Kidney Week

Abstract: PO1840

External Validation of International IgA Nephropathy Prediction Tool in a Singapore Cohort (EXIST Study)

Session Information

• Glomerular Diseases: Clinical, Outcomes, and Trials - 1
  October 22, 2020 | Location: On-Demand
  Abstract Time: 10:00 AM - 12:00 PM

Category: Glomerular Diseases

• 1203 Glomerular Diseases: Clinical, Outcomes, and Trials

Authors

• Lim, Ru Sin, Tan Tock Seng Hospital, Singapore, Singapore

Ru Sin Lim,

• Goh, Su mein, Tan Tock Seng Hospital, Singapore, Singapore

Su mein Goh,

• Yeo, See Cheng, Tan Tock Seng Hospital, Singapore, Singapore

See Cheng Yeo,

Background

IgA Nephropathy (IgAN) is the most common cause of glomerulonephritis worldwide, including in Singapore. Although IgAN may lead to end-stage kidney disease (ESKD), the risk of progressive kidney function decline is extremely heterogeneous; a reliable risk prediction model is important to inform both patients and physicians of renal prognosis and to guide clinical treatment decisions. A newly validated International IgAN prediction tool has been published recently and we aim to externally validate this model in our Singapore cohort.

Methods

We validated the predictive performance of the two full models (with or without race) derived from the International IgAN Prediction Tool study in our IgAN patient dataset over 11 years (Jul 2009 to Oct 2020) using external validation of survival prediction models (Royston and Altman). The discrimination and calibration of the models were tested using the R²_D measure, C statistics, Akaike Information Criterion (AIC), and calibration plot.

Results

The study included 119 patients; mean age of 43.3 (± 16.66) years; 62 (52.1%) were male; 90 (75.6%) Chinese, 12 (10.1%) Malay, 7 (5.9%) Indian and 10 (8.4%) other ethnicity. Complete case analysis was done with 93 patients. The 5-year risk of the primary outcome (50% reduction in estimated glomerular filtration rate or ESKD) was 15.0%. Oxford T2 histologic score was removed from the full model analysis as the number of observations is low (n=2). The original study reported AIC of 6338 for full model with race, 6379 for full model without race, vs 107.35, and 111.90 respectively in our study. The R²_D for the full models with and without race when applied to our validation cohort were 39% and 32% respectively, both were similar or better than the R²_D for the same models applied to the original derivation and validation cohorts (26.3%, 25.3%, and 35.3%, respectively). The C statistics for the full model with race was 0.858 (95% CI, 0.687-1.000), without race was 0.811 (95% CI, 0.599-1.000), comparable to the C statistics from the original derivation and validation analysis. Both full models were well-calibrated in our cohort, with good agreement between predicted and observed risk of the primary outcome at 5 years post-biopsy.

Conclusion

The 2 full models with or without race were shown to be validated in our multi-ethnic Singapore IgAN cohort for predicting disease progression.