External validation of a prognostic model predicting renal graft function one year after brain-dead donor kidney transplantation
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Abstract Background. A large German transplant center recently published a prognostic model predicting renal graft function one year after deceased donor kidney transplantation relying solely on pre-transplant variables. The aim of this study is to externally validate this prognostic model. Methods. In this external validation cohort, we retrospectively analyzed clinical data from deceased donor kidney transplant recipients undergoing kidney transplantation between January 2007 and December 2023 at University Hospital RWTH Aachen. Receiver operating characteristics (ROC) curves were analyzed to validate the abovementioned prognostic model based on donor age, donor serum creatinine, recipient body mass index, re-transplantation > 2nd kidney transplant, and cold ischemia time. Glomerular filtration rate levels were categorized using the Kidney Disease: Improving Global Outcomes (KDIGO) stages G1 – G5 with KDIGO G1 as reference category. Results. In the observation period, a total of 494 kidney transplants were performed at our institution, 350 (70.9%) thereof from donation after brain death (DBD). The median one-year estimated glomerular filtration (eGFR) was 42 [12-94] mL/min/1.73 m2. The areas under the receiver operating characteristics curve for KDIGO categories G2, G3a, G3b, G4, and G5 were 0.8664, 0.7167, 0.7442, 0.7315, and 0.7331, respectively, thus indicating a high sensitivity and specificity of prediction for all stages of renal graft function. Conclusions. Using the proposed prognostic model, we could successfully predict each eGFR category in our external cohort of deceased donor kidney transplant recipients one year after transplantation. Thus, we consider the prognostic model as highly valuable. It could be used in clinical routine to facilitate kidney allocation, optimize donor-recipient matching, and enhance long-term graft survival.
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External validation of a prognostic model predicting renal graft function one year after brain-dead donor kidney transplantation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article External validation of a prognostic model predicting renal graft function one year after brain-dead donor kidney transplantation Philipp Tessmer, Clara A. Weigle, Franziska A. Meister, Wilfried Gwinner, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7077428/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background. A large German transplant center recently published a prognostic model predicting renal graft function one year after deceased donor kidney transplantation relying solely on pre-transplant variables. The aim of this study is to externally validate this prognostic model. Methods. In this external validation cohort, we retrospectively analyzed clinical data from deceased donor kidney transplant recipients undergoing kidney transplantation between January 2007 and December 2023 at University Hospital RWTH Aachen. Receiver operating characteristics (ROC) curves were analyzed to validate the abovementioned prognostic model based on donor age, donor serum creatinine, recipient body mass index, re-transplantation > 2 nd kidney transplant, and cold ischemia time. Glomerular filtration rate levels were categorized using the Kidney Disease: Improving Global Outcomes (KDIGO) stages G1 – G5 with KDIGO G1 as reference category. Results. In the observation period, a total of 494 kidney transplants were performed at our institution, 350 (70.9%) thereof from donation after brain death (DBD). The median one-year estimated glomerular filtration (eGFR) was 42 [12-94] mL/min/1.73 m 2 . The areas under the receiver operating characteristics curve for KDIGO categories G2, G3a, G3b, G4, and G5 were 0.8664, 0.7167, 0.7442, 0.7315, and 0.7331, respectively, thus indicating a high sensitivity and specificity of prediction for all stages of renal graft function. Conclusions. Using the proposed prognostic model, we could successfully predict each eGFR category in our external cohort of deceased donor kidney transplant recipients one year after transplantation. Thus, we consider the prognostic model as highly valuable. It could be used in clinical routine to facilitate kidney allocation, optimize donor-recipient matching, and enhance long-term graft survival. Deceased donor kidney transplantation External validation Renal graft function Kidney transplantation Prognostic model Figures Figure 1 Figure 2 Background Kidney transplantation (KTx) is the treatment of choice for end-stage renal disease (ESRD), as transplant recipients benefit from superior patient survival (1–3) and improved quality of life (4) compared with dialysis patients. In Germany, there is a large disparity between organ demand and supply resulting in median waiting times of approximately nine years within the Eurotransplant kidney allocation system (ETKAS) (5). Medical factors are of paramount importance when selecting an optimal kidney transplant recipient for a specific donor organ to ensure adequate early organ function (6). One-year creatinine levels (7) and one-year estimated glomerular filtration rate (eGFR) (8) are suitable indicators for long-term graft survival. Zwirner et al. recently published a prognostic model predicting eGFR categories according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines one year after deceased donor kidney transplantation, relying solely on variables available before transplantation (9). The training data set consisted of 1.172 kidney transplants from donation after brain death (DBD) donors between January 2000 and December 2012 (9). If this model is valid to external cohorts, it could be suitable to predict KDIGO-based eGFR categories one year after kidney transplantation, thereby serving as an easy-to-apply prognostic tool for the prediction of long-term graft survival, without the need for histological analyses via pre-transplant biopsies. The model could be used to optimize kidney allocation, improve donor-recipient matching and thus to decrease the need for re-transplantation after failure of the first graft. The aim of this study was to externally validate the proposed prognostic model in our cohort of deceased donor kidney transplantations. Methods Study design This external validation study was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnostics (TRIPOD) guidelines (10). We retrospectively analyzed clinical data from all patients undergoing donation after brain death kidney transplantation at the University Hospital RWTH Aachen (UH-RWTH) between January 2007 and December 2023. A clinical trial number was not applicable. Clinical data included donors’ and recipients’ baseline characteristics, laboratory parameters and transplant data. The parameters that were identified as significant in the multivariate logistic regression model of the aforementioned publication (9) were used as test variables in Receiver Operating Characteristic (ROC) analyses. The parameters included were donor age, last donor serum creatinine, recipient body mass index, re-transplantation > 2 nd kidney transplantation, and cold ischemia time (CIT). Follow-up was one year after kidney transplantation (±3 months). Patients who were on dialysis at the time of follow-up were excluded from the analysis to ensure compatibility with the original study cohort. Patients with living kidney donation or simultaneous liver or pancreas transplantation were also excluded. The included patients are shown in a study flow chart (see Figure 1). In the first year after kidney transplantation, all patients received a standard triple immunosuppressive regimen. Study endpoints The study endpoint used for the validation of the prognostic model published by Zwirner et al. (9) was the recipient’s graft function according to KDIGO-based eGFR categories one year after kidney transplantation (±3 months). Definition of variables As in the original publication (9), the glomerular filtration rate of the kidney transplant was estimated using the 4-variable Modification of Diet in Renal Disease (MDRD) formula as published by Levey et al. (11). eGFR values were categorized using the KDIGO guidelines (12). The degree of albuminuria was not taken into account as a factor for categorization of renal function. Surgical procedure In most cases, heterotopic