External Validation of the International Prognosis Prediction Model of IgA Nephropathy

Abstract

Background

The International IgA Nephropathy (IgAN) Network developed and validated two prognostic prediction models for IgAN, one incorporating a race parameter. These models could anticipate the risk of a 50% reduction in estimated glomerular filtration rate (eGFR) or progression to end-stage renal disease (ESRD) subsequent to an IgAN diagnosis via renal biopsy. This investigation aimed to validate the International IgA Nephropathy Prediction Tool (IIgANPT) within a contemporary Chinese cohort.

Methods

Within this study,185 patients diagnosed with IgAN via renal biopsy at the Center for Kidney Disease, Second Affiliated Hospital of Nanjing Medical University, between January 2012 and December 2021, were encompassed. Each patient’s risk of progression was assessed utilizing the IIgANPT formula. The primary outcome, a 50% decline in eGFR or progression to ESRD, was examined. Two predictive models, one inclusive and the other exclusive of a race parameter, underwent evaluation via receiver-operating characteristic (ROC) curves, subgroup survival analyses, calibration plots, and decision curve analyses.

Results

The median follow-up duration within our cohort spanned 5.1 years, during which 18 patients encountered the primary outcome. The subgroup survival curves exhibited distinct separations, and the comparison of clinical and histological characteristics among the risk subgroups revealed significant differences. Both models demonstrated outstanding discrimination, evidenced by the areas under the ROC curve at five years: 0.882 and 0.878. Whether incorporating the race parameter or not, both prediction models exhibited acceptable calibration. Decision curve analysis affirmed the favorable clinical utility of both models.

Conclusions

Both prognostic risk evaluation models for IgAN exhibited remarkable discrimination, sound calibration, and acceptable clinical utility.

Keywords:

• IgA nephropathy
• prognosis
• prediction model
• international prediction tool
• external validation

Acknowledgments

The authors extend their sincere gratitude to all patients who participated in this clinical study.

Author contributions

LH contributed to the data analysis and wrote the article. YF contributed to the study design and revised the manuscript. JH, JL, and LX contributed to data collection. WH conceived of and revised the manuscript. All authors have read and approved the final version submitted for publication.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by grants from the National Nature Science Foundation of China (81170659/H0509 and 31571169/C110201) to WH.