The International IgA Nephropathy (IgAN) Prediction Tool is the preferred method in the 2021 KDIGO guidelines to predict, at the time of kidney biopsy, the risk of a 50% drop in estimated glomerular filtration rate or kidney failure. However, it is not known if the Prediction Tool can be accurately applied after a period of observation post-biopsy. Using an international multi-ethnic derivation cohort of 2,507 adults with IgAN, we updated the Prediction Tool for use one year after biopsy, and externally validated this in a cohort of 722 adults. The original Prediction Tool applied at one-year without modification had a coefficient of variation (R-2) of 55% and 54% and four-year concordance (C statistic) of 0.82 but poor calibration with under-prediction of risk (integrated calibration index (ICI) 1.54 and 2.11, with and without race, respectively). Our updated Prediction Tool had a better model fit with higher R2 (61% and 60%), significant increase in four-year C-statistic (0.87 and 0.86) and better four-year calibration with lower ICI (0.75 and 0.35). On external validation, the updated Prediction Tool had similar R-2 (60% and 58%) and four-year C-statistics (both 0.85) compared to the derivation analysis, with excellent four-year calibration (ICI 0.62 and 0.56). This updated Prediction Tool had similar prediction performance when used two years after biopsy. Thus, the original Prediction Tool should be used only at the time of biopsy whereas our updated Prediction Tool can be used for risk stratification one or two years post-biopsy.

Application of the International IgA Nephropathy Prediction Tool one or two years post-biopsy / Barbour, Sean J; Coppo, Rosanna; Zhang, Hong; Liu, Zhi-Hong; Suzuki, Yusuke; Matsuzaki, Keiichi; Er, Lee; Reich, Heather N; Barratt, Jonathan; Cattran, ; Daniel, C; Mene', P. - In: KIDNEY INTERNATIONAL. - ISSN 0085-2538. - 102:1(2022), pp. 160-172. [10.1016/j.kint.2022.02.042]

Application of the International IgA Nephropathy Prediction Tool one or two years post-biopsy

Mene' P
2022

Abstract

The International IgA Nephropathy (IgAN) Prediction Tool is the preferred method in the 2021 KDIGO guidelines to predict, at the time of kidney biopsy, the risk of a 50% drop in estimated glomerular filtration rate or kidney failure. However, it is not known if the Prediction Tool can be accurately applied after a period of observation post-biopsy. Using an international multi-ethnic derivation cohort of 2,507 adults with IgAN, we updated the Prediction Tool for use one year after biopsy, and externally validated this in a cohort of 722 adults. The original Prediction Tool applied at one-year without modification had a coefficient of variation (R-2) of 55% and 54% and four-year concordance (C statistic) of 0.82 but poor calibration with under-prediction of risk (integrated calibration index (ICI) 1.54 and 2.11, with and without race, respectively). Our updated Prediction Tool had a better model fit with higher R2 (61% and 60%), significant increase in four-year C-statistic (0.87 and 0.86) and better four-year calibration with lower ICI (0.75 and 0.35). On external validation, the updated Prediction Tool had similar R-2 (60% and 58%) and four-year C-statistics (both 0.85) compared to the derivation analysis, with excellent four-year calibration (ICI 0.62 and 0.56). This updated Prediction Tool had similar prediction performance when used two years after biopsy. Thus, the original Prediction Tool should be used only at the time of biopsy whereas our updated Prediction Tool can be used for risk stratification one or two years post-biopsy.
2022
IgA nephropathy; disease progression; end-stage kidney disease; prediction tool; risk prediction
01 Pubblicazione su rivista::01a Articolo in rivista
Application of the International IgA Nephropathy Prediction Tool one or two years post-biopsy / Barbour, Sean J; Coppo, Rosanna; Zhang, Hong; Liu, Zhi-Hong; Suzuki, Yusuke; Matsuzaki, Keiichi; Er, Lee; Reich, Heather N; Barratt, Jonathan; Cattran, ; Daniel, C; Mene', P. - In: KIDNEY INTERNATIONAL. - ISSN 0085-2538. - 102:1(2022), pp. 160-172. [10.1016/j.kint.2022.02.042]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1675005
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