graft implementation to the right or left iliac fossa was performed. Arterial and venous anastomoses were preferably applied to the common and external iliac artery and vein, respectively. In most cases, modified Lich-Gregoire (13, 14) ureteroneocystostomy was performed, with temporal stenting of the graft ureter. Statistical analysis Descriptive statistics were conducted using SPSS statistical software (SPSS® Statistics 29.0.1.0, IBM® Corporation). ROC analyses were performed with JMP Pro 13 software (SAS Institute, Cary, NC, USA). Statistical significance was set at a two-sided p -value <0.050. Areas under the receiver operating curve characteristic (AUROC) analyses were used to test the performance and external validity of the prognostic model proposed by Zwirner et al. (9) to predict each eGFR category one year after transplantation in our cohort of DBD kidney transplantations. A normal kidney function (KDIGO category G1) was set as reference category, indicating unimpaired renal graft function. Ethical approval The Institutional Review Board of UH-RWTH waived informed consent due to the collection of routine clinical data and the retrospective study design. Prior to analysis, patient data and records were anonymized and de-identified. All methods were carried out in accordance with relevant guidelines and regulations under ethical declarations. Results Descriptive statistics In the observation period, 494 kidney transplants were performed at UH-RWTH. 144 (29.1%) grafts were from living and 350 (70.9%) from deceased donors. Of the 350 deceased donor kidney transplants, 67 were excluded for various reasons: returning to dialysis before reaching the study endpoint (n=29; 43.3%), patient death (n=23; 34.3%), simultaneous liver transplantation (n=13; 19.4%) and loss to follow-up (n=2; 3.0%). Thus, the eGFR of 283 deceased donor kidney transplant recipients at one-year follow up was finally analyzed. Figure 1 gives an overview of the included patients. Of the study group, most recipients were male (n=190; 67.1%) with a median age of 57 (22-80) years and a median body mass index (BMI) of 25 (15-52) kg/m 2 , respectively. The median donor age and the donor’s last serum creatinine was 57 (8-88) years and 88 (25-650) µmol/L, respectively. The median eGFR one year after transplantation was 42 [12-94] mL/min/1.73 m 2 . One-year eGFR was categorized as KDIGO G1, G2, G3a, G3b, G4, and G5 in two (0.7%), 39 (13.8%), 70 (24.7%), 107 (37.8%), 62 (21.9%), and three (1.1%) cases, respectively. Most patients received their first or second graft (n=279; 98.6%). Only four (1.4%) patients were third graft recipients. In our cohort, no fourth or fifth kidney transplants were performed. The median cold ischemia time was 743 (54-1760) minutes. Table 1 shows the patient’s descriptive statistics. Table 1 Descriptive statistics Parameter Kidney transplants, n 494 Deceased donor, n [%] 350 [70.9] Living donor, n [%] 144 [29.1] Study group , n [%] 283 [57.3] Rec Age, median [range] 57 [22-80] Rec Sex male, n [%] 190 [67.1] female, n [%] 93 [32.9] Rec BMI (kg/m 2 ), median [range] 25 [12-52] Rec 1-year-eGFR (ml/min/1,73 m²), median [range] 42 [12-94] Don age , median [range] 57 [8-88] Don last serum creatinine (µmol/l), median [range] 88 [25-650] CIT (min), median [range] 743 [54-1760] >2 nd KTx, n [%] 4 [1.4] Baseline characteristics of n=283 deceased donor kidney transplant recipients. Parameter that were identified as statistically significant in the multivariate analysis by Zwirner et al. are marked in bold. These parameters were then used as test variables in receiver operating characteristic analyses. BMI = Body mass index, CIT = Cold ischemia time, Don = Donor, eGFR = estimated glomerular filtration rate; KTx = Kidney transplant, Rec = Recipient. AUROC analysis Using the proposed prognostic model by Zwirner et al., the AUROC values for KDIGO categories G2, G3a, G3b, G4, and G5 were 0.8664, 0.7167, 0.7442, 0.7315, and 0.7331, respectively, indicating a high sensitivity and specificity of prediction for each eGFR category. Table 2 classifies the patients’ one-year eGFR according to the KDIGO classification. Figure 2 shows the corresponding ROC curves for each eGFR category. Table 2 KDIGO classification KDIGO Number of patients G1 , n [%] 2 [0.7] G2 , n [%] 39 [13.8] G3a , n [%] 70 [24.7] G3b , n [%] 107 [37.8] G4 , n [%] 62 [21.9] G5 , n [%] 3 [1.1] One-year eGFR after deceased donor kidney transplantation was categorized according to the Kidney Disease: Improving Global Outcomes Guidelines (KDIGO) stades G1 to G5, n=283. Discussion This study aimed to evaluate the external validity and performance of a recently published prognostic model predicting KDIGO-graded renal graft function one year after DBD kidney transplantation. For reliable prognostic models, areas under the ROC curves typically range from 0.6 to 0.85 (15), with values above 0.700 generally considered to reflect high predictive sensitivity and specificity (16). As the main finding of our study, we were able to demonstrate AUROC values above 0.700 for each KDIGO-based eGFR category in our cohort of deceased donor kidney transplant recipients. To the best of our knowledge, this is the first study to provide external validation of a prognostic model capable of reliably predicting eGFR categories, according to the KDIGO classification, one year after DBD kidney transplantation. To date, a comparable model has only been published for the prediction of pancreas and kidney function following simultaneous organ transplantation (17). Our prognostic model offers several advantages. In total, 1172 kidney transplant recipients from the original study cohort and 283 patients from our cohort were included, resulting in an overall sample size of 1455 patients for the development and validation of the prognostic model. In addition to this comparatively large sample size, the inclusion periods of the original cohort (01/00 – 12/12) and our cohort (01/07 – 12/23) span a long-term period of 24 years in total. Despite these different inclusion periods and thus, independent of the time of kidney transplantation, the model was successfully validated, demonstrating high predictive sensitivity and specificity. Since the model was developed using a northern German cohort and validated in a western German cohort, it is likely to be robust against center-specific biases. Therefore, it can be assumed that the model has general applicability across transplant centers in Germany. Finally, the prognostic model has the obvious advantage that all required parameters are available before the actual kidney transplant, with exception of the actual CIT, which can be estimated with sufficient accuracy during the allocation process. In conclusion, it should be feasible to easily implement the model into the clinical routine. In contrast to the findings of Zwirner et al., we were also able to successfully predict one-year eGFR category KDIGO G5. However, this result should be interpreted with caution, as only three patients in our cohort were classified as KDIGO G5. The loss of patients due to death or returning to dialysis may be increased within KDIGO category G5 compared to the other KDIGO categories and may be the reason for the relatively small sample size within this category. In this regard, further external validation, increasing the number of patients included with KDIGO G5 at one-year follow-up, would enhance the certainty of interpretation. Our study has certain limitations. Based on our study results, the prognostic model appears applicable within Germany; however, its validity in other countries participating in the ETKAS cannot be assumed without further international validation. Furthermore, we used KDIGO categories – an ordinal scale – as state variables in the ROC analysis. Consequently, the predictive accuracy is inferior to continuous variables, such as specific eGFR values. Nevertheless, previous predictive models only rely on nominal study endpoints, for example one-year graft loss (18). Importantly, we could predict each eGFR category with high sensitivity and specificity, as determined by the abovementioned AUROC values. Overall, using KDIGO categories offers practical advantages for clinical application, as the KDIGO guidelines are the most common classification system for chronic kidney disease and are widely accepted by nephrologists, transplant surgeons and other healthcare organizations across the globe (19). In the original study population by Zwirner et al., the eGFR was calculated using the MDRD formula (9). To ensure a maximum comparability, we also used the MDRD formula for eGFR calculation. