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Validation of a prediction system for risk of kidney allograft failure in pediatric kidney transplant recipients: An international observational study

  • Julien Hogan
  • , Gillian Divard
  • , Olivier Aubert
  • , Rouba Garro
  • , Olivia Boyer
  • , Lee Alex Donald Cooper
  • , Alton Brad Farris
  • , Marc Fila
  • , Michael Seifert
  • , Anne Laure Sellier-Leclerc
  • , Jody Smith
  • , Alexander Fichtner
  • , Burkhard Tönshoff
  • , Katherine Twombley
  • , Bradley Warady
  • , Meghan Pearl
  • , Rima S. Zahr
  • , Carmen Lefaucheur
  • , Rachel Patzer
  • , Alexandre Loupy
  • PARCC - Paris-Centre de Recherche Cardiovasculaire
  • Hôpital Robert Debré
  • Emory University
  • Paris Descartes-Sorbonne Paris Cité University
  • Northwestern University
  • CHU Montpellier
  • Division of Pediatric Nephrology
  • Mother and Child University Hospital
  • Seattle Children's
  • Heidelberg University 
  • Medical University of South Carolina
  • Children's Mercy Hospital
  • University of California at Los Angeles
  • Le Bonheur Children's Medical Center

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Predicting long-term kidney allograft failure is an unmet need for clinical care and clinical trial optimization in children. We aimed to validate a kidney allograft failure risk prediction system in a large international cohort of pediatric kidney transplant recipients. Patients from 20 centers in Europe and the United States, transplanted between 2004 and 2017, were included. Allograft assessment included estimated glomerular filtration rate, urine protein-to-creatinine ratio, circulating antihuman leukocyte antigen donor-specific antibody, and kidney allograft histology. Individual predictions of allograft failure were calculated using the integrative box (iBox) system. Prediction performances were assessed using discrimination and calibration. The allograft evaluations were performed in 706 kidney transplant recipients at a median time of 9.1 (interquartile range, 3.3-19.2) months posttransplant; mean estimated glomerular filtration rate was 68.7 ± 28.1 mL/min/1.73 m2, and median urine protein-to-creatinine ratio was 0.1 (0.0-0.4) g/g, and 134 (19.0%) patients had antihuman leukocyte antigen donor-specific antibodies. The iBox exhibited accurate calibration and discrimination for predicting the outcomes up to 10 years after evaluation, with a C-index of 0.81 (95% confidence interval, 0.75-0.87). This study confirms the generalizability of the iBox to predict long-term kidney allograft failure in children, with performances similar to those reported in adults. These results support the use of the iBox to improve patient monitoring and facilitate clinical trials in children.

Original languageEnglish
Pages (from-to)1561-1569
Number of pages9
JournalAmerican Journal of Transplantation
Volume23
Issue number10
DOIs
StatePublished - Oct 2023
Externally publishedYes

Keywords

  • allograft failure
  • Banff classification
  • children
  • kidney transplantation
  • predictive model

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