Your browser doesn't support javascript.
loading
Distinct Phenotypes of Non-Citizen Kidney Transplant Recipients in the United States by Machine Learning Consensus Clustering.
Thongprayoon, Charat; Vaitla, Pradeep; Jadlowiec, Caroline C; Leeaphorn, Napat; Mao, Shennen A; Mao, Michael A; Qureshi, Fahad; Kaewput, Wisit; Qureshi, Fawad; Tangpanithandee, Supawit; Krisanapan, Pajaree; Pattharanitima, Pattharawin; Acharya, Prakrati C; Nissaisorakarn, Pitchaphon; Cooper, Matthew; Cheungpasitporn, Wisit.
Afiliación
  • Thongprayoon C; Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
  • Vaitla P; Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA.
  • Jadlowiec CC; Division of Transplant Surgery, Mayo Clinic, Phoenix, AZ 85054, USA.
  • Leeaphorn N; Renal Transplant Program, University of Missouri-Kansas City School of Medicine/Saint Luke's Health System, Kansas City, MO 64108, USA.
  • Mao SA; Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA.
  • Mao MA; Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA.
  • Qureshi F; School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA.
  • Kaewput W; Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand.
  • Qureshi F; Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
  • Tangpanithandee S; Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
  • Krisanapan P; Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
  • Pattharanitima P; Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand.
  • Acharya PC; Division of Nephrology, Department of Internal Medicine, Faculty of Medicine Thammasat University, Pathum Thani 12120, Thailand.
  • Nissaisorakarn P; Division of Nephrology, Texas Tech Health Sciences Center El Paso, El Paso, TX 79905, USA.
  • Cooper M; Department of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
  • Cheungpasitporn W; Medstar Georgetown Transplant Institute, Georgetown University School of Medicine, Washington, DC 21042, USA.
Medicines (Basel) ; 10(4)2023 Mar 27.
Article en En | MEDLINE | ID: mdl-37103780
ABSTRACT

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.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 6_ODS3_enfermedades_notrasmisibles Problema de salud: 6_kidney_renal_pelvis_ureter_cancer Tipo de estudio: Prognostic_studies Idioma: En Revista: Medicines (Basel) Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 6_ODS3_enfermedades_notrasmisibles Problema de salud: 6_kidney_renal_pelvis_ureter_cancer Tipo de estudio: Prognostic_studies Idioma: En Revista: Medicines (Basel) Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos
...