Oomen Loes, de Jong Huib, Bouts Antonia H M, Keijzer-Veen Mandy G, Cornelissen Elisabeth A M, de Wall Liesbeth L, Feitz Wout F J, Bootsma-Robroeks Charlotte M H H T
Department of Urology, Division of Pediatric Urology, Radboudumc Amalia Children's Hospital, Nijmegen, The Netherlands.
Department of Pediatric Nephrology, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands.
Clin Kidney J. 2023 Mar 23;16(7):1122-1131. doi: 10.1093/ckj/sfad057. eCollection 2023 Jul.
A prediction model for graft survival including donor and recipient characteristics could help clinical decision-making and optimize outcomes. The aim of this study was to develop a risk assessment tool for graft survival based on essential pre-transplantation parameters.
The data originated from the national Dutch registry (NOTR; Nederlandse OrgaanTransplantatie Registratie). A multivariable binary logistic model was used to predict graft survival, corrected for the transplantation era and time after transplantation. Subsequently, a prediction score was calculated from the β-coefficients. For internal validation, derivation (80%) and validation (20%) cohorts were defined. Model performance was assessed with the area under the curve (AUC) of the receiver operating characteristics curve, Hosmer-Lemeshow test and calibration plots.
In total, 1428 transplantations were performed. Ten-year graft survival was 42% for transplantations before 1990, which has improved to the current value of 92%. Over time, significantly more living and pre-emptive transplantations have been performed and overall donor age has increased ( < .05).The prediction model included 71 829 observations of 554 transplantations between 1990 and 2021. Other variables incorporated in the model were recipient age, re-transplantation, number of human leucocyte antigen (HLA) mismatches and cause of kidney failure. The predictive capacity of this model had AUCs of 0.89, 0.79, 0.76 and 0.74 after 1, 5, 10 and 20 years, respectively ( < .01). Calibration plots showed an excellent fit.
This pediatric pre-transplantation risk assessment tool exhibits good performance for predicting graft survival within the Dutch pediatric population. This model might support decision-making regarding donor selection to optimize graft outcomes.
ClinicalTrials.gov Identifier: NCT05388955.
一个包含供体和受体特征的移植肾存活预测模型有助于临床决策并优化治疗结果。本研究的目的是基于移植前的基本参数开发一种移植肾存活风险评估工具。
数据来源于荷兰国家登记处（NOTR；荷兰器官移植登记处）。使用多变量二元逻辑模型预测移植肾存活，并对移植时代和移植后时间进行校正。随后，根据β系数计算预测分数。为进行内部验证，定义了推导队列（80%）和验证队列（20%）。通过受试者操作特征曲线的曲线下面积（AUC）、Hosmer-Lemeshow检验和校准图评估模型性能。
共进行了1428例移植手术。1990年以前进行的移植手术10年移植肾存活率为42%，目前已提高到92%。随着时间的推移，活体和抢先移植手术显著增多，供体总体年龄增加（P<0.05）。该预测模型纳入了1990年至2021年间554例移植手术的71829条观察数据。模型中纳入的其他变量包括受体年龄、再次移植、人类白细胞抗原（HLA）错配数和肾衰竭原因。该模型在1年、5年、10年和20年后的预测能力AUC分别为0.89、0.79、0.76和0.74（P<0.01）。校准图显示拟合良好。
这种儿科移植前风险评估工具在预测荷兰儿科人群移植肾存活方面表现良好。该模型可能有助于在供体选择方面进行决策，以优化移植结果。
ClinicalTrials.gov标识符：NCT05388955。
