ASN's Mission

To create a world without kidney diseases, the ASN Alliance for Kidney Health elevates care by educating and informing, driving breakthroughs and innovation, and advocating for policies that create transformative changes in kidney medicine throughout the world.

learn more

Contact ASN

1401 H St, NW, Ste 900, Washington, DC 20005

email@asn-online.org

202-640-4660

The Latest on Twitter

Kidney Week

Abstract: PO1528

Morphologic Descriptors Most Predictive of Clinical Outcomes in Minimal Change Disease and FSGS

Session Information

Category: Glomerular Diseases

  • 1203 Glomerular Diseases: Clinical, Outcomes, and Trials

Authors

  • Zee, Jarcy, University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Liu, Qian, Arbor Research Collaborative for Health, Ann Arbor, Michigan, United States
  • Smith, Abigail R., Arbor Research Collaborative for Health, Ann Arbor, Michigan, United States
  • Mariani, Laura H., University of Michigan, Ann Arbor, Michigan, United States
  • Hodgin, Jeffrey B., University of Michigan, Ann Arbor, Michigan, United States
  • Gillespie, Brenda W., University of Michigan, Ann Arbor, Michigan, United States
  • Barisoni, Laura, Duke University, Durham, North Carolina, United States
  • Holzman, Lawrence B., University of Pennsylvania, Philadelphia, Pennsylvania, United States
Background

Previous studies applying the NEPTUNE Digital Pathology Scoring System (NDPSS) uncovered the value of kidney tissue features for clinically relevant patient subcategorization. This study aims to identify histologic and ultrastructural descriptors most predictive of clinical outcomes in NEPTUNE patients.

Methods

39 glomerular, 9 tubulointerstitial, 2 vascular, and 20 ultrastructural descriptors were quantified using the NDPSS on 39 MCD, 61 MCD-like, and 124 FSGS NEPTUNE digital kidney biopsies. Outcomes included time from biopsy to disease progression (kidney failure or ≥ 40% eGFR decline with eGFR <90) and first complete remission (CR) of proteinuria (UPCR <0.3). Relative importance of descriptors for prediction of outcomes was obtained from random forest models, without adjusting for clinical features.

Results

The mean age, eGFR and UPCR at biopsy for the total 224 participants was 28.8, 85.2, and 5.4, respectively. Model performance was excellent (predictive discrimination= 0.902 for disease progression and 0.853 for CR). Most predictive descriptors included conventional (e.g., global sclerosis or segmental sclerosis, and interstitial fibrosis/tubular atrophy) and unconventional features [Fig]. Top 10 predictors included inflammation, podocyte abnormalities, and acute tubular injury for both outcomes; deflation, interstitial foam cells, and collapse for disease progression; and endothelial cell abnormalities, hyalinosis, and periglomerular fibrosis for CR.

Conclusion

Most predictive descriptors of proteinuric glomerulopathies reflected structural changes in various renal compartments. Reporting these descriptors should be standardized to guide the subcategorization of proteinuric glomerular diseases and improve targeted clinical care.

Funding

  • NIDDK Support