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: SA-PO920

Evaluating GFR Slope as a Surrogate End Point Across Diseases

Session Information

Category: CKD (Non-Dialysis)

  • 2202 CKD (Non-Dialysis): Clinical‚ Outcomes‚ and Trials

Authors

  • Collier, Willem H., University of Utah Health, Salt Lake City, Utah, United States
  • Haaland, Benjamin, University of Utah Health, Salt Lake City, Utah, United States
  • Inker, Lesley Ann, Tufts Medical Center, Boston, Massachusetts, United States
  • L Heerspink, Hiddo Jan, Universitair Medisch Centrum Groningen Afdeling Cardiologie, Groningen, Groningen, Netherlands
  • Greene, Tom, University of Utah Health, Salt Lake City, Utah, United States

Group or Team Name

  • Chronic Kidney Disease Epidemiology Collaboration
Background

We have previously demonstrated that treatment effects on GFR slope can be used to accurately predict treatment effects on time-to-kidney failure (KFRT) in randomized CKD trials. It has been hypothesized that accuracy can vary by sub-categories of disease. Yet, low statistical power in subgroups can challenge the interpretation of subgroup-to-subgroup variability in GFR slope as a surrogate.

Methods

We performed analyses on N=47 previously conducted CKD trials. For each trial, we estimated treatment effects on time-to-KFRT using proportional hazards models and treatment effects on GFR chronic slope using mixed-effects models. Meta-regression methods were used to assess the strength of the association between treatment effects on the separate endpoints within three disease-defined subgroups: Diabetes (DN), glomerular diseases (GN), and CKD without diabetes (CKD). We used a novel Bayesian modeling approach (partial-pooling) to facilitate improved precision in our estimation of model parameters. The results were contrasted with those obtained using an earlier no-pooling approach.

Results

Relative to no-pooling, the partial-pooling model improved precision in parameter estimates key to the interpretation of the surrogate (see the 95% credible intervals in Figure 1 legends). The partial-pooling model also improved prediction of treatment effects on time-to-KFRT (see narrower confidence bands on the left of Figure 1).

Conclusion

On a collection of CKD trials, use of a novel meta-regression approach for surrogate evaluation indicated homogeneity in the quality of GFR chronic slope across subgroups of trials defined by disease.

α: Meta-regression intercept β: Meta-regression slope. We display meta-regression lines as well as the upper and lower confidence bands in grey (partial-poling) and red (no-pooling). Grey circles are trials not in the featured disease-defined subgroup.

Funding

  • Private Foundation Support