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Am J Transplant. 2019 Oct;19(10):2846-2854. doi: 10.1111/ajt.15380. Epub 2019 May 6.

Using computer-assisted morphometrics of 5-year biopsies to identify biomarkers of late renal allograft loss.

Author information

1
Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota.
2
Department of Surgery and Immunology, Mayo Clinic, Rochester, Minnesota.
3
Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota.
4
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.

Abstract

The current Banff scoring system was not developed to predict graft loss and may not be ideal for use in clinical trials aimed at improving allograft survival. We hypothesized that scoring histologic features of digitized renal allograft biopsies using a continuous, more objective, computer-assisted morphometric (CAM) system might be more predictive of graft loss. We performed a nested case-control study in kidney transplant recipients with a surveillance biopsy obtained 5 years after transplantation. Patients that developed death-censored graft loss (n = 67) were 2:1 matched on age, gender, and follow-up time to controls with surviving grafts (n = 134). The risk of graft loss was compared between CAM-based models vs a model based on Banff scores. Both Banff and CAM identified chronic lesions associated with graft loss (chronic glomerulopathy, arteriolar hyalinosis, and mesangial expansion). However, the CAM-based models predicted graft loss better than the Banff-based model, both overall (c-statistic 0.754 vs 0.705, P < .001), and in biopsies without chronic glomerulopathy (c-statistic 0.738 vs 0.661, P < .001) where it identified more features predictive of graft loss (% luminal stenosis and % mesangial expansion). Using 5-year renal allograft surveillance biopsies, CAM-based models predict graft loss better than Banff models and might be developed into biomarkers for future clinical trials.

KEYWORDS:

biomarker; biopsy; clinical research/practice; kidney failure/injury; kidney transplantation/nephrology

PMID:
30947386
DOI:
10.1111/ajt.15380

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