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula was shown to be superior in accuracy compared to the MDRD formula (20). However, the majority of kidney transplant recipients (242/283=85.5%) in our cohort had an eGFR below the reported threshold of 60 mL/min/1.73 m 2 , above which the MDRD equation lacks accuracy (20). Therefore, the accuracy of the eGFR estimation in our study should be sufficiently precise. In the past, several prognostic scoring systems were developed to predict graft outcomes after deceased donor kidney transplantation. First published in 2009, the kidney donor risk index includes donor and transplant variables to estimate the relative risk of graft failure compared to a 40-year-old healthy donor (21). Its derivative, called kidney donor profile index and based on the same variables, translates this relative risk into percentile ranks, aiming to simplify the score for clinicians (22). However, experts have raised concerns that the predictive power of the kidney donor profile index is limited. In the original US-American publication, the average C-statistics were reported as 0.62 (21); a similar value was found in an external validation study in a European cohort (23). Thus, similar kidney donor profile indices cannot reliably predict outcome differences of specific kidney grafts. Additionally, since the kidney donor profile index only indicates the risk of graft failure relative to a healthy reference donor, it does not allow for predictions of graft survival at specific time points. This is an important limitation in comparison with the model by Zwirner et al., which does not predict nominal outcome parameters such as graft failure, but rather could predict KDIGO-based eGFR category as an ordinal variable at a specific time point (9). Since the one-year graft function was shown to predict long-term graft survival (7), the model by Zwirner et al. could serve as a surrogate parameter in this regard. The deceased donor score is a donor-derived score that calculates one-year graft function and six-year graft survival after deceased donor kidney transplantation (24, 25). It is more commonly used in Europe, particularly in Spain (25, 26) and the United Kingdom (27). While the deceased donor score incorporates the number of human leukocyte antigen mismatches into the analysis, further recipient variables, e.g. the recipient’s age or the number of previous kidney transplants, are not considered (24) – a known limitation of the score. In contrast, the prognostic model by Zwirner et al. takes recipient variables such as the recipient’s body mass index and the re-transplantations beyond the 2 nd KTx, (thereby functioning as a surrogate parameter for various immunological risk factors) as well as transplant-specific variables like CIT into account (9). The standard Eurotransplant kidney allocation system (applying for recipients <65 years old) prioritizes the immunological matching of donor and recipient as well as ethical considerations such as medical urgency and waiting time over the use of donor-derived scores (28). Thus, a suboptimal donor-recipient matching with respect to the predicted recipient’s graft and patient survival is considered a limitation, thereby potentially decreasing overall organ utility (29). Recently, Ernst et al. proposed a new scoring system to predict both delayed graft function (DGF) and one-year graft loss after brain-dead donor kidney transplantation (18). Both scores by Zwirner and Ernst were developed and validated in German and European cohorts, respectively, using only brain-dead kidney donors as donation after cardiac death is forbidden in Germany according to the German Transplantation Law (30). Nonetheless, there are some important differences when comparing the scores by Zwirner and Ernst. The number of patients included for the development of the prognostic model were n=1172 by Zwirner et al. and n=620 (for DGF) and n=711 (for one-year graft loss) by Ernst et al., respectively. The number of patients in the validation sets, n=158 (for DGF) and n=162 (for one year-graft loss) by Ernst et al. and n=283 used in this study, was different as well (18). The Cologne score has the advantage of a comparatively high discriminative power, with c-statistics (each for training and validation set, respectively) of 0.67 and 0.70 for DGF and 0.70 and 0.76 for one-year graft loss. C-statistics slightly improved after taking the results of optional nephropathology (step two of the score) into account. This is superior to the kidney donor profile index (21) and will be interesting to be assessed in future (validation) studies. Furthermore, the 2-Step score is an easy-to-use tool with optional histopathology providing the opportunity to the allocating clinician in otherwise unclear cases (18). The most important difference between the 2-Step-Score and the prognostic model by Zwirner et al. lies in the type of outcome variables they predict. The score by Ernst and colleagues estimates the risk of two nominal variables — specifically, delayed graft function and one-year graft loss — as major adverse events after kidney transplantation. In contrast, the now validated prognostic model by Zwirner et al. accurately predicts one-year eGFR category for a specific donor-recipient match, using eGFR categories as an ordinal variable (9). Taken together, the 2-Step-Score offers a promising tool for kidney allocation within the ETKAS and may be superior to established scores. The now validated score by Zwirner et al. may serve as a valuable addition for the nephrologist or transplant surgeon responsible for organ allocation by improving individual decision-making, as it allows to predict one-year eGFR categories in different recipients based on a specific donor graft. We successfully validated the prognostic model that provides an accurate estimate of the eGFR category one year after DBD kidney transplantation by using non-invasive and easy-to-obtain parameters all available before transplantation. This model cannot only enhance donor and recipient matching, thereby improving long-term graft survival, but it may also be useful in identifying recipients at risk for major adverse events prior to kidney transplantation. Such high-risk recipients could then be monitored more closely in follow-up care after kidney transplantation. Based on the results of our study, further validation studies in countries participating within the ETKAS are now warranted, before a broad application of the prognostic model within the ETKAS can become clinical reality. Conclusion In this study, we successfully validated the recently published prognostic model predicting KDIGO-graded eGFR categories one year after deceased donor kidney transplantation. Thus, the model should be implemented in clinical practice to optimize organ allocation, to improve donor-recipient matching, and ultimately enhance long-term graft survival. Abbreviations AUROC = Areas under the receiver operating curve characteristic, BMI = Body mass index, CIT = Cold ischemia time, CKD-EPI = Chronic kidney disease – epidemiology collaboration, DBD = Donation after brain death, DGF = Delayed graft function, eGFR = estimated glomerular filtration rate, ESRD = end-stage renal disease, ETKAS = Eurotransplant kidney allocation system, KDIGO = Kidney Disease: Improving Global Outcomes, KTx = Kidney transplantation, MDRD = Modification of diet in renal disease, ROC = Receiver operating characteristics, TRIPOD = Transparent Reporting of a 2 multivariable prediction model for Individual Prognosis or Diagnostics, UH-RWTH = University Hospital RWTH Aachen Declarations Ethics approval and consent to participate: According to the general policy of our institution, patients or their legal guardians provided informed consent that their data may be used for scientific purposes. The ethical committee at UH-RWTH stated that no further approval is needed. Patient data and records were anonymized and deidentified prior to analysis. Consent for publication: Not applicable. Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: This study did not receive any funding. Authors‘ contributions: P.T., U.Z. and H.S. designed the study. P.T. acquired the data. P.T. and U.Z. performed the statistical analysis. P.T., U.Z. and O.B. interpreted the data. P.T., U.Z. and O.B. wrote the main manuscript. C.A.W., F.A.M., W.G., A.M., R.K., B.A.W., D. K.