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Differences between kidney retransplant recipients as identified by machine learning consensus clustering.
Thongprayoon, Charat; Vaitla, Pradeep; Jadlowiec, Caroline C; Mao, Shennen A; Mao, Michael A; Acharya, Prakrati C; Leeaphorn, Napat; Kaewput, Wisit; Pattharanitima, Pattharawin; Tangpanithandee, Supawit; Krisanapan, Pajaree; Nissaisorakarn, Pitchaphon; Cooper, Matthew; Cheungpasitporn, Wisit.
Afiliação
  • Thongprayoon C; Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Vaitla P; Division of Nephrology, University of Mississippi Medical Center, Jackson, Mississippi, USA.
  • Jadlowiec CC; Division of Transplant Surgery, Mayo Clinic, Phoenix, Arizona, USA.
  • Mao SA; Division of Transplant Surgery, Mayo Clinic, Jacksonville, Florida, USA.
  • Mao MA; Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, Florida, USA.
  • Acharya PC; Division of Nephrology, Texas Tech Health Sciences Center El Paso, El Paso, Texas, USA.
  • Leeaphorn N; Renal Transplant Program, University of Missouri-Kansas City School of Medicine/Saint Luke's Health System, Kansas City, Missouri, USA.
  • Kaewput W; Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok, Thailand.
  • Pattharanitima P; Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani, Thailand.
  • Tangpanithandee S; Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Krisanapan P; Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Nissaisorakarn P; Department of Internal Medicine, Faculty of Medicine, Thammasat University, Pathum Thani, Thailand.
  • Cooper M; Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Cheungpasitporn W; Department of Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Clin Transplant ; 37(5): e14943, 2023 05.
Article em En | MEDLINE | ID: mdl-36799718
ABSTRACT

BACKGROUND:

Our study aimed to characterize kidney retransplant recipients using an unsupervised machine-learning approach.

METHODS:

We performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 17 443 kidney retransplant recipients in the OPTN/UNOS database from 2010 to 2019. We identified each cluster's key characteristics using the standardized mean difference of >.3. We compared the posttransplant outcomes, including death-censored graft failure and patient death among the assigned clusters

RESULTS:

Consensus cluster analysis identified three distinct clusters of kidney retransplant recipients. Cluster 1 recipients were predominantly white and were less sensitized. They were most likely to receive a living donor kidney transplant and more likely to be preemptive (30%) or need ≤1 year of dialysis (32%). In contrast, cluster 2 recipients were the most sensitized (median PRA 95%). They were more likely to have been on dialysis >1 year, and receive a nationally allocated, low HLA mismatch, standard KDPI deceased donor kidney. Recipients in cluster 3 were more likely to be minorities (37% Black; 15% Hispanic). They were moderately sensitized with a median PRA of 87% and were also most likely to have been on dialysis >1 year. They received locally allocated high HLA mismatch kidneys from standard KDPI deceased donors. Thymoglobulin was the most commonly used induction agent for all three clusters. Cluster 1 had the most favorable patient and graft survival, while cluster 3 had the worst patient and graft survival.

CONCLUSION:

The use of an unsupervised machine learning approach characterized kidney retransplant recipients into three clinically distinct clusters with differing posttransplant outcomes. Recipients with moderate allosensitization, such as those represented in cluster 3, are perhaps more disadvantaged in the kidney retransplantation process. Potential opportunities for improvement specific to these re-transplant recipients include working to improve opportunities to improve access to living donor kidney transplantation, living donor paired exchange and identifying strategies for better HLA matching.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Obtenção de Tecidos e Órgãos Tipo de estudo: Guideline / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Obtenção de Tecidos e Órgãos Tipo de estudo: Guideline / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article