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Quinino RM, Agena F, Modelli de Andrade LG, Furtado M, Chiavegatto Filho ADP, David-Neto E. A Machine Learning Prediction Model for Immediate Graft Function After Deceased Donor Kidney Transplantation. Transplantation 2023; 107:1380-1389. [PMID: 36872507 DOI: 10.1097/tp.0000000000004510] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
BACKGROUND After kidney transplantation (KTx), the graft can evolve from excellent immediate graft function (IGF) to total absence of function requiring dialysis. Recipients with IGF do not seem to benefit from using machine perfusion, an expensive procedure, in the long term when compared with cold storage. This study proposes to develop a prediction model for IGF in KTx deceased donor patients using machine learning algorithms. METHODS Unsensitized recipients who received their first KTx deceased donor between January 1, 2010, and December 31, 2019, were classified according to the conduct of renal function after transplantation. Variables related to the donor, recipient, kidney preservation, and immunology were used. The patients were randomly divided into 2 groups: 70% were assigned to the training and 30% to the test group. Popular machine learning algorithms were used: eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Gradient Boosting classifier, Logistic Regression, CatBoost classifier, AdaBoost classifier, and Random Forest classifier. Comparative performance analysis on the test dataset was performed using the results of the AUC values, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. RESULTS Of the 859 patients, 21.7% (n = 186) had IGF. The best predictive performance resulted from the eXtreme Gradient Boosting model (AUC, 0.78; 95% CI, 0.71-0.84; sensitivity, 0.64; specificity, 0.78). Five variables with the highest predictive value were identified. CONCLUSIONS Our results indicated the possibility of creating a model for the prediction of IGF, enhancing the selection of patients who would benefit from an expensive treatment, as in the case of machine perfusion preservation.
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Affiliation(s)
- Raquel M Quinino
- Renal Transplant Service, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | - Fabiana Agena
- Renal Transplant Service, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
| | | | - Mariane Furtado
- Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo, Brazil
| | | | - Elias David-Neto
- Renal Transplant Service, Hospital das Clinicas, University of São Paulo School of Medicine, São Paulo, Brazil
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Ietto G, Guzzetti L, Baglieri CS, Raveglia V, Zani E, Benedetti F, Parise C, Iori V, Franchi C, Masci F, Vigezzi A, Ferri E, Iovino D, Liepa L, Brusa D, Oltolina M, Gritti M, Ripamonti M, Gasperina DD, Ambrosini A, Amico F, Saverio SD, Soldini G, Latham L, Tozzi M, Carcano G. Predictive Models for the Functional Recovery of Transplanted Kidney. Transplant Proc 2021; 53:2873-2878. [PMID: 34728075 DOI: 10.1016/j.transproceed.2021.08.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 08/04/2021] [Accepted: 08/30/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND Renal transplantation is the gold standard treatment for end-stage renal disease, however, in 20% of cases, the graft develops a delayed graft function (DGF) that is associated with both early and late worsening of the outcome. The aim of this study was to examine and validate in a population of transplanted patients the appropriateness of the predictive score systems of DGF available to identify patients who might take advantage of a tailored immunosuppressive therapy. MATERIALS AND METHODS We conducted a systematic review of the literature to identify articles concerning scoring systems predicting DGF to identify those applicable to the study population and subsequently comparing their appropriateness for defining the most accurate one. RESULTS From an analysis of the scientific literature, we found 7 scoring systems predicting DGF. Of these, 3 can be calculated for the study population. We enrolled 247 renal transplants in the study. DGF was recorded in 41 cases (15.95%). The Irish score recognized 25 of 41 cases (60.98%), the Jeldres score 41 of 41 cases (100%), and the Chapal score only 7 of 41 (17.07%). Although the Irish score did not identify all cases of DGF, the analysis of data revealed that it is the most accurate, with area under the receiver operating characteristic almost overlapping. CONCLUSIONS The study resulted in some interesting and promising conclusions about the predictability of DGF, defining the Irish score as the most reliable. This result can be considered the fundamental requirement to develop a custom therapeutic algorithm to be applied to all recipients with higher probability of developing DGF.
