Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering

被引:2
|
作者
Tangpanithandee, Supawit [1 ,2 ]
Thongprayoon, Charat [1 ]
Jadlowiec, Caroline C. C. [3 ]
Mao, Shennen A. A. [4 ]
Mao, Michael A. A. [5 ]
Vaitla, Pradeep [6 ]
Leeaphorn, Napat [5 ]
Kaewput, Wisit [7 ]
Pattharanitima, Pattharawin [8 ]
Krisanapan, Pajaree [1 ,8 ]
Nissaisorakarn, Pitchaphon [9 ]
Cooper, Matthew [10 ]
Cheungpasitporn, Wisit [1 ]
机构
[1] Mayo Clin, Dept Med, Div Nephrol & Hypertens, Rochester, MN 55905 USA
[2] Mahidol Univ, Fac Med Ramathibodi Hosp, Chakri Naruebodindra Med Inst, Samut Prakan 10540, Thailand
[3] Mayo Clin, Div Transplant Surg, Phoenix, AZ 85054 USA
[4] Mayo Clin, Div Transplant Surg, Jacksonville, FL 32224 USA
[5] Mayo Clin, Dept Med, Div Nephrol & Hypertens, Jacksonville, FL 32224 USA
[6] Univ Mississippi Med Ctr, Div Nephrol, Jackson, MS 39216 USA
[7] Phramongkutklao Coll Med, Dept Mil & Community Med, Bangkok 10400, Thailand
[8] Thammasat Univ, Dept Internal Med, Div Nephrol, Bangkok 12120, Thailand
[9] Massachusetts Gen Hosp, Harvard Med Sch, Dept Med, Div Nephrol, Boston, MA 02114 USA
[10] Georgetown Univ, Medstar Georgetown Transplant Inst, Sch Med, Washington, DC 21042 USA
来源
MEDICINA-LITHUANIA | 2022年 / 58卷 / 12期
关键词
dual kidney transplant; dual kidney transplant recipients; transplant; transplantation; kidney transplantation; clustering; machine learning; artificial intelligence; ADULT RENAL-ALLOGRAFTS; TERM-FOLLOW-UP; MARGINAL DONORS; CLASS DISCOVERY; ORGAN DONATION; SINGLE; ALLOCATION; OUTCOMES; EXPERIENCE; IMPUTATION;
D O I
10.3390/medicina58121831
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background and Objectives: Our study aimed to cluster dual kidney transplant recipients using an unsupervised machine learning approach to characterize donors and recipients better and to compare the survival outcomes across these various clusters. Materials and Methods: We performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 2821 dual kidney transplant recipients from 2010 to 2019 in the OPTN/UNOS database. We determined the important characteristics of each assigned cluster and compared the post-transplant outcomes between clusters. Results: Two clinically distinct clusters were identified by consensus cluster analysis. Cluster 1 patients was characterized by younger patients (mean recipient age 49 +/- 13 years) who received dual kidney transplant from pediatric (mean donor age 3 +/- 8 years) non-expanded criteria deceased donor (100% non-ECD). In contrast, Cluster 2 patients were characterized by older patients (mean recipient age 63 +/- 9 years) who received dual kidney transplant from adult (mean donor age 59 +/- 11 years) donor with high kidney donor profile index (KDPI) score (59% had KDPI >= 85). Cluster 1 had higher patient survival (98.0% vs. 94.6% at 1 year, and 92.1% vs. 76.3% at 5 years), and lower acute rejection (4.2% vs. 6.1% within 1 year), when compared to cluster 2. Death-censored graft survival was comparable between two groups (93.5% vs. 94.9% at 1 year, and 89.2% vs. 84.8% at 5 years). Conclusions: In summary, DKT in the United States remains uncommon. Two clusters, based on specific recipient and donor characteristics, were identified through an unsupervised machine learning approach. Despite varying differences in donor and recipient age between the two clusters, death-censored graft survival was excellent and comparable. Broader utilization of DKT from high KDPI kidneys and pediatric en bloc kidneys should be encouraged to better address the ongoing organ shortage.
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页数:14
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