Differences between Very Highly Sensitized Kidney Transplant Recipients as Identified by Machine Learning Consensus Clustering

被引:2
作者
Thongprayoon, Charat [1 ]
Miao, Jing [1 ]
Jadlowiec, Caroline C. [2 ]
Mao, Shennen A. [3 ]
Mao, Michael A. [4 ]
Vaitla, Pradeep [5 ]
Leeaphorn, Napat [4 ]
Kaewput, Wisit [6 ]
Pattharanitima, Pattharawin [7 ]
Tangpanithandee, Supawit [1 ]
Krisanapan, Pajaree [1 ,7 ]
Nissaisorakarn, Pitchaphon [8 ]
Cooper, Matthew [9 ]
Cheungpasitporn, Wisit [1 ]
机构
[1] Mayo Clin, Dept Med, Div Nephrol & Hypertens, Rochester, MN 55905 USA
[2] Mayo Clin, Div Transplant Surg, Phoenix, AZ 85054 USA
[3] Mayo Clin, Div Transplant Surg, Jacksonville, FL 32224 USA
[4] Mayo Clin, Dept Med, Div Nephrol & Hypertens, Jacksonville, FL 32224 USA
[5] Univ Mississippi, Div Nephrol, Med Ctr, Jackson, MS 39216 USA
[6] Phramongkutklao Coll Med, Dept Mil & Community Med, Bangkok 10400, Thailand
[7] Thammasat Univ, Fac Med, Dept Internal Med, Pathum Thani 12120, Thailand
[8] Harvard Med Sch, Massachusetts Gen Hosp, Dept Med, Div Nephrol, Boston, MA 02114 USA
[9] Med Coll Wisconsin, Milwaukee, WI 53226 USA
来源
MEDICINA-LITHUANIA | 2023年 / 59卷 / 05期
关键词
clustering; highly sensitized kidney transplant recipients; kidney transplant; kidney transplantation; transplantation; CLASS DISCOVERY; HLA ANTIBODIES; DESENSITIZATION; OUTCOMES;
D O I
10.3390/medicina59050977
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background and Objectives: The aim of our study was to categorize very highly sensitized kidney transplant recipients with pre-transplant panel reactive antibody (PRA)= 98% using an unsuper-vised machine learning approach as clinical outcomes for this population are inferior, despite receiving increased allocation priority. Identifying subgroups with higher risks for inferior outcomes is essential to guide individualized management strategies for these vulnerable recipients. Materials and Methods: To achieve this, we analyzed the Organ Procurement and Transplantation Network (OPTN)/United Net-work for Organ Sharing (UNOS) database from 2010 to 2019 and performed consensus cluster analysis based on the recipient-, donor-, and transplant-related characteristics in 7458 kidney transplant patients with pre-transplant PRA = 98%. The key characteristics of each cluster were identified by calculating the standardized mean difference. The post-transplant outcomes were compared between the assigned clusters. Results: We identified two distinct clusters and compared the post-transplant outcomes among the assigned clusters of very highly sensitized kidney transplant patients. Cluster 1 patients were younger (median age 45 years), male predominant, and more likely to have previously undergone a kidney transplant, but had less diabetic kidney disease. Cluster 2 recipients were older (median 54 years), female predominant, and more likely to be undergoing a first-time transplant. While patient survival was comparable between the two clusters, cluster 1 had lower death-censored graft survival and higher acute rejection compared to cluster 2. Conclusions: The unsupervised machine learning approach catego-rized very highly sensitized kidney transplant patients into two clinically distinct clusters with differing post-transplant outcomes. A better understanding of these clinically distinct subgroups may assist the transplant community in developing individualized care strategies and improving the outcomes for very highly sensitized kidney transplant patients.
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页数:14
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