Validation of 2 Prognostic Models to Predict Renal Allograft Outcome After IgA Nephropathy Recurrence

INTRODUCTION: IgA nephropathy (IgAN) recurrence (IgANr) after kidney transplantation (KTx) is common and contributes to reducing graft survival. Some tools have been developed to predict the patients who are at a higher risk of poor outcomes among the native (international IgAN prediction tool [IIgA...

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Detalles Bibliográficos
Autores: Rodrigo, Emilio, Quintana, Luis F, Vázquez-Sánchez, Teresa, Sánchez-Fructuoso, Ana, Buxeda, Anna, Gavela, Eva, Cazorla, Juan M, Cabello Pelegrin, Sheila, Beneyto, Isabel, Sevillano, Angel M, López-Oliva, María O, Diekmann, Fritz, Gómez-Ortega, José M, Calvo-Romero, Natividad, Pérez-Sáez, María J, Sancho, Asunción, Mazuecos, Auxiliadora, Espí, Alejandro, Trujillo, Hernando, Jiménez, Carlos, Hernández, Domingo
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Conselleria de Salut i Consum del Govern de les Illes Balears
Repositorio:Docusalut
Idioma:inglés
OAI Identifier:oai:docusalut.com:20.500.13003/25428
Acceso en línea:https://hdl.handle.net/20.500.13003/25428
Access Level:acceso abierto
Palabra clave:Glomerulonephritis, IGA
Inflammation
Kidney Transplantation
Recurrence
Glomerulonefritis por IGA
Inflamación
Trasplante de Riñón
Recurrencia
IgA nephropathy
crescents
graft loss
inflammation
kidney transplantation
prediction tools
recurrence
Descripción
Sumario:INTRODUCTION: IgA nephropathy (IgAN) recurrence (IgANr) after kidney transplantation (KTx) is common and contributes to reducing graft survival. Some tools have been developed to predict the patients who are at a higher risk of poor outcomes among the native (international IgAN prediction tool [IIgAN-PT]) and graft (Bednarova's prediction tool [Bednarova-PT]) kidney. We aimed to analyze their performance in a KTx population other than the originally reported. METHODS: We performed a multicenter retrospective study including KTx with biopsy-proven IgANr. IIgAN-PT and Bednarova-PT were used to calculate the risk of death-censored graft loss (DCGL). We assessed the performance of both prediction models using discrimination and calibration metrics and Kaplan-Meier plots. RESULTS: One hundred twenty KTx with IgANr were included. The time-dependent receiver operating characteristic (ROC) area under the curve (AUC) of Bednarova-PT for predicting DCGL was 83.5 (95% CI: 72.3-94.7) and the calibration slope was 0.96 (95% CI: 0.37-1.49). The time-dependent ROC AUC of IIgAN-PT for predicting DCGL was 87.3 (95% CI: 77.58-97.02) and the calibration slope was 2.49 (95% CI: 0.19-4.13). IIgAN-PT tended to underestimate the graft-loss risk in high-risk individuals. The Kaplan-Meier curve of the highest risk group, defined by using both prediction tools, was clearly separated from the other curves. CONCLUSION: Both IIgAN-PT and Bednarova-PT performed well in predicting DCGL after IgANr and should be used to identify those KTx at the highest risk. Both models had good discriminatory ability and were well-calibrated, although the calibration slope was higher for IIgAN-PT, tending to underestimate the risk in high-risk individuals.

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