Comparative analysis of outcomes in high KDPI spectrum kidney transplants using unsupervised machine learning algorithm
The Kidney Donor Profile Index (KDPI) is a continuous metric used to estimate the risk of allograft failure for kidneys from deceased donors. Lower KDPI scores are associated with longer post-transplant kidney function. This study aims to evaluate the outcomes of kidney transplantation using high-KD...
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| Published in: | PloS one Vol. 20; no. 8; p. e0324265 |
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| Format: | Journal Article |
| Language: | English |
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26.08.2025
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | The Kidney Donor Profile Index (KDPI) is a continuous metric used to estimate the risk of allograft failure for kidneys from deceased donors. Lower KDPI scores are associated with longer post-transplant kidney function. This study aims to evaluate the outcomes of kidney transplantation using high-KDPI kidneys (98-100%) compared to those with moderately high KDPI scores (85-97%), employing a novel case-matching approach using machine learning.
We conducted a retrospective analysis of the United Network for Organ Sharing (UNOS) database, examining kidney transplants performed in the United States between January 2000 and May 2020. An unsupervised machine learning algorithm was used to match recipients of KDPI 98-100% kidneys with recipients of KDPI 85-97% kidneys based on key baseline characteristics, including recipient age, body mass index (BMI), cold ischemia time, HLA mismatch, ethnicity, and gender.
A total of 6,624 matched cases were selected for analysis. The mean follow-up duration was 4.5 years for the KDPI 98-100% cohort and 4.6 years for the KDPI 85-97% cohort. The five-year allograft survival was 51.7% for the KDPI 98-100% group versus 58% for the KDPI 85-97% group (P < 0.001). Asian recipients showed the highest survival in both cohorts (68% vs. 69%). Donation after circulatory death (DCD) status did not significantly impact outcomes. Across the full cohort, 1,819 cases of allograft failure were recorded, with chronic rejection being the leading cause (28.4% vs. 30%, P = 0.56).
Transplantation with high-KDPI kidneys, though associated with lower survival rates, remains a viable option for expanding the donor pool. With appropriate recipient selection, high-KDPI kidneys can improve patient quality of life, reduce wait times, and lower healthcare costs. Our findings support a more nuanced approach to organ allocation using advanced matching strategies. |
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| AbstractList | BackgroundThe Kidney Donor Profile Index (KDPI) is a continuous metric used to estimate the risk of allograft failure for kidneys from deceased donors. Lower KDPI scores are associated with longer post-transplant kidney function. This study aims to evaluate the outcomes of kidney transplantation using high-KDPI kidneys (98–100%) compared to those with moderately high KDPI scores (85–97%), employing a novel case-matching approach using machine learning.MethodsWe conducted a retrospective analysis of the United Network for Organ Sharing (UNOS) database, examining kidney transplants performed in the United States between January 2000 and May 2020. An unsupervised machine learning algorithm was used to match recipients of KDPI 98–100% kidneys with recipients of KDPI 85–97% kidneys based on key baseline characteristics, including recipient age, body mass index (BMI), cold ischemia time, HLA mismatch, ethnicity, and gender.ResultsA total of 6,624 matched cases were selected for analysis. The mean follow-up duration was 4.5 years for the KDPI 98–100% cohort and 4.6 years for the KDPI 85–97% cohort. The five-year allograft survival was 51.7% for the KDPI 98–100% group versus 58% for the KDPI 85–97% group (P < 0.001). Asian recipients showed the highest survival in both cohorts (68% vs. 69%). Donation after circulatory death (DCD) status did not significantly impact outcomes. Across the full cohort, 1,819 cases of allograft failure were recorded, with chronic rejection being the leading cause (28.4% vs. 30%, P = 0.56).ConclusionTransplantation with high-KDPI kidneys, though associated with lower survival rates, remains a viable option for expanding the donor pool. With appropriate recipient selection, high-KDPI kidneys can improve patient quality of life, reduce wait times, and lower healthcare costs. Our findings support a more nuanced approach to organ allocation using advanced matching strategies. Background The Kidney Donor Profile Index (KDPI) is a continuous metric used to estimate the risk of allograft failure for kidneys from deceased donors. Lower KDPI scores are associated with longer post-transplant kidney function. This study aims to evaluate the outcomes of kidney transplantation using