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Risk prediction models for graft failure in kidney transplantation: a systematic review

Rémi Kaboré, Maria C Haller, Jérôme Harambat, Georg Heinze, Karen Leffondré
Nephrology, Dialysis, Transplantation 2017 April 1, 32 (suppl_2): ii68-ii76
Risk prediction models are useful for identifying kidney recipients at high risk of graft failure, thus optimizing clinical care. Our objective was to systematically review the models that have been recently developed and validated to predict graft failure in kidney transplantation recipients. We used PubMed and Scopus to search for English, German and French language articles published in 2005-15. We selected studies that developed and validated a new risk prediction model for graft failure after kidney transplantation, or validated an existing model with or without updating the model. Data on recipient characteristics and predictors, as well as modelling and validation methods were extracted. In total, 39 articles met the inclusion criteria. Of these, 34 developed and validated a new risk prediction model and 5 validated an existing one with or without updating the model. The most frequently predicted outcome was graft failure, defined as dialysis, re-transplantation or death with functioning graft. Most studies used the Cox model. There was substantial variability in predictors used. In total, 25 studies used predictors measured at transplantation only, and 14 studies used predictors also measured after transplantation. Discrimination performance was reported in 87% of studies, while calibration was reported in 56%. Performance indicators were estimated using both internal and external validation in 13 studies, and using external validation only in 6 studies. Several prediction models for kidney graft failure in adults have been published. Our study highlights the need to better account for competing risks when applicable in such studies, and to adequately account for post-transplant measures of predictors in studies aiming at improving monitoring of kidney transplant recipients.


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