External Validation of the International IgA Nephropathy Prediction Tool in Older Adult Patients

Abstract

Purpose

The International IgA Nephropathy Prediction Tool (IIgAN-PT) can predict the risk of End-stage renal disease (ESRD) or estimated glomerular filtration rate (eGFR) decline ≥ 50% for adult IgAN patients. Considering the differential progression between older adult and adult patients, this study aims to externally validate its performance in the older adult cohort.

Patients and Methods

We analyzed 165 IgAN patients aged 60 and above from six medical centers, categorizing them by their predicted risk. The primary outcome was a ≥50% reduction in estimated glomerular filtration rate (eGFR) or kidney failure. Evaluation of both models involved concordance statistics (C-statistics), time-dependent receiver operating characteristic (ROC) curves, Kaplan–Meier survival curves, and calibration plots. Comparative reclassification was conducted using net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

Results

The study included 165 Chinese patients (median age 64, 60% male), with a median follow-up of 5.1 years. Of these, 21% reached the primary outcome. Both models with or without race demonstrated good discrimination (C-statistics 0.788 and 0.790, respectively). Survival curves for risk groups were well-separated. The full model without race more accurately predicted 5-year risks, whereas the full model with race tended to overestimate risks after 3 years. No significant reclassification improvement was noted in the full model without race (NRI 0.09, 95% CI: −0.27 to 0.34; IDI 0.003, 95% CI: −0.009 to 0.019).

Conclusion

: Both models exhibited excellent discrimination among older adult IgAN patients. The full model without race demonstrated superior calibration in predicting the 5-year risk.

Keywords:

• IgAN Progression
• statistical validation
• risk analysis
• progression risk
• prediction models
• older adults

Data Sharing Statement

The raw data supporting the conclusions of this article will be made available by the authors without undue reservation. Correspondence and requests for materials should be addressed to Guang-yan Cai.

Acknowledgments

I would like to thank the Natural Science Foundation of China (82170686) and the Grant for GYC (22KJLJ001) for funding this study. My heartfelt thanks go to my supervisor, Professor Guangyan Cai, for his invaluable and continuous guidance throughout my study and research endeavors. I also wish to express my appreciation to all the co-authors and contributors who have been integral to this study, for their collaborative efforts and insights.

Disclosure

The authors report no conflicts of interest in this work.