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Affiliation(s)
- Giuseppe Ietto
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy.
| | - Luca Guzzetti
- Anesthesia and Intensive Care Unit, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Cristiano Salvino Baglieri
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Veronica Raveglia
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Elia Zani
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Fabio Benedetti
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Cristiano Parise
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Valentina Iori
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Caterina Franchi
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Federica Masci
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Andrea Vigezzi
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Enrico Ferri
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Domenico Iovino
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Linda Liepa
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Davide Brusa
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Mauro Oltolina
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Mattia Gritti
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Marta Ripamonti
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | | | - Andrea Ambrosini
- Nephrology Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Francesco Amico
- Trauma Service, Department of Surgery, University of Newcastle, Newcastle, Australia
| | - Salomone Di Saverio
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Gabriele Soldini
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Lorenzo Latham
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Matteo Tozzi
- Vascular Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
| | - Giulio Carcano
- General, Emergency and Transplant Surgery Department, ASST-Settelaghi and University of Insubria, Varese, Italy
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Jahn L, Rüster C, Schlosser M, Winkler Y, Foller S, Grimm MO, Wolf G, Busch M. Rate, Factors, and Outcome of Delayed Graft Function After Kidney Transplantation of Deceased Donors. Transplant Proc 2021; 53:1454-1461. [PMID: 33612277 DOI: 10.1016/j.transproceed.2021.01.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 01/08/2021] [Indexed: 01/24/2023]
Abstract
BACKGROUND Delayed graft function (DGF) is a frequent complication after kidney transplantation affecting long-term outcome. PATIENTS AND METHODS A total of 525 consecutive recipients (age 54.2 ± 13.4 years, 33% female) of kidneys from deceased donors transplanted between 2005 and 2012 were retrospectively examined. DGF was defined as the need of dialysis within the first week after transplantation. RESULTS DGF developed in 21.1% (n = 111). Factors associated with DGF (P ≤ .035, respectively) were recipient body mass index, C-reactive protein of the recipient, residual diuresis, cold ischemia time, donor age, and diuresis in the first hour after transplantation. Median duration of DGF was 16 (2-66) days. Patients after DGF had a significantly lower GFR compared with recipients without DGF either after 3 (32.9 ± 16.5 vs 46.3 ± 18.4 mL/min/1.73 m2) or after 12 months (38.9 ± 19.3 vs 48.6 ± 20.4 mL/min/1.73 m2, P < .001, resp.). During DGF, 12.4% developed BANFF II and 18.0% BANFF I rejection, 20.2% had signs of transplant glomerulitis (first biopsy), and 16.2% (n = 18) remained on dialysis. CONCLUSION DGF affects 1 out of 5 kidney transplants from deceased donors. Minimizing modifiable risk factors, in particular immunologic risk, may ameliorate the incidence and outcome of DGF. The outcome of DGF depends mainly on the diagnosis of any rejection and worsens upon detection of transplant glomerulitis and pronounced interstitial fibrosis and tubular atrophy (IFTA).
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Affiliation(s)
- Laura Jahn
- Department of Internal Medicine III/Collaborative Kidney Transplant Center, University Hospital Jena - Friedrich Schiller University, Jena, Germany
| | - Christiane Rüster
- Department of Internal Medicine III/Collaborative Kidney Transplant Center, University Hospital Jena - Friedrich Schiller University, Jena, Germany
| | - Mandy Schlosser
- Department of Internal Medicine III/Collaborative Kidney Transplant Center, University Hospital Jena - Friedrich Schiller University, Jena, Germany
| | - Yvonne Winkler
- Department of Urology/Collaborative Kidney Transplant Center, University Hospital Jena - Friedrich Schiller University, Jena, Germany
| | - Susan Foller
- Department of Urology/Collaborative Kidney Transplant Center, University Hospital Jena - Friedrich Schiller University, Jena, Germany
| | - Marc-Oliver Grimm
- Department of Urology/Collaborative Kidney Transplant Center, University Hospital Jena - Friedrich Schiller University, Jena, Germany
| | - Gunter Wolf
- Department of Internal Medicine III/Collaborative Kidney Transplant Center, University Hospital Jena - Friedrich Schiller University, Jena, Germany
| | - Martin Busch
- Department of Internal Medicine III/Collaborative Kidney Transplant Center, University Hospital Jena - Friedrich Schiller University, Jena, Germany.
