Differences between kidney retransplant recipients as identified by machine learning consensus clustering

被引:1
|
作者
Thongprayoon, Charat [1 ]
Vaitla, Pradeep [2 ]
Jadlowiec, Caroline C. [3 ]
Mao, Shennen A. [4 ]
Mao, Michael A. [5 ]
Acharya, Prakrati C. [6 ]
Leeaphorn, Napat [7 ]
Kaewput, Wisit [8 ]
Pattharanitima, Pattharawin [9 ]
Tangpanithandee, Supawit [1 ]
Krisanapan, Pajaree [1 ,9 ]
Nissaisorakarn, Pitchaphon [10 ]
Cooper, Matthew [11 ]
Cheungpasitporn, Wisit [1 ]
机构
[1] Mayo Clin, Dept Med, Div Nephrol & Hypertens, Rochester, MN USA
[2] Univ Mississippi, Med Ctr, Div Nephrol, Jackson, MS USA
[3] Mayo Clin, Div Transplant Surg, Phoenix, AZ USA
[4] Mayo Clin, Div Transplant Surg, Jacksonville, FL USA
[5] Mayo Clin, Dept Med, Div Nephrol & Hypertens, Jacksonville, FL USA
[6] Texas Tech Hlth Sci Ctr El Paso, Div Nephrol, El Paso, TX USA
[7] Univ Missouri, Kansas City Sch Med, Renal Transplant Program, St Lukes Hlth Syst, Kansas City, MO USA
[8] Phramongkutklao Coll Med, Dept Mil & Community Med, Bangkok, Thailand
[9] Thammasat Univ, Fac Med, Dept Internal Med, Pathum Thani, Thailand
[10] Harvard Med Sch, Massachusetts Gen Hosp, Dept Med, Div Nephrol, Boston, MA 55905 USA
[11] Med Coll Wisconsin, Dept Surg, Milwaukee, WI USA
关键词
clustering; kidney transplant; kidney transplantation; retransplant; transplantation; RENAL RETRANSPLANTATION; CLASS DISCOVERY; TRANSPLANTATION; DONATION; BARRIERS; FAILURE;
D O I
10.1111/ctr.14943
中图分类号
R61 [外科手术学];
学科分类号
摘要
BackgroundOur study aimed to characterize kidney retransplant recipients using an unsupervised machine-learning approach. MethodsWe 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 ResultsConsensus 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. ConclusionThe 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.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering
    Thongprayoon, Charat
    Miao, Jing
    Jadlowiec, Caroline C.
    Mao, Shennen A.
    Mao, Michael A.
    Vaitla, Pradeep
    Leeaphorn, Napat
    Kaewput, Wisit
    Pattharanitima, Pattharawin
    Tangpanithandee, Supawit
    Krisanapan, Pajaree
    Nissaisorakarn, Pitchaphon
    Cooper, Matthew
    Cheungpasitporn, Wisit
    [J]. MEDICINA-LITHUANIA, 2023, 59 (05):
  • [2] Differences between Kidney Transplant Recipients from Deceased Donors with Diabetes Mellitus as Identified by Machine Learning Consensus Clustering
    Thongprayoon, Charat
    Miao, Jing
    Jadlowiec, Caroline C.
    Mao, Shennen A.
    Mao, Michael A.
    Leeaphorn, Napat
    Kaewput, Wisit
    Pattharanitima, Pattharawin
    Tangpanithandee, Supawit
    Krisanapan, Pajaree
    Nissaisorakarn, Pitchaphon
    Cooper, Matthew
    Cheungpasitporn, Wisit
    [J]. JOURNAL OF PERSONALIZED MEDICINE, 2023, 13 (07):
  • [3] CHARACTERISTICS OF KIDNEY RECIPIENTS OF HIGH KIDNEY DONOR PROFILE INDEX KIDNEYS AS IDENTIFIED BY MACHINE LEARNING CONSENSUS CLUSTERING
    Radhakrishnan, Yeshwanter
    Vaitla, Charat
    Jadlowiec, Caroline C.
    Mao, Shennen A.
    Thongprayoon, Pradeep
    Jadlowiec, Caroline C.
    Mao, Shennen A.
