Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering Article Swipe
Charat Thongprayoon
,
Pradeep Vaitla
,
Caroline C. Jadlowiec
,
Napat Leeaphorn
,
Shennen A. Mao
,
Michael A. Mao
,
Fahad Qureshi
,
Wisit Kaewput
,
Fawad Qureshi
,
Supawit Tangpanithandee
,
Pajaree Krisanapan
,
Pattharawin Pattharanitima
,
Prakrati Acharya
,
Pitchaphon Nissaisorakarn
,
Matthew Cooper
,
Wisit Cheungpasitporn
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/medicines10040025
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/medicines10040025
Background: Better understanding of the different phenotypes/subgroups of non-U.S. citizen kidney transplant recipients may help the transplant community to identify strategies that improve outcomes among non-U.S. citizen kidney transplant recipients. This study aimed to cluster non-U.S. citizen kidney transplant recipients using an unsupervised machine learning approach; Methods: We conducted a consensus cluster analysis based on recipient-, donor-, and transplant- related characteristics in non-U.S. citizen kidney transplant recipients in the United States from 2010 to 2019 in the OPTN/UNOS database using recipient, donor, and transplant-related characteristics. Each cluster’s key characteristics were identified using the standardized mean difference. Post-transplant outcomes were compared among the clusters; Results: Consensus cluster analysis was performed in 11,300 non-U.S. citizen kidney transplant recipients and identified two distinct clusters best representing clinical characteristics. Cluster 1 patients were notable for young age, preemptive kidney transplant or dialysis duration of less than 1 year, working income, private insurance, non-hypertensive donors, and Hispanic living donors with a low number of HLA mismatch. In contrast, cluster 2 patients were characterized by non-ECD deceased donors with KDPI <85%. Consequently, cluster 1 patients had reduced cold ischemia time, lower proportion of machine-perfused kidneys, and lower incidence of delayed graft function after kidney transplant. Cluster 2 had higher 5-year death-censored graft failure (5.2% vs. 9.8%; p < 0.001), patient death (3.4% vs. 11.4%; p < 0.001), but similar one-year acute rejection (4.7% vs. 4.9%; p = 0.63), compared to cluster 1; Conclusions: Machine learning clustering approach successfully identified two clusters among non-U.S. citizen kidney transplant recipients with distinct phenotypes that were associated with different outcomes, including allograft loss and patient survival. These findings underscore the need for individualized care for non-U.S. citizen kidney transplant recipients.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/medicines10040025
- https://www.mdpi.com/2305-6320/10/4/25/pdf?version=1680001592
- OA Status
- gold
- Cited By
- 1
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4361285544
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4361285544Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/medicines10040025Digital Object Identifier
- Title
-
Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus ClusteringWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-27Full publication date if available
- Authors
-
Charat Thongprayoon, Pradeep Vaitla, Caroline C. Jadlowiec, Napat Leeaphorn, Shennen A. Mao, Michael A. Mao, Fahad Qureshi, Wisit Kaewput, Fawad Qureshi, Supawit Tangpanithandee, Pajaree Krisanapan, Pattharawin Pattharanitima, Prakrati Acharya, Pitchaphon Nissaisorakarn, Matthew Cooper, Wisit CheungpasitpornList of authors in order
- Landing page
-
https://doi.org/10.3390/medicines10040025Publisher landing page
- PDF URL
-
https://www.mdpi.com/2305-6320/10/4/25/pdf?version=1680001592Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2305-6320/10/4/25/pdf?version=1680001592Direct OA link when available
- Concepts
-
Kidney transplant, Medicine, Dialysis, Cluster (spacecraft), Kidney transplantation, Kidney, Incidence (geometry), Internal medicine, Machine perfusion, Renal transplant, Transplantation, Optics, Programming language, Liver transplantation, Physics, Computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
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2023: 1Per-year citation counts (last 5 years)
- References (count)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| corresponding_institution_ids | https://openalex.org/I1330342723 |
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| citation_normalized_percentile.is_in_top_10_percent | False |