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Costa SD, de Andrade LGM, Barroso FVC, de Oliveira CMC, Daher EDF, Fernandes PFCBC, Esmeraldo RDM, de Sandes-Freitas TV. The impact of deceased donor maintenance on delayed kidney allograft function: A machine learning analysis. PLoS One 2020; 15:e0228597. [PMID: 32027717 PMCID: PMC7004552 DOI: 10.1371/journal.pone.0228597] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 01/18/2020] [Indexed: 12/23/2022] Open
Abstract
Background This study evaluated the risk factors for delayed graft function (DGF) in a country where its incidence is high, detailing donor maintenance-related (DMR) variables and using machine learning (ML) methods beyond the traditional regression-based models. Methods A total of 443 brain dead deceased donor kidney transplants (KT) from two Brazilian centers were retrospectively analyzed and the following DMR were evaluated using predictive modeling: arterial blood gas pH, serum sodium, blood glucose, urine output, mean arterial pressure, vasopressors use, and reversed cardiac arrest. Results Most patients (95.7%) received kidneys from standard criteria donors. The incidence of DGF was 53%. In multivariable logistic regression analysis, DMR variables did not impact on DGF occurrence. In post-hoc analysis including only KT with cold ischemia time<21h (n = 220), urine output in 24h prior to recovery surgery (OR = 0.639, 95%CI 0.444–0.919) and serum sodium (OR = 1.030, 95%CI 1.052–1.379) were risk factors for DGF. Using elastic net regularized regression model and ML analysis (decision tree, neural network and support vector machine), urine output and other DMR variables emerged as DGF predictors: mean arterial pressure, ≥ 1 or high dose vasopressors and blood glucose. Conclusions Some DMR variables were associated with DGF, suggesting a potential impact of variables reflecting poor clinical and hemodynamic status on the incidence of DGF.
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Affiliation(s)
- Silvana Daher Costa
- Department of Clinical Medicine, Faculty of Medicine, Federal University of Ceará, Fortaleza, Ceará, Brazil
- Walter Cantídio University Hospital, Fortaleza, Ceará, Brazil
- Hospital Geral de Fortaleza, Fortaleza, Ceará, Brazil
| | | | | | | | | | | | | | - Tainá Veras de Sandes-Freitas
- Department of Clinical Medicine, Faculty of Medicine, Federal University of Ceará, Fortaleza, Ceará, Brazil
- Hospital Geral de Fortaleza, Fortaleza, Ceará, Brazil
- * E-mail:
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Kers J, Peters-Sengers H, Heemskerk MBA, Berger SP, Betjes MGH, van Zuilen AD, Hilbrands LB, de Fijter JW, Nurmohamed AS, Christiaans MH, Homan van der Heide JJ, Debray TPA, Bemelman FJ. Prediction models for delayed graft function: external validation on The Dutch Prospective Renal Transplantation Registry. Nephrol Dial Transplant 2019; 33:1259-1268. [PMID: 29462353 DOI: 10.1093/ndt/gfy019] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 01/08/2018] [Indexed: 12/16/2022] Open
Abstract
Background Delayed graft function (DGF) is a common complication after kidney transplantation in the era of accepting an equal number of brain- and circulatory-death donor kidneys in the Netherlands. To identify those cases with an increased risk of developing DGF, various multivariable algorithms have been proposed. The objective was to validate the reproducibility of four predictive algorithms by Irish et al. (A risk prediction model for delayed graft function in the current era of deceased donor renal transplantation. Am J Transplant 2010;10:2279-2286) (USA), Jeldres et al. (Prediction of delayed graft function after renal transplantation. Can Urol Assoc J 2009;3:377-382) (Canada), Chapal et al. (A useful scoring system for the prediction and management of delayed graft function following kidney transplantation from cadaveric donors. Kidney Int 2014;86:1130-1139) (France) and Zaza et al. (Predictive model for delayed graft function based on easily available pre-renal transplant variables. Intern Emerg Med 2015;10:135-141) (Italy) according to a novel framework for external validation. Methods We conducted a prospective observational study with data from the Dutch Organ Transplantation Registry (NOTR). Renal transplant recipients from all eight Dutch academic medical centers between 2002 and 2012 who received a deceased allograft were included (N = 3333). The four prediction algorithms were reconstructed from donor, recipient and transplantation data. Their predictive value for DGF was validated by c-statistics, calibration statistics and net benefit analysis. Case-mix (un)relatedness was investigated with a membership model and mean and standard deviation of the linear predictor. Results The prevalence of DGF was 37%. Despite a significantly different case-mix, the US algorithm by Irish was best reproducible, with a c-index of 0.761 (range 0.756 - 0.762), and well-calibrated over the complete range of predicted probabilities of having DGF. The US model had a net benefit of 0.242 at a threshold probability of 0.25, compared with 0.089 net benefit for the same threshold in the original study, equivalent to correctly identifying DGF in 24 cases per 100 patients (true positive results) without an increase in the number of false-positive results. Conclusions The US model by Irish et al. was generalizable and best transportable to Dutch recipients with a deceased donor kidney. The algorithm detects an increased risk of DGF after allocation and enables us to improve individual patient management.