-D., N.R., F.O., F.W.R.V., and H.S. critically revised the manuscript. All the authors approved the final manuscript for publication. Acknowledgements: The authors would like to thank the patients included in this study for providing their clinical data. 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Analysis of the Eurotransplant Kidney Allocation Algorithm: How Should We Balance Utility and Equity? Transplant Proc. 2018;50(10):3010-6. Gesetz über die Spende, Entnahme und Übertragung von Organen und Geweben (Transplantationsgesetz - TPG) 1997 [updated March 22nd, 2024. Available from: https://www.gesetze-im-internet.de/tpg/BJNR263100997.html. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7077428","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490468225,"identity":"753a29ad-30a3-4d32-9c08-76a3e7199911","order_by":0,"name":"Philipp Tessmer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDADfgY2EHUARBjgV8oGpSUbSNZicIBYLebyzYc//syxyTe+kZb44UfNHTlzBuaND/BpsWxjS5Pm3ZZmue1G2mHJnmPPjC0b2IrxWmNwjMeMmXHbYQOzG+kNErwNhxM3HOAxk8Cvhf/zx5/b/hsYz0hv/vm34XA9UIv5DwK2MEjwbjtgYCCRdkwaaEuCAdAWfDqAfkkzA/ol2UDizLM0a5ljhw03HGYrxuswc+bDj4EOszPgb08zvvmm5rC8wfHmjR/wOgxTiBmvswhG9CgYBaNgFIwCIAAA48BMvQF2lcEAAAAASUVORK5CYII=","orcid":"","institution":"University Hospital RWTH Aachen","correspondingAuthor":true,"prefix":"","firstName":"Philipp","middleName":"","lastName":"Tessmer","suffix":""},{"id":490468226,"identity":"f374ca9f-8824-4681-a553-86ebc977b8e6","order_by":1,"name":"Clara A. Weigle","email":"","orcid":"","institution":"University Hospital RWTH Aachen","correspondingAuthor":false,"prefix":"","firstName":"Clara","middleName":"A.","lastName":"Weigle","suffix":""},{"id":490468228,"identity":"1357b2db-2b88-47b9-8df9-e8586dbd0164","order_by":2,"name":"Franziska A. Meister","email":"","orcid":"","institution":"University Hospital RWTH Aachen","correspondingAuthor":false,"prefix":"","firstName":"Franziska","middleName":"A.","lastName":"Meister","suffix":""},{"id":490468230,"identity":"49d94bbd-07d5-4246-8b8f-54a6ddbce7df","order_by":3,"name":"Wilfried Gwinner","email":"","orcid":"","institution":"Hannover Medical School","correspondingAuthor":false,"prefix":"","firstName":"Wilfried","middleName":"","lastName":"Gwinner","suffix":""},{"id":490468232,"identity":"4a2bcf3c-4963-4eac-950b-ef51eb936e01","order_by":4,"name":"Anja Mühlfeld","email":"","orcid":"","institution":"University Hospital RWTH Aachen","correspondingAuthor":false,"prefix":"","firstName":"Anja","middleName":"","lastName":"Mühlfeld","suffix":""},{"id":490468234,"identity":"a1216289-ac15-45d2-8fcf-7743a16ee40e","order_by":5,"name":"Rafael Kramann","email":"","orcid":"","institution":"University Hospital RWTH Aachen","correspondingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"","lastName":"Kramann","suffix":""},{"id":490468236,"identity":"d2865546-241d-48e2-a352-6a42bd6f2a84","order_by":6,"name":"Bengt A. Wiemann","email":"","orcid":"","institution":"Hannover Medical School","correspondingAuthor":false,"prefix":"","firstName":"Bengt","middleName":"A.","lastName":"Wiemann","suffix":""},{"id":490468238,"identity":"75e8879b-bac9-4bad-8847-6d23e8c30116","order_by":7,"name":"Dennis Kleine-Döpke","email":"","orcid":"","institution":"Hannover Medical School","correspondingAuthor":false,"prefix":"","firstName":"Dennis","middleName":"","lastName":"Kleine-Döpke","suffix":""},{"id":490468239,"identity":"b71ecd2c-34ee-4d25-bfe6-1195acf01efb","order_by":8,"name":"Nicolas Richter","email":"","orcid":"","institution":"Hannover Medical School","correspondingAuthor":false,"prefix":"","firstName":"Nicolas","middleName":"","lastName":"Richter","suffix":""},{"id":490468240,"identity":"d2ea9b88-6dfb-47ec-8283-ef9398fba614","order_by":9,"name":"Felix Oldhafer","email":"","orcid":"","institution":"University Hospital RWTH Aachen","correspondingAuthor":false,"prefix":"","firstName":"Felix","middleName":"","lastName":"Oldhafer","suffix":""},{"id":490468241,"identity":"a0479906-57a5-4c19-9457-66eaf9ffea2f","order_by":10,"name":"Florian W. R. Vondran","email":"","orcid":"","institution":"University Hospital RWTH Aachen","correspondingAuthor":false,"prefix":"","firstName":"Florian","middleName":"W. R.","lastName":"Vondran","suffix":""},{"id":490468242,"identity":"aaa1d788-50e0-422d-af80-655ceca0cdd7","order_by":11,"name":"Harald Schrem","email":"","orcid":"","institution":"Klinikum Chemnitz","correspondingAuthor":false,"prefix":"","firstName":"Harald","middleName":"","lastName":"Schrem","suffix":""},{"id":490468243,"identity":"1f51c34c-9cfb-4802-93eb-c66b67c3d865","order_by":12,"name":"Oliver Beetz","email":"","orcid":"","institution":"University Hospital RWTH Aachen","correspondingAuthor":false,"prefix":"","firstName":"Oliver","middleName":"","lastName":"Beetz","suffix":""},{"id":490468244,"identity":"86e42084-d914-4425-bb3b-3b3edb2c50b0","order_by":13,"name":"Ulrich Zwirner","email":"","orcid":"","institution":"Hannover Medical School","correspondingAuthor":false,"prefix":"","firstName":"Ulrich","middleName":"","lastName":"Zwirner","suffix":""}],"badges":[],"createdAt":"2025-07-08 18:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7077428/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7077428/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87586973,"identity":"39687be8-d08a-45f9-af40-d89b27955dc5","added_by":"auto","created_at":"2025-07-25 14:04:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42240,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flow chart. Overview of excluded and included patients for later analysis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7077428/v1/6367eb07f0476793e855c018.png"},{"id":87586978,"identity":"ac993316-64f8-4d50-8743-493deed50882","added_by":"auto","created_at":"2025-07-25 14:04:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":144988,"visible":true,"origin":"","legend":"\u003cp\u003eExtern\u003cstrong\u003e \u003c/strong\u003eperformance of the proposed multivariable regression model to predict eGFR categories (according to the KDIGO classification) one year after deceased donor kidney transplantation. For each eGFR category, the corresponding area under the receiver operating curve (AUROC) is shown. KDIGO G1 was set as reference category. AUROC \u0026gt;0.7 indicates a high sensitivity and specificity of prediction. The figure was created with JMP Pro 13 software (SAS Institute, Cary, NC, USA) and optimized for publication using Adobe Photoshop CS4 V11 (Adobe Systems Incorp., San Jose, CA, USA).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7077428/v1/d946d0ad95486e33ff53a6ad.png"},{"id":88326871,"identity":"5182fcbc-827b-473b-bf21-644e7145f0cc","added_by":"auto","created_at":"2025-08-05 09:54:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":678792,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7077428/v1/aa612f60-3b19-4bf0-886c-fe6d1a8529a2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"External validation of a prognostic model predicting renal graft function one year after brain-dead donor kidney transplantation","fulltext":[{"header":"Background","content":"\u003cp\u003eKidney transplantation (KTx) is the treatment of choice for end-stage renal disease (ESRD), as transplant recipients benefit from superior patient survival (1–3) and improved quality of life (4) compared with dialysis patients. In Germany, there is a large disparity between organ demand and supply resulting in median waiting times of approximately nine years within the Eurotransplant kidney allocation system (ETKAS) (5). Medical factors are of paramount importance when selecting an optimal kidney transplant recipient for a specific donor organ to ensure adequate early organ function (6). One-year creatinine levels (7) and one-year estimated glomerular filtration rate (eGFR) (8) are suitable indicators for long-term graft survival. Zwirner et al. recently published a prognostic model predicting eGFR categories according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines one year after deceased donor kidney transplantation, relying solely on variables available before transplantation (9). The training data set consisted of 1.172 kidney transplants from donation after brain death (DBD) donors between January 2000 and December 2012 (9). If this model is valid to external cohorts, it could be suitable to predict KDIGO-based eGFR categories one year after kidney