External Validation of the International Prediction Tool in Korean Patients with Immunoglobulin A Nephropathy

Overview

Journal Kidney Res Clin Pract

Specialty Nephrology

Date 2022 May 11

PMID 35545218

Authors

Young Su Joo

Hyung Woo Kim

Chung Hee Baek

Jung Tak Park

Hajeong Lee

Beom Jin Lim

Tae-Hyun Yoo

Kyung Chul Moon

Ho Jun Chin

Shin-Wook Kang

Seung Hyeok Han

Affiliations

Soon will be listed here.

Abstract

Background: The International IgA Nephropathy Prediction Tool has been recently developed to estimate the progression risk of immunoglobulin A nephropathy (IgAN). This study aimed to evaluate the clinical performance of this prediction tool in a large IgAN cohort in Korea.

Methods: The study cohort was comprised of 2,064 patients with biopsy-proven IgAN from four medical centers between March 2012 and September 2021. We calculated the predicted risk for each patient. The primary outcome was occurrence of a 50% decline in estimated glomerular filtration rate (eGFR) from the time of biopsy or end-stage kidney disease. The model performance was evaluated for discrimination, calibration, and reclassification. We also constructed and tested an additional model with a new coefficient for the Korean race.

Results: During a median follow-up period of 3.8 years (interquartile range, 1.8-6.6 years), 363 patients developed the primary outcome. The two prediction models exhibited good discrimination power, with a C-statistic of 0.81. The two models generally underestimated the risk of the primary outcome, with lesser underestimation for the model with race. The model with race showed better performance in reclassification compared to the model without race (net reclassification index, 0.13). The updated model with the Korean coefficient showed good agreement between predicted risk and observed outcome.

Conclusion: In Korean IgAN patients, International IgA Nephropathy Prediction Tool had good discrimination power but underestimated the risk of progression. The updated model with the Korean coefficient showed acceptable calibration and warrants external validation.

Citing Articles

Machine learning-based diagnosis and prognosis of IgAN: A systematic review and meta-analysis.

Zhuang K, Wang W, Xu C, Guo X, Ren X, Liang Y Heliyon. 2024; 10(12):e33090.

PMID: 38988582 PMC: 11234108. DOI: 10.1016/j.heliyon.2024.e33090.

Machine learning-based 2-year risk prediction tool in immunoglobulin A nephropathy.

Kim Y, Jhee J, Park C, Oh D, Lim B, Choi H Kidney Res Clin Pract. 2023; 43(6):739-752.

PMID: 37919889 PMC: 11615444. DOI: 10.23876/j.krcp.23.076.

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