通过机器学习重新审视急性肾损伤肾脏的移植结果。

Reexamining Transplant Outcomes in Acute Kidney Injury Kidneys Through Machine Learning.

机构信息

Division of Transplant Surgery, Mayo Clinic, Phoenix, Arizona, USA.

Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA.

出版信息

Clin Transplant. 2024 Oct;38(10):e15470. doi: 10.1111/ctr.15470.

DOI:10.1111/ctr.15470

PMID:39367771

Abstract

BACKGROUND

Despite many people awaiting kidney transplant, kidney allografts from acute kidney injury (AKI) donors continue to be underutilized. We aimed to cluster kidney transplant recipients of AKI kidney allografts using an unsupervised machine learning (ML) approach.

METHODS

Using Organ Procurement and Transplantation Network-United Network for Organ Sharing (OPTN/UNOS) data, a consensus clustering cohort analysis was performed in 12 356 deceased donor kidney transplant recipients between 2015 and 2019 in whom donors had a terminal serum creatinine ≥1.5 mg/dL. Significant cluster characteristics were determined, and outcomes were compared.

RESULTS

The median donor terminal creatinine was 2.2 (interquartile range [IQR] 1.7-3.3) mg/dL. Cluster analysis was performed on 12 356 AKI kidney recipients, and three clinically distinct clusters were identified. Young, sensitized kidney re-transplant patients characterized Cluster 1. Cluster 2 was characterized by first-time kidney transplant patients with hypertensive and diabetic kidney diseases. Older diabetic recipients characterized Cluster 3. Clusters 1 and 2 donors were young and met standard kidney donor profile index (KDPI) criteria; Cluster 3 donors were older, more likely to have hypertension or diabetes, and meet high KDPI criteria. Cluster 1 had a higher risk of acute rejection, 3-year patient death, and graft failure. Cluster 3 had a higher risk of death-censored graft failure, patient death, and graft failure at 1 and 3 years. Cluster 2 had the best patient-, graft-, and death-censored graft survival at 1 and 3 years. Compared to non-AKI kidney recipients, the AKI clusters showed a higher incidence of delayed graft function (DGF, AKI: 43.2%, 41.7%, 45.3% vs. non-AKI: 25.5%); however, there were comparable long-term outcomes specific to death-censored graft survival (AKI: 93.6%, 93.4%, 90.4% vs. non-AKI: 92.3%), patient survival (AKI: 89.1%, 93.2%, 84.2% vs. non-AKI: 91.2%), and overall graft survival (AKI: 84.7%, 88.2%, 79.0% vs. non-AKI: 86.0%).

CONCLUSIONS

In this unsupervised ML approach study, AKI recipient clusters demonstrated differing, but good clinical outcomes, suggesting opportunities for transplant centers to incrementally increase kidney utilization from AKI donors.

摘要

背景

尽管有许多人等待接受肾移植，但来自急性肾损伤（AKI）供者的肾移植仍未得到充分利用。我们旨在使用无监督机器学习（ML）方法对 AKI 肾移植受者进行聚类。

方法

使用器官获取和移植网络-联合网络为器官共享（OPTN/UNOS）数据，在 2015 年至 2019 年间，对 12356 名接受死亡供者肾移植的患者进行了共识聚类队列分析，其中供者的终末期血清肌酐≥1.5mg/dL。确定了显著的聚类特征，并比较了结果。

结果

供者终末期肌酐中位数为 2.2（四分位距 [IQR] 1.7-3.3）mg/dL。对 12356 例 AKI 肾移植受者进行聚类分析，确定了三个具有临床意义的聚类。年轻、致敏的肾再移植患者为聚类 1。聚类 2 的特点是首次接受肾移植的高血压和糖尿病肾病患者。老年糖尿病患者为聚类 3。聚类 1 和 2 的供者年轻，符合标准肾供者概况指数（KDPI）标准；聚类 3 的供者年龄较大，更有可能患有高血压或糖尿病，且符合高 KDPI 标准。聚类 1 急性排斥反应、3 年患者死亡和移植物失功的风险较高。聚类 3 死亡风险较高，死亡风险，移植物失功风险在 1 年和 3 年。聚类 2 在 1 年和 3 年的患者、移植物和死亡风险方面具有最佳的移植物存活率。与非 AKI 肾移植受者相比，AKI 组的延迟移植物功能（DGF，AKI：43.2%、41.7%、45.3%vs.非 AKI：25.5%）发生率较高；然而，在死亡风险方面，特定于死亡风险的移植物存活率（AKI：93.6%、93.4%、90.4%vs.非 AKI：92.3%）、患者存活率（AKI：89.1%、93.2%、84.2%vs.非 AKI：91.2%）和总体移植物存活率（AKI：84.7%、88.2%、79.0%vs.非 AKI：86.0%）方面具有可比的长期结果。