transplantation, thereby serving as an easy-to-apply prognostic tool for the prediction of long-term graft survival, without the need for histological analyses via pre-transplant biopsies. The model could be used to optimize kidney allocation, improve donor-recipient matching and thus to decrease the need for re-transplantation after failure of the first graft. The aim of this study was to externally validate the proposed prognostic model in our cohort of deceased donor kidney transplantations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design\u003c/p\u003e\n\u003cp\u003eThis external validation study was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnostics (TRIPOD) guidelines (10). We retrospectively analyzed clinical data from all patients undergoing donation after brain death kidney transplantation at the University Hospital RWTH Aachen (UH-RWTH) between January 2007 and December 2023. A clinical trial number was not applicable. Clinical data included donors\u0026rsquo; and recipients\u0026rsquo; baseline characteristics, laboratory parameters and transplant data. The parameters that were identified as significant in the multivariate logistic regression model of the aforementioned publication (9) were used as test variables in Receiver Operating Characteristic (ROC) analyses. The parameters included were donor age, last donor serum creatinine, recipient body mass index, re-transplantation \u0026gt; 2\u003csup\u003end\u003c/sup\u003e kidney transplantation, and cold ischemia time (CIT). Follow-up was one year after kidney transplantation (\u0026plusmn;3 months). Patients who were on dialysis at the time of follow-up were excluded from the analysis to ensure compatibility with the original study cohort. Patients with living kidney donation or simultaneous liver or pancreas transplantation were also excluded. The included patients are shown in a study flow chart (see Figure 1). In the first year after kidney transplantation, all patients received a standard triple immunosuppressive regimen.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudy endpoints\u003c/p\u003e\n\u003cp\u003eThe study endpoint used for the validation of the prognostic model published by Zwirner et al. (9) was the recipient\u0026rsquo;s graft function according to KDIGO-based eGFR categories one year after kidney transplantation (\u0026plusmn;3 months).\u003c/p\u003e\n\u003cp\u003eDefinition of variables\u003c/p\u003e\n\u003cp\u003eAs in the original publication (9), the glomerular filtration rate of the kidney transplant was estimated using the 4-variable Modification of Diet in Renal Disease (MDRD) formula as published by Levey et al. (11). eGFR values were categorized using the KDIGO guidelines (12). The degree of albuminuria was not taken into account as a factor for categorization of renal function.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSurgical procedure\u003c/p\u003e\n\u003cp\u003eIn most cases, heterotopic graft implementation to the right or left iliac fossa was performed. Arterial and venous anastomoses were preferably applied to the common and external iliac artery and vein, respectively. In most cases, modified Lich-Gregoire (13, 14) ureteroneocystostomy was performed, with temporal stenting of the graft ureter.\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were conducted using SPSS statistical software (SPSS\u0026reg; Statistics 29.0.1.0, IBM\u0026reg; Corporation). ROC analyses were performed with JMP Pro 13 software (SAS Institute, Cary, NC, USA). Statistical significance was set at a two-sided \u003cem\u003ep\u003c/em\u003e-value \u0026lt;0.050. Areas under the receiver operating curve characteristic (AUROC) analyses were used to test the performance and external validity of the prognostic model proposed by Zwirner et al. (9) to predict each eGFR category one year after transplantation in our cohort of DBD kidney transplantations. A normal kidney function (KDIGO category G1) was set as reference category, indicating unimpaired renal graft function.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Board of UH-RWTH waived informed consent due to the collection of routine clinical data and the retrospective study design. Prior to analysis, patient data and records were anonymized and de-identified. All methods were carried out in accordance with relevant guidelines and regulations under ethical declarations.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive statistics\u003c/p\u003e\n\u003cp\u003eIn the observation period, 494 kidney transplants were performed at UH-RWTH. 144 (29.1%) grafts were from living and 350 (70.9%) from deceased donors. Of the 350 deceased donor kidney transplants, 67 were excluded for various reasons: returning to dialysis before reaching the study endpoint (n=29; 43.3%), patient death (n=23; 34.3%), simultaneous liver transplantation (n=13; 19.4%) and loss to follow-up (n=2; 3.0%). Thus, the eGFR of 283 deceased donor kidney transplant recipients at one-year follow up was finally analyzed. Figure 1 gives an overview of the included patients.\u003c/p\u003e\n\u003cp\u003eOf the study group, most recipients were male (n=190; 67.1%) with a median age of 57 (22-80) years and a median body mass index (BMI) of 25 (15-52) kg/m\u003csup\u003e2\u003c/sup\u003e, respectively. The median donor age and the donor\u0026rsquo;s last serum creatinine was 57 (8-88) years and 88 (25-650) \u0026micro;mol/L, respectively. The median eGFR one year after transplantation was 42 [12-94] mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e. One-year eGFR was categorized as KDIGO G1, G2, G3a, G3b, G4, and G5 in two (0.7%), 39 (13.8%), 70 (24.7%), 107 (37.8%), 62 (21.9%), and three (1.1%) cases, respectively. Most patients received their first or second graft (n=279; 98.6%). Only four (1.4%) patients were third graft recipients. In our cohort, no fourth or fifth kidney transplants were performed. The median cold ischemia time was 743 (54-1760) minutes. Table 1 shows the patient\u0026rsquo;s descriptive statistics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDescriptive statistics\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"376\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eKidney transplants, n\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eDeceased donor, n [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e350 [70.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eLiving donor, n [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e144 [29.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cu\u003eStudy group\u003c/u\u003e, n [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e283 [57.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eRec Age, median [range]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e57 [22-80]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eRec Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003emale, n [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e190 [67.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003efemale, n [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e93 [32.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRec BMI\u003c/strong\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e), median [range]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e25 [12-52]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003eRec 1-year-eGFR (ml/min/1,73 m\u0026sup2;), median [range]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e42 [12-94]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDon age\u003c/strong\u003e, median [range]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e57 [8-88]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDon last serum creatinine\u003c/strong\u003e (\u0026micro;mol/l), median [range]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e88 [25-650]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCIT\u0026nbsp;\u003c/strong\u003e(min), median [range]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e743 [54-1760]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 236px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;2\u003csup\u003end\u003c/sup\u003e KTx, n [%]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e4 [1.