Rodrigo Emilio, Quintana Luis F, Vázquez-Sánchez Teresa, Sánchez-Fructuoso Ana, Buxeda Anna, Gavela Eva, Cazorla Juan M, Cabello 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-Reig Jordi, Trujillo Hernando, Jiménez Carlos, Hernández Domingo
Nephrology Department, Hospital Universitario Marqués de Valdecilla/IDIVAL, Universidad de Cantabria, Santander, Spain.
Complex Glomerular Disease Unit, Nephrology and Renal Transplantation Department, Hospital Clinic, Barcelona, Spain.
Kidney Int Rep. 2025 Apr 21;10(7):2323-2333. doi: 10.1016/j.ekir.2025.04.028. eCollection 2025 Jul.
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.
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.
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.
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.
肾移植（KTx）后IgA肾病（IgAN）复发（IgANr）很常见，会降低移植肾存活率。已经开发了一些工具来预测原发性肾（国际IgA肾病预测工具[IIgAN-PT]）和移植肾（贝德娜罗娃预测工具[Bednarova-PT]）中预后不良风险较高的患者。我们旨在分析它们在最初报告人群以外的肾移植人群中的表现。
我们进行了一项多中心回顾性研究，纳入经活检证实为IgANr的肾移植患者。使用IIgAN-PT和Bednarova-PT计算死亡审查的移植肾丢失（DCGL）风险。我们使用鉴别和校准指标以及Kaplan-Meier曲线评估了两种预测模型的表现。
纳入了120例发生IgANr的肾移植患者。Bednarova-PT预测DCGL的时间依赖性受试者工作特征（ROC）曲线下面积（AUC）为83.5（95%CI：72.3-94.7），校准斜率为0.96（95%CI：0.37-1.49）。IIgAN-PT预测DCGL的时间依赖性ROC AUC为87.3（95%CI：77.58-97.02），校准斜率为2.49（95%CI：0.19-4.13）。IIgAN-PT往往低估高危个体的移植肾丢失风险。使用两种预测工具定义的最高风险组的Kaplan-Meier曲线与其他曲线明显分开。
IIgAN-PT和Bednarova-PT在预测IgANr后的DCGL方面表现良好，应用于识别风险最高的肾移植患者。两种模型都具有良好的鉴别能力且校准良好，尽管IIgAN-PT的校准斜率较高，倾向于低估高危个体的风险。
