Corrigendum: Early post-transplant urinary EGF as a potential predictor of long-term allograft loss in kidney transplant recipients

Abstract

Corrigendum on: Créon A, Morin L, Garcia V, Aouni L, Rabant M, Terzi F and Anglicheau D (2025) Early Post-Transplant Urinary EGF as a Potential Predictor of Long-Term Allograft Loss in Kidney Transplant Recipients. Transpl. Int. 38:15061. doi: 10.3389/ti.2025.15061 In the original article, we referred throughout the manuscript to a prognostic score described in Loupy et al., BMJ, 2019 (1) using the name "iBox". After publication, the authors of the original score informed us that this score is patented and that the proprietary instrument marketed under the name "iBox" may not be identical in every respect to our independently implemented algorithm.To avoid any confusion, the terminology used in our article has been updated to reflect that we implemented the score as described in its original publication, without access to the patented version.These wording changes are editorial in nature and concern only the terminology used to describe the prognostic score. The authors apologize for this error and state that this does not alter the data, analyses, numerical results, interpretation, or scientific conclusions of the article in any way. The original article has been updated. (1) Loupy A, Aubert O, Orandi BJ, et al. Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study. BMJ. 2019;366:l4923. doi:10.1136/bmj.l4923 The changes made are detailed below: Abstract (starting from line 8) Model performance was compared to an existing prediction model using 7-year time-dependent AUC and Akaike Information Criterion (AIC), with internal validation via bootstrap resampling. Temporal validation was performed in an independent cohort of 203 patients. uEGF correlated with markers of chronic injury, including eGFR, donor age, and interstitial fibrosis. After a median 8.8-year follow-up, lower uEGF was independently associated with allograft loss (adjusted HR 0.19; 95% CI, 0.11-0.32). Adding uEGF to the existing prediction model improved discrimination (AUC 0.72 vs. 0.63) and reduced AIC (383 vs. 394). Paragraph 3 line 24:Finally, a model was constructed by adding uEGF to the allograft loss risk score (ALRS) described by Loupy et al., which is the reference model for allograft loss prediction (5). Paragraph 4, line 27, 28 and 36:The models' discrimination ability was evaluated using the time-dependent area under the curve (AUC) at 7 years, as risks of allograft loss beyond 7 years could not be derived from the original ALRS publication (see Supplementary Methods). Discrimination was assessed for both the ALRS model and the extended model including uEGF, and their 7-year AUC was compared as in Blanche et al. To reflect the original ALRS publication, observed 7-year risks were estimated using the Kaplan-Meier method rather than the Aalen-Johansen estimator when assessing calibration. Section "UEGF is associated with allograft loss in multivariable analysis" Lines 37-38: (3) combination of uEGF and ALRS model. Lines 42-44: When adjusting on the 3 variables most strongly predicting uEGF levels by random forest, or on the ALRS model, uEGF remained significantly associated with the risk of allograft loss (Figure 4 and Table S3).Section "UEGF improves allograft loss risk prediction" Lines 2-3: Given that uEGF was independently associated with allograft loss, we assessed whether adding it to the ALRS model improved predictive performance.Lines 5-7: The 7-year timepoint was chosen as it is the longest follow-up duration for which the ALRS score could be computed. The addition of uEGF to the ALRS model improved discrimination (7-year AUC: 0.72 [0.61-0.82] vs. 0.63 [0.53-0.74], p-val=0.002)… Line 9: The association between uEGF and allograft loss, adjusted on the ALRS score, is visually depicted in Figure 5.Section "Internal validation" 1000 random samples from the original cohort were generated using a bootstrapping procedure. The optimism-corrected 7-year AUC of the uEGF+ALRS model was 0.71 (95% CI 0.68-0.74). The optimismcorrected calibration plot suggested that the model tended to overestimate risk in individuals at higher predicted risk and underestimate it in those at lower predicted risk (Figure 6). Paragraph 2, line 17: Similarly, in the ALRS derivation cohort, day 0 parameters were not associated with allograft survival after adjustment for post-transplant parameters, which were mostly evaluated within the first 18 months post-transplant Paragraph 4, line 40: The association between uEGF and graft failure was internally validated and robust to adjustment for the ALRS model.Paragraph 5, lines 43-48: Although the addition of uEGF improved the predictive performance of the ALRS model in our cohort, it is important to note that the baseline performance of the ALRS was substantially lower than that reported in its original derivation and validation studies. Several factors may account for this discrepancy. Notably, our cohort consisted exclusively of patients assessed at 3 months posttransplant, an earlier time point than the one used in the development of the ALRS score. Paragraph 6, line 6: Altogether, our findings contribute to the ongoing discussion of whether uEGF offers prognostic information beyond established markers such as eGFR, or integrated prognostic models like the ALRS. Average predicted (x-axis) and observed (y-axis) 7-year risks across quantiles of predicted risk. To reflect the original ALRS publication (5), observed 7-year risks were estimated using the Kaplan-Meier method rather than the Aalen-Johansen estimator.