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBaseline characteristics of n=283 deceased donor kidney transplant recipients. Parameter that were identified as statistically significant in the multivariate analysis by Zwirner et al. are marked in bold. These parameters were then used as test variables in receiver operating characteristic analyses. BMI = Body mass index, CIT = Cold ischemia time, Don = Donor, eGFR = estimated glomerular filtration rate; KTx = Kidney transplant, Rec = Recipient.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUROC analysis\u003c/p\u003e\n\u003cp\u003eUsing the proposed prognostic model by Zwirner et al., the AUROC values for KDIGO categories G2, G3a, G3b, G4, and G5 were 0.8664, 0.7167, 0.7442, 0.7315, and 0.7331, respectively, indicating a high sensitivity and specificity of prediction for each eGFR category. Table 2 classifies the patients\u0026rsquo; one-year eGFR according to the KDIGO classification. Figure 2 shows the corresponding ROC curves for each eGFR category.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e KDIGO classification\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" title=\"Table XX cfcff\" width=\"376\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKDIGO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 310px;\"\u003e\n \u003cp\u003eNumber of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e, n [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 310px;\"\u003e\n \u003cp\u003e2 [0.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG2\u003c/strong\u003e, n [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 310px;\"\u003e\n \u003cp\u003e39 [13.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3a\u003c/strong\u003e, n [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 310px;\"\u003e\n \u003cp\u003e70 [24.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3b\u003c/strong\u003e, n [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 310px;\"\u003e\n \u003cp\u003e107 [37.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG4\u003c/strong\u003e, n [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 310px;\"\u003e\n \u003cp\u003e62 [21.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG5\u003c/strong\u003e, n [%]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 310px;\"\u003e\n \u003cp\u003e3 [1.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOne-year eGFR after deceased donor kidney transplantation was categorized according to the Kidney Disease: Improving Global Outcomes Guidelines (KDIGO) stades G1 to G5, n=283.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to evaluate the external validity and performance of a recently published prognostic model predicting KDIGO-graded renal graft function one year after DBD kidney transplantation. For reliable prognostic models, areas under the ROC curves typically range from 0.6 to 0.85 (15), with values above 0.700 generally considered to reflect high predictive sensitivity and specificity (16). As the main finding of our study, we were able to demonstrate AUROC values above 0.700 for each KDIGO-based eGFR category in our cohort of deceased donor kidney transplant recipients. To the best of our knowledge, this is the first study to provide external validation of a prognostic model capable of reliably predicting eGFR categories, according to the KDIGO classification, one year after DBD kidney transplantation. To date, a comparable model has only been published for the prediction of pancreas and kidney function following simultaneous organ transplantation (17).\u003c/p\u003e\n\u003cp\u003eOur prognostic model offers several advantages. In total, 1172 kidney transplant recipients from the original study cohort and 283 patients from our cohort were included, resulting in an overall sample size of 1455 patients for the development and validation of the prognostic model. In addition to this comparatively large sample size, the inclusion periods of the original cohort (01/00 \u0026ndash; 12/12) and our cohort (01/07 \u0026ndash; 12/23) span a long-term period of 24 years in total. \u0026nbsp;Despite these different inclusion periods and thus, independent of the time of kidney transplantation, the model was successfully validated, demonstrating high predictive sensitivity and specificity. Since the model was developed using a northern German cohort and validated in a western German cohort, it is likely to be robust against center-specific biases. Therefore, it can be assumed that the model has general applicability across transplant centers in Germany. Finally, the prognostic model has the obvious advantage that all required parameters are available before the actual kidney transplant, with exception of the actual CIT, which can be estimated with sufficient accuracy during the allocation process. In conclusion, it should be feasible to easily implement the model into the clinical routine.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast to the findings of Zwirner et al., we were also able to successfully predict one-year eGFR category KDIGO G5. \u0026nbsp;However, this result should be interpreted with caution, as only three patients in our cohort were classified as KDIGO G5. The loss of patients due to death or returning to dialysis may be increased within KDIGO category G5 compared to the other KDIGO categories and may be the reason for the relatively small sample size within this category. In this regard, further external validation, increasing the number of patients included with KDIGO G5 at one-year follow-up, would enhance the certainty of interpretation.\u003c/p\u003e\n\u003cp\u003eOur study has certain limitations. Based on our study results, the prognostic model appears applicable within Germany; however, its validity in other countries participating in the ETKAS cannot be assumed without further international validation. Furthermore, we used KDIGO categories \u0026ndash; an ordinal scale \u0026ndash; as state variables in the ROC analysis. Consequently, the predictive accuracy is inferior to continuous variables, such as specific eGFR values. Nevertheless, previous predictive models only rely on nominal study endpoints, for example one-year graft loss (18). Importantly, we could predict each eGFR category with high sensitivity and specificity, as determined by the abovementioned AUROC values. Overall, using KDIGO categories offers practical advantages for clinical application, as the KDIGO guidelines are the most common classification system for chronic kidney disease and are widely accepted by nephrologists, transplant surgeons and other healthcare organizations across the globe (19). In the original study population by Zwirner et al., the eGFR was calculated using the MDRD formula (9). To ensure a maximum comparability, we also used the MDRD formula for eGFR calculation. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula was shown to be superior in accuracy compared to the MDRD formula (20). However, the majority of kidney transplant recipients (242/283=85.5%) in our cohort had an eGFR below the reported threshold of 60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, above which the MDRD equation lacks accuracy (20). Therefore, the accuracy of the eGFR estimation in our study should be sufficiently precise.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the past, several prognostic scoring systems were developed to predict graft outcomes after deceased donor kidney transplantation. First published in 2009, the kidney donor risk index includes donor and transplant variables to estimate the relative risk of graft failure compared to a 40-year-old healthy donor (21). \u0026nbsp;Its derivative, called kidney donor profile index and based on the same variables, translates this relative risk into percentile ranks, aiming to simplify the score for clinicians (22). However, experts have raised concerns that the predictive power of the kidney donor profile index is limited. In the original US-American publication, the average C-statistics were reported as 0.62 (21); a similar value was found in an external validation study in a European cohort (23). Thus, similar kidney donor profile indices cannot reliably predict outcome differences of specific kidney grafts. Additionally, since the kidney donor profile index only indicates the risk of graft failure relative to a healthy reference donor, it does not allow for predictions of graft survival at specific time points. This is an important limitation in comparison with the model by Zwirner et al., which does not predict nominal outcome parameters such as graft failure, but rather could predict KDIGO-based eGFR category as an ordinal variable at a specific time point (9). Since the one-year graft function was shown to predict long-term graft survival (7), the model by Zwirner et al. could serve as a surrogate parameter in this regard.