high-KDPI kidneys (98-100%) compared to those with moderately high KDPI scores (85-97%), employing a novel case-matching approach using machine learning. Methods We conducted a retrospective analysis of the United Network for Organ Sharing (UNOS) database, examining kidney transplants performed in the United States between January 2000 and May 2020. An unsupervised machine learning algorithm was used to match recipients of KDPI 98-100% kidneys with recipients of KDPI 85-97% kidneys based on key baseline characteristics, including recipient age, body mass index (BMI), cold ischemia time, HLA mismatch, ethnicity, and gender. Results A total of 6,624 matched cases were selected for analysis. The mean follow-up duration was 4.5 years for the KDPI 98-100% cohort and 4.6 years for the KDPI 85-97% cohort. The five-year allograft survival was 51.7% for the KDPI 98-100% group versus 58% for the KDPI 85-97% group (P < 0.001). Asian recipients showed the highest survival in both cohorts (68% vs. 69%). Donation after circulatory death (DCD) status did not significantly impact outcomes. Across the full cohort, 1,819 cases of allograft failure were recorded, with chronic rejection being the leading cause (28.4% vs. 30%, P = 0.56). Conclusion Transplantation with high-KDPI kidneys, though associated with lower survival rates, remains a viable option for expanding the donor pool. With appropriate recipient selection, high-KDPI kidneys can improve patient quality of life, reduce wait times, and lower healthcare costs. Our findings support a more nuanced approach to organ allocation using advanced matching strategies. The Kidney Donor Profile Index (KDPI) is a continuous metric used to estimate the risk of allograft failure for kidneys from deceased donors. Lower KDPI scores are associated with longer post-transplant kidney function. This study aims to evaluate the outcomes of kidney transplantation using high-KDPI kidneys (98-100%) compared to those with moderately high KDPI scores (85-97%), employing a novel case-matching approach using machine learning.BACKGROUNDThe Kidney Donor Profile Index (KDPI) is a continuous metric used to estimate the risk of allograft failure for kidneys from deceased donors. Lower KDPI scores are associated with longer post-transplant kidney function. This study aims to evaluate the outcomes of kidney transplantation using high-KDPI kidneys (98-100%) compared to those with moderately high KDPI scores (85-97%), employing a novel case-matching approach using machine learning.We conducted a retrospective analysis of the United Network for Organ Sharing (UNOS) database, examining kidney transplants performed in the United States between January 2000 and May 2020. An unsupervised machine learning algorithm was used to match recipients of KDPI 98-100% kidneys with recipients of KDPI 85-97% kidneys based on key baseline characteristics, including recipient age, body mass index (BMI), cold ischemia time, HLA mismatch, ethnicity, and gender.METHODSWe conducted a retrospective analysis of the United Network for Organ Sharing (UNOS) database, examining kidney transplants performed in the United States between January 2000 and May 2020. An unsupervised machine learning algorithm was used to match recipients of KDPI 98-100% kidneys with recipients of KDPI 85-97% kidneys based on key baseline characteristics, including recipient age, body mass index (BMI), cold ischemia time, HLA mismatch, ethnicity, and gender.A total of 6,624 matched cases were selected for analysis. The mean follow-up duration was 4.5 years for the KDPI 98-100% cohort and 4.6 years for the KDPI 85-97% cohort. The five-year allograft survival was 51.7% for the KDPI 98-100% group versus 58% for the KDPI 85-97% group (P < 0.001). Asian recipients showed the highest survival in both cohorts (68% vs. 69%). Donation after circulatory death (DCD) status did not significantly impact outcomes. Across the full cohort, 1,819 cases of allograft failure were recorded, with chronic rejection being the leading cause (28.4% vs. 30%, P = 0.56).RESULTSA total of 6,624 matched cases were selected for analysis. The mean follow-up duration was 4.5 years for the KDPI 98-100% cohort and 4.6 years for the KDPI 85-97% cohort. The five-year allograft survival was 51.7% for the KDPI 98-100% group versus 58% for the KDPI 85-97% group (P < 0.001). Asian recipients showed the highest survival in both cohorts (68% vs. 69%). Donation after circulatory death (DCD) status did not significantly impact outcomes. Across the full cohort, 1,819 cases of allograft failure were recorded, with chronic rejection being the leading cause (28.4% vs. 30%, P = 0.56).Transplantation with high-KDPI kidneys, though associated with lower survival rates, remains a viable option for expanding the donor pool. With appropriate recipient selection, high-KDPI kidneys can improve patient quality of life, reduce wait times, and lower healthcare