Kidney Week

Abstract: SA-PO271

The Derivation and Validation of an International Multi-Ethnic Risk Prediction Model in IgA Nephropathy

Session Information

• Clinical Glomerular Disorders: Vasculitis, C3G, IgAN
  November 04, 2017 | Location: Hall H, Morial Convention Center
  Abstract Time: 10:00 AM - 10:00 AM

Category: Glomerular

• 1005 Clinical Glomerular Disorders

Authors

• Barbour, Sean, University of BC, Vancouver, British Columbia, Canada

Sean Barbour,

• Coppo, Rosanna, N/a, Torino, Italy

Rosanna Coppo,

• Suzuki, Yusuke, Juntendo University, Tokyo, Japan

Yusuke Suzuki,

• Liu, Zhi-Hong, Nanjing University, Nanjing, China

Zhi-Hong Liu,

• Espino-Hernandez, Gabriela, BC Renal Agency, Vancouver, British Columbia, Canada

Gabriela Espino-Hernandez,

• Reich, Heather N., University of Toronto, Toronto, Ontario, Canada

Heather N. Reich,

• Cattran, Daniel C., University of Toronto, Toronto, Ontario, Canada

Daniel C. Cattran,

Group or Team Name

• International IgAN Collaboration

Background

Predicting renal outcome in IgA nephropathy (IgAN) is challenging. A prediction model is needed to improve risk stratification that is properly validated in multiple ethnic groups worldwide and can be used in clinical practice. To overcome these obstacles, we used large datasets from international collaborators to generate an accurate prediction model in IgAN.

Methods

The derivation dataset was from European, Japanese and Chinese adult cohorts; the validation dataset was from separate North/South American, European, Chinese and Japanese cohorts. Time from biopsy to the composite outcome (50% decline in eGFR or ESRD) was analyzed using Cox survival models.

Results

The validation dataset (N=2784) is 42% Caucasian, 21% Japanese and 37% Chinese; 18% (N=495) experienced the composite outcome over a median 4.8 years of follow-up. Two models were considered: a reduced model containing eGFR, blood pressure, proteinuria at biopsy, and MEST score; and a full model that also contained age, sex, race, use of RAS blockade/immunosuppression, and crescents. Compared to the reduced model, the full model had improved prediction with better AIC (5679 vs 5648), R² (19 vs 21%), ΔC-statistic (0.01, 95%CI 0.008-0.03), continuous NRI (0.17, 95%CI 0.11-0.27) and IDI (0.03, 95%CI 0.01-0.05), with similar calibration curves. We will next externally validate the full model in the validation dataset (N=1401), and convert the model into web and mobile app calculators for implementation in clinical practice.

Conclusion

Using the largest and most diverse datasets to date in IgAN, we will generate the first risk prediction tool that is externally validated in multiple ethnic groups worldwide and can be easily implemented in clinical practice using web/app-based calculators. We expect the prediction model will become the international standard for risk stratification in IgAN, and will facilitate both clinical trial recruitment of high-risk patients and testing the added prediction benefit of novel biomarkers.