Format

Send to

Choose Destination
Nephrol Dial Transplant. 2016 Feb;31(2):317-24. doi: 10.1093/ndt/gfv313. Epub 2015 Aug 27.

Identification of subgroups by risk of graft failure after paediatric renal transplantation: application of survival tree models on the ESPN/ERA-EDTA Registry.

Author information

1
Department of Nephrology, Dialysis and Transplantation, "Kidney and Transplantation" Research Centre, Annunziata Hospital, Cosenza, Italy de-Health Lab, DIMEG, University of Calabria, Rende, Italy.
2
Department of Medical Informatics, ERA-EDTA Registry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands Department of Medical Informatics, ESPN/ERA-EDTA Registry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
3
Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
4
Department of Medical Informatics, ESPN/ERA-EDTA Registry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands Department of Pediatric Nephrology, Emma Children's Hospital AMC, Amsterdam, The Netherlands.
5
Department of Pediatrics, Kuopio University Hospital, Kuopio, Finland.
6
Division of Pediatric Nephrology, Center for Pediatrics and Adolescent Medicine, University of Heidelberg, Heidelberg, Germany.
7
Medical Pediatric Clinic, Hospital Center and University of Nantes, Nantes, France.
8
Department of Nephrology, University Children's Hospital, Belgrade, Serbia.
9
Department of Pediatric Nephrology, Gaslini Children's Hospital, Genoa, Italy.
10
Department of Medical Informatics, ESPN/ERA-EDTA Registry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands Division of Pediatric Nephrology, Center for Pediatrics and Adolescent Medicine, University of Heidelberg, Heidelberg, Germany.
11
Department of Medical Informatics, ESPN/ERA-EDTA Registry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.

Abstract

BACKGROUND:

Identification of patient groups by risk of renal graft loss might be helpful for accurate patient counselling and clinical decision-making. Survival tree models are an alternative statistical approach to identify subgroups, offering cut-off points for covariates and an easy-to-interpret representation.

METHODS:

Within the European Society of Pediatric Nephrology/European Renal Association-European Dialysis and Transplant Association (ESPN/ERA-EDTA) Registry data we identified paediatric patient groups with specific profiles for 5-year renal graft survival. Two analyses were performed, including (i) parameters known at time of transplantation and (ii) additional clinical measurements obtained early after transplantation. The identified subgroups were added as covariates in two survival models. The prognostic performance of the models was tested and compared with conventional Cox regression analyses.

RESULTS:

The first analysis included 5275 paediatric renal transplants. The best 5-year graft survival (90.4%) was found among patients who received a renal graft as a pre-emptive transplantation or after short-term dialysis (<45 days), whereas graft survival was poorest (51.7%) in adolescents transplanted after long-term dialysis (>2.2 years). The Cox model including both pre-transplant factors and tree subgroups had a significantly better predictive performance than conventional Cox regression (P < 0.001). In the analysis including clinical factors, graft survival ranged from 97.3% [younger patients with estimated glomerular filtration rate (eGFR) >30 mL/min/1.73 m(2) and dialysis <20 months] to 34.7% (adolescents with eGFR <60 mL/min/1.73 m(2) and dialysis >20 months). Also in this case combining tree findings and clinical factors improved the predictive performance as compared with conventional Cox model models (P < 0.0001).

CONCLUSIONS:

In conclusion, we demonstrated the tree model to be an accurate and attractive tool to predict graft failure for patients with specific characteristics. This may aid the evaluation of individual graft prognosis and thereby the design of measures to improve graft survival in the poor prognosis groups.

KEYWORDS:

cut-off values; graft failure; interactions; paediatric renal transplantation; survival trees

PMID:
26320038
DOI:
10.1093/ndt/gfv313
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center