costs. Our findings support a more nuanced approach to organ allocation using advanced matching strategies.CONCLUSIONTransplantation with high-KDPI kidneys, though associated with lower survival rates, remains a viable option for expanding the donor pool. With appropriate recipient selection, high-KDPI kidneys can improve patient quality of life, reduce wait times, and lower healthcare costs. Our findings support a more nuanced approach to organ allocation using advanced matching strategies. The Kidney Donor Profile Index (KDPI) is a continuous metric used to estimate the risk of allograft failure for kidneys from deceased donors. Lower KDPI scores are associated with longer post-transplant kidney function. This study aims to evaluate the outcomes of kidney transplantation using high-KDPI kidneys (98-100%) compared to those with moderately high KDPI scores (85-97%), employing a novel case-matching approach using machine learning. We conducted a retrospective analysis of the United Network for Organ Sharing (UNOS) database, examining kidney transplants performed in the United States between January 2000 and May 2020. An unsupervised machine learning algorithm was used to match recipients of KDPI 98-100% kidneys with recipients of KDPI 85-97% kidneys based on key baseline characteristics, including recipient age, body mass index (BMI), cold ischemia time, HLA mismatch, ethnicity, and gender. A total of 6,624 matched cases were selected for analysis. The mean follow-up duration was 4.5 years for the KDPI 98-100% cohort and 4.6 years for the KDPI 85-97% cohort. The five-year allograft survival was 51.7% for the KDPI 98-100% group versus 58% for the KDPI 85-97% group (P < 0.001). Asian recipients showed the highest survival in both cohorts (68% vs. 69%). Donation after circulatory death (DCD) status did not significantly impact outcomes. Across the full cohort, 1,819 cases of allograft failure were recorded, with chronic rejection being the leading cause (28.4% vs. 30%, P = 0.56). Transplantation with high-KDPI kidneys, though associated with lower survival rates, remains a viable option for expanding the donor pool. With appropriate recipient selection, high-KDPI kidneys can improve patient quality of life, reduce wait times, and lower healthcare costs. Our findings support a more nuanced approach to organ allocation using advanced matching strategies. Background The Kidney Donor Profile Index (KDPI) is a continuous metric used to estimate the risk of allograft failure for kidneys from deceased donors. Lower KDPI scores are associated with longer post-transplant kidney function. This study aims to evaluate the outcomes of kidney transplantation using high-KDPI kidneys (98–100%) compared to those with moderately high KDPI scores (85–97%), employing a novel case-matching approach using machine learning. Methods We conducted a retrospective analysis of the United Network for Organ Sharing (UNOS) database, examining kidney transplants performed in the United States between January 2000 and May 2020. An unsupervised machine learning algorithm was used to match recipients of KDPI 98–100% kidneys with recipients of KDPI 85–97% kidneys based on key baseline characteristics, including recipient age, body mass index (BMI), cold ischemia time, HLA mismatch, ethnicity, and gender. Results A total of 6,624 matched cases were selected for analysis. The mean follow-up duration was 4.5 years for the KDPI 98–100% cohort and 4.6 years for the KDPI 85–97% cohort. The five-year allograft survival was 51.7% for the KDPI 98–100% group versus 58% for the KDPI 85–97% group (P < 0.001). Asian recipients showed the highest survival in both cohorts (68% vs. 69%). Donation after circulatory death (DCD) status did not significantly impact outcomes. Across the full cohort, 1,819 cases of allograft failure were recorded, with chronic rejection being the leading cause (28.4% vs. 30%, P = 0.56). Conclusion Transplantation with high-KDPI kidneys, though associated with lower survival rates, remains a viable option for expanding the donor pool. With appropriate recipient selection, high-KDPI kidneys can improve patient quality of life, reduce wait times, and lower healthcare costs. Our findings support a more nuanced approach to organ allocation using advanced matching strategies. The Kidney Donor Profile Index (KDPI) is a continuous metric used to estimate the risk of allograft failure for kidneys from deceased donors. Lower KDPI scores are associated with longer post-transplant kidney function. This study aims to evaluate the outcomes of kidney transplantation using high-KDPI kidneys (98-100%) compared to those with moderately high KDPI scores (85-97%), employing a novel case-matching approach using machine learning. We conducted a retrospective analysis of the United Network for Organ Sharing (UNOS) database, examining kidney transplants performed in the United States between January 2000 and May 