Kidney Biopsy Features Most Predictive of Clinical Outcomes in the Spectrum of Minimal Change Disease and Focal Segmental Glomerulosclerosis

Abstract

Background: Heterogeneity in disease course and treatment response among patients with MCD/FSGS necessitates a granular evaluation of kidney tissue features. This study aimed to identify histologic and ultrastructural descriptors of structural changes most predictive of clinical outcomes in the Nephrotic Syndrome Study Network (NEPTUNE).

Methods: Forty-eight histologic (37 glomerular, 9 tubulointerstitial, 2 vascular) and 20 ultrastructural descriptors were quantified by applying the NEPTUNE Digital Pathology Scoring System to NEPTUNE kidney biopsies. Outcomes included time from biopsy to disease progression, first complete remission of proteinuria, and treatment response. Relative importance of pathology and clinical predictors was obtained from random forest models, and predictive discrimination was assessed.

Results: Among 224 participants (34% Black, 24% Hispanic), model performance was excellent, with predictive discrimination of 0.9 for disease progression, 0.85 for complete remission, and 0.81 for treatment response. The most predictive descriptors of outcomes included both conventional-e.g., global sclerosis or segmental sclerosis and interstitial fibrosis/tubular atrophy-and novel features, including adhesion, interstitial foam cells, deflation, periglomerular fibrosis, mononuclear white blood cells, endothelial cell abnormalities, microvillous transformation, and acute tubular injury.

Conclusions: The most predictive descriptors of clinical outcomes among MCD/FSGS patients reflected structural changes in multiple renal compartments. Reporting these descriptors should be standardized to guide prognostication of proteinuric glomerular diseases.

Keywords: glomerular disease; kidney biopsy; nephrotic syndrome; outcome prediction; renal morphology.

Graphical abstract

Figure 1.

Analytic strategy. The left panel shows all combinations of parameters included in separate machine learning models, with dark shading denoting the primary models of interest and light shading denoting secondary models; EM, electron microscopy; Dem/Clin, demographics and clinical characteristics. The center panel illustrates that on the basis of the primary models with all predictors, including histologic and ultrastructural (EM) features and Dem/Clin, analyses using only the top 5, 10, 15, 20, etc. most predictive variables were conducted. The right panel shows that the top descriptors list was determined on the basis of variable importance and iAUC and each of these top descriptors were assessed in unadjusted Cox models.

Figure 2.

Flow diagram of study sample inclusion criteria and selection from NEPTUNE (square corner rectangles) and subsamples included in analyses of each clinical outcome (rounded corner rectangles).

Figure 3.

iAUC values from models with only top 5, 10, 15, 20, etc. predictors and variable importance values from models predicting disease progression (top row), complete remission (middle row), and treatment response (bottom row), using individual descriptors (left column) or grouped descriptors (right column).

Figure 4.

Standardized hazard ratios with 95% confidence intervals showing unadjusted associations from Cox proportional hazards models between top individual (top panel) and grouped (bottom panel) descriptors and time to disease progression (40% decline in eGFR with eGFR <90 ml/min per 1.73 m2 or kidney failure).

Figure 5.

Standardized hazard ratios with 95% confidence intervals showing unadjusted associations from Cox proportional hazards models between top individual (top panel) and grouped (bottom panel) descriptors and time to complete proteinuria remission (UPCR <0.3 g/g).

Figure 6.

Standardized hazard ratios with 95% confidence intervals showing unadjusted associations from Cox proportional hazards models between top individual (top panel) and grouped (bottom panel) descriptors and treatment response (complete proteinuria remission among participants treated with immunosuppression medication [ISM]).