Abstract
This paper provides a methodology for predicting post-transplant kidney function, that is, the 1-year post-transplant estimated Glomerular Filtration Rate (eGFR-1) for each donor-candidate pair. We apply customized machine-learning algorithms to pre-transplant donor and recipient data to determine the probability of achieving an eGFR-1 of at least 30 ml/min. This threshold was chosen because there is insufficient survival benefit if the kidney fails to generate an eGFR-1 ≥ 30 ml/min. For some donor-candidate pairs, the developed algorithm provides highly accurate predictions. For others, limitations of previous transplants' data results in noisier predictions. However, because the same kidney is offered to many candidates, we identify those pairs for whom the predictions are highly accurate. Out of 6977 discarded older-donor kidneys that were a match with at least one transplanted kidney, 5282 had one or more identified candidate, who were offered that kidney, did not accept any other offer, and would have had ≥80% chance of achieving eGFR-1 ≥ 30 ml/min, had the kidney been transplanted. We also show that transplants with ≥80% chance of achieving eGFR-1 ≥ 30 ml/min and that survive 1 year have higher 10-year death-censored graft survival probabilities than all older-donor transplants that survive 1 year (73.61% vs. 70.48%, respectively).
Original language | English (US) |
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Pages (from-to) | 21-33 |
Number of pages | 13 |
Journal | Naval Research Logistics |
Volume | 70 |
Issue number | 1 |
DOIs | |
State | Published - Feb 2023 |
Bibliographical note
Funding Information:information Agency for Healthcare Research and Quality, Grant/Award Number: R03-HS27671-01; U.S. Department of Health and Human Services, University of Texas at AustinThis project was funded, in part, under grant number R03 HS27671-01 from the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services (HHS). This work was supported in part by Health Resources and Services Administration contract HHSH250-2019-00001C. The content is the responsibility of the authors alone and does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. Additional funding was provided by the McCombs School of Business, University of Texas at Austin via a Research Excellence Grant to Diwakar Gupta. The authors are solely responsible for this document's contents, findings, and conclusions, which do not necessarily represent the views of AHRQ. Readers should not interpret any statement in this report as an official position of AHRQ or of HHS.
Funding Information:
This project was funded, in part, under grant number R03 HS27671‐01 from the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services (HHS). This work was supported in part by Health Resources and Services Administration contract HHSH250‐2019‐00001C. The content is the responsibility of the authors alone and does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. Additional funding was provided by the McCombs School of Business, University of Texas at Austin via a Research Excellence Grant to Diwakar Gupta. The authors are solely responsible for this document's contents, findings, and conclusions, which do not necessarily represent the views of AHRQ. Readers should not interpret any statement in this report as an official position of AHRQ or of HHS.
Publisher Copyright:
© 2022 The Authors. Naval Research Logistics published by Wiley Periodicals LLC.
Keywords
- machine-learning
- older-donor kidneys
- post-transplant renal function
PubMed: MeSH publication types
- Journal Article