External Validation of the International Prognosis Prediction Model of IgA Nephropathy

AbstractBackground 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-st...

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Main Authors: Lichao Hu, Yi Fang, Jiaxin Huang, Jin Liu, Lingling Xu, Weichun He
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Renal Failure
Subjects:
Online Access:https://www.tandfonline.com/doi/10.1080/0886022X.2024.2313174
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author Lichao Hu
Yi Fang
Jiaxin Huang
Jin Liu
Lingling Xu
Weichun He
author_facet Lichao Hu
Yi Fang
Jiaxin Huang
Jin Liu
Lingling Xu
Weichun He
author_sort Lichao Hu
collection DOAJ
description AbstractBackground 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.
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spelling doaj.art-be2fe0a4fb84495f837716941313d5a62024-03-22T04:42:51ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492024-12-0146110.1080/0886022X.2024.2313174External Validation of the International Prognosis Prediction Model of IgA NephropathyLichao HuYi FangJiaxin HuangJin LiuLingling XuWeichun He0Center for Kidney Disease, Second Affiliated Hospital, Nanjing Medical University, Nanjing, ChinaAbstractBackground 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.https://www.tandfonline.com/doi/10.1080/0886022X.2024.2313174IgA nephropathyprognosisprediction modelinternational prediction toolexternal validation
spellingShingle Lichao Hu
Yi Fang
Jiaxin Huang
Jin Liu
Lingling Xu
Weichun He
External Validation of the International Prognosis Prediction Model of IgA Nephropathy
Renal Failure
IgA nephropathy
prognosis
prediction model
international prediction tool
external validation
title External Validation of the International Prognosis Prediction Model of IgA Nephropathy
title_full External Validation of the International Prognosis Prediction Model of IgA Nephropathy
title_fullStr External Validation of the International Prognosis Prediction Model of IgA Nephropathy
title_full_unstemmed External Validation of the International Prognosis Prediction Model of IgA Nephropathy
title_short External Validation of the International Prognosis Prediction Model of IgA Nephropathy
title_sort external validation of the international prognosis prediction model of iga nephropathy
topic IgA nephropathy
prognosis
prediction model
international prediction tool
external validation
url https://www.tandfonline.com/doi/10.1080/0886022X.2024.2313174
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