve analysis.Results: XGBoost demonstrated the best discriminative performance for survival (area under the curve = 0.73, 0.74, and 0.75 at 3, 7, and 10 years post-transplant, respectively). The concordance index was 0.72. The calibration process was adequate, as evidenced by the IBS score of 0.09.Conclusion: By evaluating possible donor–recipient pairs based on graft survival, the AI-based UK Live-Donor Kidney Transplant Outcome Prediction has the potential to enhance choices for the best live-donor selection. This methodology may improve the outcomes of kidney paired exchange schemes. In general terms we show how the new AI and ML tools can have a role in developing effective and equitable healthcare. |
| format | Article |
| id | doaj-art-bb95dd0f82914b63b2fcc3cf0907a8f2 |
| institution | Kabale University |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-bb95dd0f82914b63b2fcc3cf0907a8f22025-01-22T05:01:03ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492025-12-0147110.1080/0886022X.2024.2431147Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction toolHatem Ali0Arun Shroff1Tibor Fülöp2Miklos Z. Molnar3Adnan Sharif4Bernard Burke5Sunil Shroff6David Briggs7Nithya Krishnan8University Hospitals of Coventry and Warwickshire, Coventry, UKITU/WHO Focus Group on AI for Health, Geneva, SwitzerlandDivision of Nephrology, Department of Medicine, Medical University Hospitals of South Carolina, Charleston, SC, USADivision of Nephrology & Hypertension, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USAUniversity Hospitals of Birmingham, Birmingham, UKResearch Centre for Health and Life Sciences, Coventry University, Coventry, UKITU/WHO Focus Group on AI for Health, Geneva, SwitzerlandHistocompatibility and Immunogenetics Laboratory, Birmingham Centre, NHS Blood and Transplant, Bristol, UKUniversity Hospitals of Coventry and Warwickshire, Coventry, UKIntroduction: Predicting the outcome of a kidney transplant involving a living donor advances donor decision-making donors for clinicians and patients. However, the discriminative or calibration capacity of the currently employed models are limited. We set out to apply artificial intelligence (AI) algorithms to create a highly predictive risk stratification indicator, applicable to the UK’s transplant selection process.Methodology: Pre-transplant characteristics from 12,661 live-donor kidney transplants (performed between 2007 and 2022) from the United Kingdom Transplant Registry database were analyzed. The transplants were randomly divided into training (70%) and validation (30%) sets. Death-censored graft survival was the primary performance indicator. We experimented with four machine learning (ML) models assessed for calibration and discrimination [integrated Brier score (IBS) and Harrell’s concordance index]. We assessed the potential clinical utility using decision curve analysis.Results: XGBoost demonstrated the best discriminative performance for survival (area under the curve = 0.73, 0.74, and 0.75 at 3, 7, and 10 years post-transplant, respectively). The concordance index was 0.72. The calibration process was adequate, as evidenced by the IBS score of 0.09.Conclusion: By evaluating possible donor–recipient pairs based on graft survival, the AI-based UK Live-Donor Kidney Transplant Outcome Prediction has the potential to enhance choices for the best live-donor selection. This methodology may improve the outcomes of kidney paired exchange schemes. In general terms we show how the new AI and ML tools can have a role in developing effective and equitable healthcare.https://www.tandfonline.com/doi/10.1080/0886022X.2024.2431147Transplant outcomespersonalized medicinedeceased kidney donormachine learning |
| spellingShingle | Hatem Ali Arun Shroff Tibor Fülöp Miklos Z. Molnar Adnan Sharif Bernard Burke Sunil Shroff David Briggs Nithya Krishnan Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool Renal Failure Transplant outcomes personalized medicine deceased kidney donor machine learning |
| title | Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool |
| title_full | Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool |
| title_fullStr | Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool |
| title_full_unstemmed | Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool |
| title_short | Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool |
| title_sort | artificial intelligence assisted risk prediction in organ transplantation a uk live donor kidney transplant outcome prediction tool |
| topic | Transplant outcomes personalized medicine deceased kidney donor machine learning |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2024.2431147 |
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