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe deceased donor score is a donor-derived score that calculates one-year graft function and six-year graft survival after deceased donor kidney transplantation (24, 25). It is more commonly used in Europe, particularly in Spain (25, 26) and the United Kingdom (27). While the deceased donor score incorporates the number of human leukocyte antigen mismatches into the analysis, further recipient variables, e.g. the recipient\u0026rsquo;s age or the number of previous kidney transplants, are not considered (24) \u0026ndash; a known limitation of the score. In contrast, the prognostic model by Zwirner et al. takes recipient variables such as the recipient\u0026rsquo;s body mass index and the re-transplantations beyond the 2\u003csup\u003end\u003c/sup\u003e KTx, (thereby functioning as a surrogate parameter for various immunological risk factors) as well as transplant-specific variables like CIT into account (9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe standard Eurotransplant kidney allocation system (applying for recipients \u0026lt;65 years old) prioritizes the immunological matching of donor and recipient as well as ethical considerations such as medical urgency and waiting time over the use of donor-derived scores (28). Thus, a suboptimal donor-recipient matching with respect to the predicted recipient\u0026rsquo;s graft and patient survival is considered a limitation, thereby potentially decreasing overall organ utility (29).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecently, Ernst et al. proposed a new scoring system to predict both delayed graft function (DGF) and one-year graft loss after brain-dead donor kidney transplantation (18). \u0026nbsp;Both scores by Zwirner and Ernst were developed and validated in German and European cohorts, respectively, using only brain-dead kidney donors as donation after cardiac death is forbidden in Germany according to the German Transplantation Law (30). Nonetheless, there are some important differences when comparing the scores by Zwirner and Ernst. The number of patients included for the development of the prognostic model were n=1172 by Zwirner et al. and n=620 (for DGF) and n=711 (for one-year graft loss) by Ernst et al., respectively. The number of patients in the validation sets, n=158 (for DGF) and n=162 (for one year-graft loss) by Ernst et al. and n=283 used in this study, was different as well (18). The Cologne score has the advantage of a comparatively high discriminative power, with c-statistics (each for training and validation set, respectively) of 0.67 and 0.70 for DGF and 0.70 and 0.76 for one-year graft loss. C-statistics slightly improved after taking the results of optional nephropathology (step two of the score) into account. This is superior to the kidney donor profile index (21) and will be interesting to be assessed in future (validation) studies. Furthermore, the 2-Step score is an easy-to-use tool with optional histopathology providing the opportunity to the allocating clinician in otherwise unclear cases (18). The most important difference between the 2-Step-Score and the prognostic model by Zwirner et al. lies in the type of outcome variables they predict. The score by Ernst and colleagues estimates the risk of two nominal variables \u0026mdash; specifically, delayed graft function and one-year graft loss \u0026mdash; as major adverse events after kidney transplantation. In contrast, the now validated prognostic model by Zwirner et al. accurately predicts one-year eGFR category for a specific donor-recipient match, using eGFR categories as an ordinal variable (9). Taken together, the 2-Step-Score offers a promising tool for kidney allocation within the ETKAS and may be superior to established scores. The now validated score by Zwirner et al. may serve as a valuable addition for the nephrologist or transplant surgeon responsible for organ allocation by improving individual decision-making, as it allows to predict one-year eGFR categories in different recipients based on a specific donor graft.\u003c/p\u003e\n\u003cp\u003eWe successfully validated the prognostic model that provides an accurate estimate of the eGFR category one year after DBD kidney transplantation by using non-invasive and easy-to-obtain parameters all available before transplantation. This model cannot only enhance donor and recipient matching, thereby improving long-term graft survival, but it may also be useful in identifying recipients at risk for major adverse events prior to kidney transplantation. Such high-risk recipients could then be monitored more closely in follow-up care after kidney transplantation. Based on the results of our study, further validation studies in countries participating within the ETKAS are now warranted, before a broad application of the prognostic model within the ETKAS can become clinical reality.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we successfully validated the recently published prognostic model predicting KDIGO-graded eGFR categories one year after deceased donor kidney transplantation. Thus, the model should be implemented in clinical practice to optimize organ allocation, to improve donor-recipient matching, and ultimately enhance long-term graft survival.\u0026nbsp;\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUROC = Areas under the receiver operating curve characteristic, BMI = Body mass index, CIT = Cold ischemia time, CKD-EPI = Chronic kidney disease \u0026ndash; epidemiology collaboration, DBD = Donation after brain death, DGF = Delayed graft function, \u0026nbsp;eGFR = estimated glomerular filtration rate, ESRD = end-stage renal disease, ETKAS = Eurotransplant kidney allocation system, KDIGO = Kidney Disease: Improving Global Outcomes, KTx = Kidney transplantation, MDRD = Modification of diet in renal disease, ROC = Receiver operating characteristics, TRIPOD = Transparent Reporting of a 2 multivariable prediction model for Individual Prognosis or Diagnostics, UH-RWTH = University Hospital RWTH Aachen\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the general policy of our institution, patients or their legal guardians provided informed consent that their data may be used for scientific purposes. The ethical committee at UH-RWTH stated that no further approval is needed. Patient data and records were anonymized and deidentified prior to analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026lsquo; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eP.T., U.Z. and H.S. designed the study. P.T. acquired the data. P.T. and U.Z. performed the statistical analysis. P.T., U.Z. and O.B. interpreted the data. P.T., U.Z. and O.B. wrote the main manuscript. C.A.W., F.A.M., W.G., A.M., R.K., B.A.W., D. K.-D., N.R., F.O., F.W.R.V., and H.S. critically revised the manuscript. All the authors approved the final manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the patients included in this study for providing their clinical data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKaballo MA, Canney M, O\u0026rsquo;Kelly P, Williams Y, O\u0026rsquo;Seaghdha CM, Conlon PJ. A comparative analysis of survival of patients on dialysis and after kidney transplantation. Clinical Kidney Journal. 2018;11(3):389-93.\u003c/li\u003e\n\u003cli\u003eWolfe RA, Ashby VB, Milford EL, Ojo AO, Ettenger RE, Agodoa LY, et al. Comparison of mortality in all patients on dialysis, patients on dialysis awaiting transplantation, and recipients of a first cadaveric transplant. N Engl J Med. 1999;341(23):1725-30.\u003c/li\u003e\n\u003cli\u003ePort FK. Comparison of Survival Probabilities for Dialysis Patients vs Cadaveric Renal Transplant Recipients. JAMA: The Journal of the American Medical Association. 1993;270(11).\u003c/li\u003e\n\u003cli\u003eTonelli M, Wiebe N, Knoll G, Bello A, Browne S, Jadhav D, et al. Systematic review: kidney transplantation compared with dialysis in clinically relevant outcomes. Am J Transplant. 2011;11(10):2093-109.\u003c/li\u003e\n\u003cli\u003eZecher D, Tieken I, Wadewitz J, Zeman F, Rahmel A, Banas B. Regional Differences in Waiting Times Before Kidney Transplantation in Germany. Dtsch Arztebl Int. 2023(Forthcoming).\u003c/li\u003e\n\u003cli\u003eIrish WD, McCollum DA, Tesi RJ, Owen AB, Brennan DC, Bailly JE, et al. Nomogram for predicting the likelihood of delayed graft function in adult cadaveric renal transplant recipients. J Am Soc Nephrol. 