Using Regression Equations to Enhance Interpretation of Histology Lesions of Kidney Transplant Rejection

Abstract

Background. The Banff system for histologic diagnosis of rejection in kidney transplant biopsies uses guidelines to assess designated features—lesions, donor-specific antibody (DSA), and C4d staining. We explored whether using regression equations to interpret the features as well as current guidelines could establish the relative importance of each feature and improve histologic interpretation. Methods. We developed logistic regression equations using the designated features to predict antibody-mediated rejection (AMR/mixed) and T-cell–mediated rejection (TCMR/mixed) in 1679 indication biopsies from the INTERCOMEX study (ClinicalTrials.gov NCT01299168). Equations were trained on molecular diagnoses independent of the designated features. Results. In regression and random forests, the important features predicting molecular rejection were as follows: for AMR, ptc and g, followed by cg; for TCMR, t > i. V-lesions were relatively unimportant. C4d and DSA were also relatively unimportant for predicting AMR: by AUC, the model excluding them (0.853) was nearly as good as the model including them (0.860). Including time posttransplant slightly but significantly improved all models. By AUC, regression predicted molecular AMR and TCMR better than Banff histologic diagnoses. More importantly, in biopsies called “no rejection” by Banff guidelines, regression equations based on histology features identified histologic and molecular rejection-related changes in some biopsies and improved survival predictions. Thus, regression can screen for missed rejection. Conclusions. Using lesion-based regression equations in addition to Banff histology guidelines defines the relative important of histology features for identifying rejection, allows screening for potential missed diagnoses, and permits early estimates of AMR when C4d and DSA are not available.