A pre-transplantation risk assessment tool for graft survival in Dutch pediatric kidney recipients

Loes Oomen; Huib de Jong; Antonia H M Bouts; Mandy G Keijzer-Veen; Elisabeth A M Cornelissen; Liesbeth L de Wall; Wout F J Feitz; Charlotte M H H T Bootsma-Robroeks

doi:10.1093/ckj/sfad057

A pre-transplantation risk assessment tool for graft survival in Dutch pediatric kidney recipients

Clin Kidney J. 2023 Mar 23;16(7):1122-1131. doi: 10.1093/ckj/sfad057. eCollection 2023 Jul.

Authors

Loes Oomen¹, Huib de Jong², Antonia H M Bouts³, Mandy G Keijzer-Veen⁴, Elisabeth A M Cornelissen⁵, Liesbeth L de Wall¹, Wout F J Feitz¹, Charlotte M H H T Bootsma-Robroeks^{5

6}

Affiliations

¹ Department of Urology, Division of Pediatric Urology, Radboudumc Amalia Children's Hospital, Nijmegen, The Netherlands.
² Department of Pediatric Nephrology, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands.
³ Department of Pediatric Nephrology, Amsterdam University Medical Center, Emma Children's Hospital, Amsterdam, The Netherlands.
⁴ Department of Pediatric Nephrology, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, The Netherlands.
⁵ Department of Pediatric Nephrology, Radboudumc Amalia Children's Hospital, Nijmegen, The Netherlands.
⁶ University of Groningen, University Medical Center Groningen, Department of Pediatrics, Pediatric Nephrology, Beatrix Children's Hospital, Groningen, The Netherlands.

Abstract

Background: 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.

Methods: 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.

Results: 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 (P < .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 (P < .01). Calibration plots showed an excellent fit.

Conclusions: 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.

Trial registration: ClinicalTrials.gov Identifier: NCT05388955.

Keywords: donor allocation; graft survival; pediatric kidney transplantation; prediction model.

Associated data

ClinicalTrials.gov/NCT05388955