Evidence map›Paper›PMID 40677345›Full record

ArticleKidney international reports2025

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

Emilio Rodrigo, Luis F Quintana, Teresa Vázquez-Sánchez, Ana Sánchez-Fructuoso, Anna Buxeda, Eva Gavela, Juan M Cazorla, Sheila Cabello, Isabel Beneyto, Angel M Sevillano and 11 more

Abstract read
In one paragraph

Article in Kidney international reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Emilio RodrigoNephrology Department, Hospital Universitario Marqués de Valdecilla/IDIVAL, Universidad de Cantabria, Santander, Spain.
Luis F QuintanaComplex Glomerular Disease Unit, Nephrology and Renal Transplantation Department, Hospital Clinic, Barcelona, Spain.
Teresa Vázquez-SánchezNephrology Department, Hospital Universitario Regional de Málaga, Málaga, Spain.
Ana Sánchez-FructuosoNephrology Department, Hospital Clínico San Carlos, Facultad de Medicina Universidad Complutense Madrid, Spain.
Anna BuxedaNephrology Department, Hospital del Mar, Barcelona, Spain.
Eva GavelaNephrology Department, Hospital Universitario Dr. Peset/FISABIO, Valencia, Spain.
Juan M CazorlaNephrology Department, Hospital Universitario Puerta del, Mar, Cádiz, Spain.
Sheila CabelloNephrology Department, Hospital Universitario Son Espases, Mallorca, Spain.
Isabel BeneytoKidney Transplant Unit, Nephrology Department, Hospital Universitario La Fe, Valencia, Spain.
Angel M SevillanoNephrology Department, Hospital Universitario 12 de Octubre, Madrid, Spain.
María O López-OlivaNephrology Department, Hospital Universitario La Paz, Madrid, Spain.
Fritz DiekmannNephrology and Renal Transplantation Department, Hospital Cliínic, Barcelona, Spain.
José M Gómez-OrtegaPathology Department, Hospital Universitario Marqués de Valdecilla/Santander, Spain.
Natividad Calvo-RomeroNephrology Department, Hospital Clínico San Carlos, Madrid, Spain.
María J Pérez-SáezNephrology Department, Hospital del Mar, Barcelona, Spain.
Asunción SanchoNephrology Department, Hospital Universitario Dr. Peset/FISABIO, Valencia, Spain.
Auxiliadora MazuecosNephrology Department, Hospital Universitario Puerta del, Mar, Cádiz, Spain.
Jordi Espí-ReigKidney Transplant Unit, Nephrology Department, Hospital Universitario La Fe, Valencia, Spain.
Hernando TrujilloNephrology Department, Hospital Universitario 12 de Octubre, Madrid, Spain.
Carlos JiménezNephrology Department, Hospital Universitario La Paz, Madrid, Spain.
Domingo HernándezNephrology Department, Hospital Universitario de Canarias, Tenerife, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

crescentsgraft lossIgA nephropathyinflammationkidney transplantationprediction toolsrecurrence

Identifiers

PMID40677345
PMCPMC12266211

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.