The predictive value of kidney allograft baseline biopsies for long-term graft survival.

JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY(2013)

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Abstract
The effect of baseline histology and individual histologic lesions at the time of transplantation on long-term graft survival has been evaluated using different scoring systems, but the predictive capacity of these systems has not been adequately validated. All kidney recipients transplanted in a single institution between 1991 and 2009 who underwent a baseline kidney allograft biopsy at transplantation were included in this prospective study (N=548). All baseline biopsies were rescored according to the updated Banff classification, and the relationship between the individual histologic lesions and donor demographics was assessed using hierarchical clustering and principal component analysis. Survival analysis was performed using Cox proportional hazards analysis and log-rank testing. Mean follow-up time was 6.7 years after transplantation. Interstitial fibrosis, tubular atrophy, and glomerulosclerosis associated significantly with death-censored graft survival, whereas arteriolar hyalinosis and vascular intimal thickening did not. Notably, donor age correlated significantly with interstitial fibrosis, tubular atrophy, and glomerulosclerosis and associated independently with graft survival. On the basis of these findings, a novel scoring system for prediction of 5-year graft survival was constructed by logistic regression analysis. Although the predictive performance of previously published histologic scoring systems was insufficient to guide kidney allocation in our cohort (receiver operating characteristic area under the curve 0.62 for each system), the new system based on histologic data and donor age was satisfactory for prediction of allograft loss (receiver operating characteristic area under the curve = 0.81) and may be valuable in the assessment of kidney quality before transplantation.
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Key words
predictive value of tests,frozen sections,proportional hazards models
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