2003;14(11):2967-74.\u003c/li\u003e\n\u003cli\u003eHariharan S, McBride MA, Cherikh WS, Tolleris CB, Bresnahan BA, Johnson CP. Post-transplant renal function in the first year predicts long-term kidney transplant survival. Kidney Int. 2002;62(1):311-8.\u003c/li\u003e\n\u003cli\u003eBaek CH, Kim H, Yang WS, Han DJ, Park SK. A postoperative 1-Year eGFR of More Than 45 ml/min May be the Cutoff Level for a Favorable Long-Term Prognosis in Renal Transplant Patients. Ann Transplant. 2016;21:439-47.\u003c/li\u003e\n\u003cli\u003eZwirner U, Kleine-Dopke D, Wagner A, Storzer S, Gronau F, Beetz O, et al. Prediction of Renal Graft Function 1 Year After Adult Deceased-Donor Kidney Transplantation Using Variables Available Prior to Transplantation. Ann Transplant. 2024;29:e944603.\u003c/li\u003e\n\u003cli\u003eCollins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD). Ann Intern Med. 2015;162(10):735-6.\u003c/li\u003e\n\u003cli\u003eLevey AS, Coresh J, Greene T, Marsh J, Stevens LA, Kusek JW, et al. Expressing the Modification of Diet in Renal Disease Study equation for estimating glomerular filtration rate with standardized serum creatinine values. Clin Chem. 2007;53(4):766-72.\u003c/li\u003e\n\u003cli\u003eStevens PE, Levin A, Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group M. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013;158(11):825-30.\u003c/li\u003e\n\u003cli\u003eLich R, Jr., Howerton LW, Davis LA. Childhood urosepsis. J Ky Med Assoc. 1961;59:1177-9.\u003c/li\u003e\n\u003cli\u003eGregoir W. [Congenital vesico-ureteral reflux]. Acta Urol Belg. 1962;30:286-300.\u003c/li\u003e\n\u003cli\u003eRoyston P, Moons KG, Altman DG, Vergouwe Y. Prognosis and prognostic research: Developing a prognostic model. BMJ. 2009;338:b604.\u003c/li\u003e\n\u003cli\u003eHanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29-36.\u003c/li\u003e\n\u003cli\u003eZorn KS, Littbarski S, Schwager Y, Kaltenborn A, Beneke J, Gwiasda J, et al. Development and validation of a prognostic model for kidney function 1 year after combined pancreas and kidney transplantation using pre-transplant donor and recipient variables. Langenbecks Arch Surg. 2018;403(7):837-49.\u003c/li\u003e\n\u003cli\u003eErnst A, Regele H, Chatzikyrkou C, Dendooven A, Turkevi-Nagy S, Tieken I, et al. 2-Step Scores with optional nephropathology for the prediction of adverse outcomes for brain-dead donor kidneys in Eurotransplant. Nephrol Dial Transplant. 2024;40(1):83-108.\u003c/li\u003e\n\u003cli\u003eLevey AS, Eckardt KU, Tsukamoto Y, Levin A, Coresh J, Rossert J, et al. Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney Int. 2005;67(6):2089-100.\u003c/li\u003e\n\u003cli\u003eStevens LA, Schmid CH, Greene T, Zhang YL, Beck GJ, Froissart M, et al. Comparative performance of the CKD Epidemiology Collaboration (CKD-EPI) and the Modification of Diet in Renal Disease (MDRD) Study equations for estimating GFR levels above 60 mL/min/1.73 m2. Am J Kidney Dis. 2010;56(3):486-95.\u003c/li\u003e\n\u003cli\u003eRao PS, Schaubel DE, Guidinger MK, Andreoni KA, Wolfe RA, Merion RM, et al. A comprehensive risk quantification score for deceased donor kidneys: the kidney donor risk index. Transplantation. 2009;88(2):231-6.\u003c/li\u003e\n\u003cli\u003eA Guide to Calculating and Interpreting the Kidney Donor Profle Index (KDPI) 2020 [updated March 23rd, 2020. Available from: https://optn.transplant.hrsa.gov/media/1512/guide_to_calculating_interpreting_kdpi.pdf.\u003c/li\u003e\n\u003cli\u003eLehner LJ, Kleinsteuber A, Halleck F, Khadzhynov D, Schrezenmeier E, Duerr M, et al. Assessment of the Kidney Donor Profile Index in a European cohort. Nephrol Dial Transplant. 2018;33(8):1465-72.\u003c/li\u003e\n\u003cli\u003eNyberg SL, Matas AJ, Kremers WK, Thostenson JD, Larson TS, Prieto M, et al. Improved scoring system to assess adult donors for cadaver renal transplantation. Am J Transplant. 2003;3(6):715-21.\u003c/li\u003e\n\u003cli\u003eNyberg SL, Baskin-Bey ES, Kremers W, Prieto M, Henry ML, Stegall MD. Improving the prediction of donor kidney quality: deceased donor score and resistive indices. Transplantation. 2005;80(7):925-9.\u003c/li\u003e\n\u003cli\u003eArnau A, Rodrigo E, Minambres E, Ruiz JC, Ballesteros MA, Pinera C, et al. Prediction of kidney transplant outcome by donor quality scoring systems: expanded criteria donor and deceased donor score. Transplant Proc. 2012;44(9):2555-7.\u003c/li\u003e\n\u003cli\u003ePlata-Munoz JJ, Vazquez-Montes M, Friend PJ, Fuggle SV. The deceased donor score system in kidney transplants from deceased donors after cardiac death. Transpl Int. 2010;23(2):131-9.\u003c/li\u003e\n\u003cli\u003eMayer G, Persijn GG. Eurotransplant kidney allocation system (ETKAS): rationale and implementation. Nephrol Dial Transplant. 2006;21(1):2-3.\u003c/li\u003e\n\u003cli\u003eSchulte K, Klasen V, Vollmer C, Borzikowsky C, Kunzendorf U, Feldkamp T. Analysis of the Eurotransplant Kidney Allocation Algorithm: How Should We Balance Utility and Equity? Transplant Proc. 2018;50(10):3010-6.\u003c/li\u003e\n\u003cli\u003eGesetz \u0026uuml;ber die Spende, Entnahme und \u0026Uuml;bertragung von Organen und Geweben (Transplantationsgesetz - TPG) 1997 [updated March 22nd, 2024. Available from: https://www.gesetze-im-internet.de/tpg/BJNR263100997.html.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Deceased donor kidney transplantation, External validation, Renal graft function, Kidney transplantation, Prognostic model","lastPublishedDoi":"10.21203/rs.3.rs-7077428/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7077428/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA large German transplant center recently published a prognostic model predicting renal graft function one year after deceased donor kidney transplantation relying solely on pre-transplant variables. The aim of this study is to externally validate this prognostic model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this external validation cohort, we retrospectively analyzed clinical data from deceased donor kidney transplant recipients undergoing kidney transplantation between January 2007 and December 2023 at University Hospital RWTH Aachen. Receiver operating characteristics (ROC) curves were analyzed to validate the abovementioned prognostic model based on donor age, donor serum creatinine, recipient body mass index, re-transplantation \u0026gt; 2\u003csup\u003end\u003c/sup\u003e kidney transplant, and cold ischemia time. Glomerular filtration rate levels were categorized using the Kidney Disease: Improving Global Outcomes (KDIGO) stages G1 – G5 with KDIGO G1 as reference category.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the observation period, a total of 494 kidney transplants were performed at our institution, 350 (70.9%) thereof from donation after brain death (DBD). The median one-year estimated glomerular filtration (eGFR) was 42 [12-94] mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e. The areas under the receiver operating characteristics curve for KDIGO categories G2, G3a, G3b, G4, and G5 were 0.8664, 0.7167, 0.7442, 0.7315, and 0.7331, respectively, thus indicating a high sensitivity and specificity of prediction for all stages of renal graft function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the proposed prognostic model, we could successfully predict each eGFR category in our external cohort of deceased donor kidney transplant recipients one year after transplantation. Thus, we consider the prognostic model as highly valuable. It could be used in clinical routine to facilitate kidney allocation, optimize donor-recipient matching, and enhance long-term graft survival.\u003c/p\u003e","manuscriptTitle":"External validation of a prognostic model predicting renal graft function one year after brain-dead donor kidney transplantation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 14:04:36","doi":"10.21203/rs.3.rs-7077428/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"87504a64-31f6-43c8-b20a-84518e2bb124","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-05T09:53:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 14:04:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7077428","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7077428","identity":"rs-7077428","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
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