    Vaitla, Pradeep
    Tangpanithandee, Supawit
    Krisanapan, Pajaree
    Cheungpasitporn, Wisit
    [J]. AMERICAN JOURNAL OF KIDNEY DISEASES, 2023, 81 (04) : S120 - S120
  • [4] Characteristics of Kidney Recipients of High Kidney Donor Profile Index Kidneys as Identified by Machine Learning Consensus Clustering
    Thongprayoon, Charat
    Radhakrishnan, Yeshwanter
    Jadlowiec, Caroline C.
    Mao, Shennen A. A.
    Mao, Michael A. A.
    Vaitla, Pradeep
    Acharya, Prakrati C.
    Leeaphorn, Napat
    Kaewput, Wisit
    Pattharanitima, Pattharawin
    Tangpanithandee, Supawit
    Krisanapan, Pajaree
    Nissaisorakarn, Pitchaphon
    Cooper, Matthew
    Cheungpasitporn, Wisit
    [J]. JOURNAL OF PERSONALIZED MEDICINE, 2022, 12 (12):
  • [5] Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering
    Tangpanithandee, Supawit
    Thongprayoon, Charat
    Jadlowiec, Caroline C. C.
    Mao, Shennen A. A.
    Mao, Michael A. A.
    Vaitla, Pradeep
    Leeaphorn, Napat
    Kaewput, Wisit
    Pattharanitima, Pattharawin
    Krisanapan, Pajaree
    Nissaisorakarn, Pitchaphon
    Cooper, Matthew
    Cheungpasitporn, Wisit
    [J]. MEDICINA-LITHUANIA, 2022, 58 (12):
  • [6] DISTINCT PHENOTYPES OF DUAL KIDNEY TRANSPLANT RECIPIENTS IN THE UNITED STATES AS IDENTIFIED THROUGH MACHINE LEARNING CONSENSUS CLUSTERING
    Tangpanithandee, Supawit
    Thongprayoon, Charat
    Krisanapan, Pajaree
    Jadlowiec, Caroline C.
    Mao, Shennen A.
    Mao, Michael A.
    Vaitla, Pradeep
    Cheungpasitporn, Wisit
    [J]. AMERICAN JOURNAL OF KIDNEY DISEASES, 2023, 81 (04) : S122 - S122
  • [7] Characteristics of Kidney Transplant Recipients with Prolonged Pre-Transplant Dialysis Duration as Identified by Machine Learning Consensus Clustering
    Tangpanithandee, S.
    Thongprayoon, C.
    Jadlowiec, C.
    Mao, S. A.
    Mao, M. A.
    Leeaphorn, N.
    Vaitla, P.
    Krisanapan, P.
    Nissaisorakarn, P.
    Cooper, M.
    Cheungpasitporn, W.
    [J]. AMERICAN JOURNAL OF TRANSPLANTATION, 2023, 23 (06) : S737 - S738
  • [8] Distinct Phenotypes of Kidney Transplant Recipients with Delayed Graft Function Identified Through Machine Learning Consensus Clustering.
    Jadlowiec, C.
    Thongprayoon, C.
    Leeaphorn, N.
    Kaewput, W.
    Pattharanitima, P.
    Cooper, M.
    Cheungpasitporn, W.
    [J]. AMERICAN JOURNAL OF TRANSPLANTATION, 2022, 22 : 1134 - 1134
  • [9] Distinct Phenotypes of Kidney Transplant Recipients in the United States with Limited Functional Status as Identified through Machine Learning Consensus Clustering
    Thongprayoon, Charat
    Jadlowiec, Caroline C.
    Kaewput, Wisit
    Vaitla, Pradeep
    Mao, Shennen A.
    Mao, Michael A.
    Leeaphorn, Napat
    Qureshi, Fawad
    Pattharanitima, Pattharawin
    Qureshi, Fahad
    Acharya, Prakrati C.
    Nissaisorakarn, Pitchaphon
    Cooper, Matthew
    Cheungpasitporn, Wisit
    [J]. JOURNAL OF PERSONALIZED MEDICINE, 2022, 12 (06):
  • [10] Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States
    Thongprayoon, Charat
    Mao, Shennen A.
    Jadlowiec, Caroline C.
    Mao, Michael A.
    Leeaphorn, Napat
    Kaewput, Wisit
    Vaitla, Pradeep
    Pattharanitima, Pattharawin
    Tangpanithandee, Supawit
    Krisanapan, Pajaree
    Qureshi, Fawad
    Nissaisorakarn, Pitchaphon
    Cooper, Matthew
    Cheungpasitporn, Wisit
    [J]. JOURNAL OF CLINICAL MEDICINE, 2022, 11 (12)