2020. An unsupervised machine learning algorithm was used to match recipients of KDPI 98-100% kidneys with recipients of KDPI 85-97% kidneys based on key baseline characteristics, including recipient age, body mass index (BMI), cold ischemia time, HLA mismatch, ethnicity, and gender. A total of 6,624 matched cases were selected for analysis. The mean follow-up duration was 4.5 years for the KDPI 98-100% cohort and 4.6 years for the KDPI 85-97% cohort. The five-year allograft survival was 51.7% for the KDPI 98-100% group versus 58% for the KDPI 85-97% group (P < 0.001). Asian recipients showed the highest survival in both cohorts (68% vs. 69%). Donation after circulatory death (DCD) status did not significantly impact outcomes. Across the full cohort, 1,819 cases of allograft failure were recorded, with chronic rejection being the leading cause (28.4% vs. 30%, P = 0.56). Transplantation with high-KDPI kidneys, though associated with lower survival rates, remains a viable option for expanding the donor pool. With appropriate recipient selection, high-KDPI kidneys can improve patient quality of life, reduce wait times, and lower healthcare costs. Our findings support a more nuanced approach to organ allocation using advanced matching strategies. |
| Audience | Academic |
| Author | Saidi, Reza Golkarieh, Alireza Lioudis, Michael Moein, Mahmoudreza Vlassis, Isabella |
| AuthorAffiliation | 3 Division of Nephrology, Department of Medicine, SUNY Upstate Medical University, Syracuse, New York, United States of America Keck Hospital of USC, UNITED STATES OF AMERICA 1 Division of Transplant Services, Department of Surgery, SUNY Upstate Medical University, Syracuse, New York, United States of America 2 Department of Computer Science and Engineering, Oakland University, Rochester, Michigan, United States of America |
| AuthorAffiliation_xml | – name: Keck Hospital of USC, UNITED STATES OF AMERICA – name: 2 Department of Computer Science and Engineering, Oakland University, Rochester, Michigan, United States of America – name: 3 Division of Nephrology, Department of Medicine, SUNY Upstate Medical University, Syracuse, New York, United States of America – name: 1 Division of Transplant Services, Department of Surgery, SUNY Upstate Medical University, Syracuse, New York, United States of America |
| Author_xml | – sequence: 1 givenname: Mahmoudreza surname: Moein fullname: Moein, Mahmoudreza – sequence: 2 givenname: Alireza surname: Golkarieh fullname: Golkarieh, Alireza – sequence: 3 givenname: Isabella surname: Vlassis fullname: Vlassis, Isabella – sequence: 4 givenname: Reza surname: Saidi fullname: Saidi, Reza – sequence: 5 givenname: Michael orcidid: 0009-0003-7524-4624 surname: Lioudis fullname: Lioudis, Michael |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40857289$$D View this record in MEDLINE/PubMed |
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| DOI | 10.1371/journal.pone.0324265 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 These authors contributed equally to this work and authors share first authorship on this work. Competing Interests: The authors have declared that no competing interests exist. |
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| Snippet | The Kidney Donor Profile Index (KDPI) is a continuous metric used to estimate the risk of allograft failure for kidneys from deceased donors. Lower KDPI scores... Background The Kidney Donor Profile Index (KDPI) is a continuous metric used to estimate the risk of allograft failure for kidneys from deceased donors. Lower... BackgroundThe Kidney Donor Profile Index (KDPI) is a continuous metric used to estimate the risk of allograft failure for kidneys from deceased donors. Lower... Background The Kidney Donor Profile Index (KDPI) is a continuous metric used to estimate the risk of allograft failure for kidneys from deceased donors. Lower... |
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| SubjectTerms | Adult Algorithms Allografts Biology and Life Sciences Body mass index Body size Clustering Comparative analysis Computer and Information Sciences Confounding (Statistics) Donation of organs, tissues, etc Ethnicity Female Gender Graft Rejection Graft Survival Health aspects Humans Influence Ischemia Kidney transplantation Kidney Transplantation - adverse effects Kidney transplants Kidneys Learning algorithms Machine learning Male Matching Medicine and Health Sciences Middle Aged Organ donors People and Places Physical Sciences Quality of life Renal failure Research and Analysis Methods Retrospective Studies Survival Tissue Donors Transplantation Transplants Treatment Outcome United States Unsupervised learning Unsupervised Machine Learning |
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| Title | Comparative analysis of outcomes in high KDPI spectrum kidney transplants using unsupervised machine learning algorithm |
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