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Abstract: SA-PO415

Molecular Risk Prediction in ANCA-Associated Crescentic Glomerulonephritis: Added Value over Clinical and Histologic Parameters

Session Information

Category: Glomerular Diseases

  • 1203 Glomerular Diseases: Clinical, Outcomes, and Trials

Authors

  • Adam, Benjamin A., University of Alberta, Edmonton, Alberta, Canada
  • Watson, Kristalee, University of Alberta, Edmonton, Alberta, Canada
  • Dromparis, Peter, University of Alberta, Edmonton, Alberta, Canada
  • Hildebrand, Ainslie M., University of Alberta, Edmonton, Alberta, Canada
  • Mengel, Michael, University of Alberta, Edmonton, Alberta, Canada
Background

Novel molecular tools have the potential to improve current clinical and histology-based risk classification systems for ANCA-associated crescentic glomerulonephritis (GN). We aimed to assess the utility of gene expression for improving biopsy-based risk prediction in these patients.

Methods

NanoString was used to measure the expression of 54 previously-described inflammation, nephron injury and crescent-related genes in 74 archival, formalin-fixed paraffin-embedded (FFPE) native kidney biopsies with ANCA-associated crescentic GN. Corresponding clinical and histologic data were retrieved. Multivariate Cox proportional hazards regression was used to identify the clinical, histologic and gene expression variables independently predictive of end-stage renal disease (ESRD). Receiver operating characteristic (ROC) curve, net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses were used to compare full and reduced logistic regression models composed of the independently predictive variables. Kaplan-Meier renal survival curves were used to further assess differences in model performance. Data analysis was performed using nSolver and R.

Results

Multivariate Cox analysis demonstrated lower patient age (p=0.002), higher percentage global glomerulosclerosis (p=0.003) and higher expression of crescent-related genes (p<0.001) to be independently predictive of ESRD. Comparison of logistic regression models demonstrated that adding crescentic gene expression to age and global glomerulosclerosis significantly improved the prediction of ESRD versus age and global glomerulosclerosis alone (AUC 85.1 vs. 71.9, respectively, p=0.023; continuous NRI 94.1%, p<0.001; IDI 17.3%, p=0.001). Kaplan-Meier renal survival curves further demonstrated improved model performance with the addition of crescentic gene expression (Figure 1).

Conclusion

Adding FFPE-derived gene expression to existing clinical and histologic parameters improves the prediction of ESRD in ANCA-associated crescentic GN.