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Affiliation(s)
- Jesper Kers
- Department of Pathology, Academic Medical Center (AMC), Amsterdam, The Netherlands
| | - Hessel Peters-Sengers
- Department of Internal Medicine, Renal Transplant Unit, Academic Medical Center (AMC), Amsterdam, The Netherlands
| | | | - Stefan P Berger
- Department of Nephrology, University Medical Center Groningen (UMCG), Groningen, The Netherlands
| | - Michiel G H Betjes
- Department of Nephrology, Erasmus University Medical Center (Erasmus MC), Rotterdam, The Netherlands
| | - Arjan D van Zuilen
- Department of Nephrology, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Luuk B Hilbrands
- Department of Nephrology, Radboud University Nijmegen Medical Center (RUNMC), Nijmegen, The Netherlands
| | - Johan W de Fijter
- Department of Nephrology, Leiden University Medical Center (LUMC), Leiden, The Netherlands
| | - Azam S Nurmohamed
- Department of Nephrology, Free University Medical Center (VUMC), Amsterdam, The Netherlands
| | - Maarten H Christiaans
- Department of Nephrology, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
| | - Jaap J Homan van der Heide
- Department of Internal Medicine, Renal Transplant Unit, Academic Medical Center (AMC), Amsterdam, The Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Fréderike J Bemelman
- Department of Internal Medicine, Renal Transplant Unit, Academic Medical Center (AMC), Amsterdam, The Netherlands
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Ding CG, Li Y, Tian XH, Hu XJ, Tian PX, Ding XM, Xiang HL, Zheng J, Xue WJ. Predictive Score Model for Delayed Graft Function Based on Hypothermic Machine Perfusion Variables in Kidney Transplantation. Chin Med J (Engl) 2019; 131:2651-2657. [PMID: 30425191 PMCID: PMC6247597 DOI: 10.4103/0366-6999.245278] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Background: Hypothermic machine perfusion (HMP) is being used more often in cardiac death kidney transplantation; however, the significance of assessing organ quality and predicting delayed graft function (DGF) by HMP parameters is still controversial. Therefore, we used a readily available HMP variable to design a scoring model that can identify the highest risk of DGF and provide the guidance and advice for organ allocation and DCD kidney assessment. Methods: From September 1, 2012 to August 31, 2016, 366 qualified kidneys were randomly assigned to the development and validation cohorts in a 2:1 distribution. The HMP variables of the development cohort served as candidate univariate predictors for DGF. The independent predictors of DGF were identified by multivariate logistic regression analysis with a P < 0.05. According to the odds ratios (ORs) value, each HMP variable was assigned a weighted integer, and the sum of the integers indicated the total risk score for each kidney. The validation cohort was used to verify the accuracy and reliability of the scoring model. Results: HMP duration (OR = 1.165, 95% confidence interval [CI ]: 1.008–1.360, P = 0.043), resistance (OR = 2.190, 95% CI: 1.032–10.20, P < 0.001), and flow rate (OR = 0.931, 95% CI: 0.894–0.967, P = 0.011) were the independent predictors of identified DGF. The HMP predictive score ranged from 0 to 14, and there was a clear increase in the incidence of DGF, from the low predictive score group to the very high predictive score group. We formed four increasingly serious risk categories (scores 0–3, 4–7, 8–11, and 12–14) according to the frequency associated with the different risk scores of DGF. The HMP predictive score indicates good discriminative power with a c-statistic of 0.706 in the validation cohort, and it had significantly better prediction value for DGF compared to both terminal flow (P = 0.012) and resistance (P = 0.006). Conclusion: The HMP predictive score is a good noninvasive tool for assessing the quality of DCD kidneys, and it is potentially useful for physicians in making optimal decisions about the organs donated.
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Affiliation(s)
- Chen-Guang Ding
- Department of Kidney Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Yang Li
- Department of Kidney Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Xiao-Hui Tian
- Department of Kidney Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Xiao-Jun Hu
- Department of Kidney Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Pu-Xu Tian
- Department of Kidney Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Xiao-Ming Ding
- Department of Kidney Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - He-Li Xiang
- Department of Kidney Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Jin Zheng
- Department of Kidney Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Wu-Jun Xue
- Department of Kidney Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
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Ding CG, Tai QH, Han F, Li Y, Tian XH, Tian PX, Ding XM, Pan XM, Zheng J, Xiang HL, Xue WJ. Predictive Score Model for Delayed Graft Function Based on Easily Available Variables before Kidney Donation after Cardiac Death. Chin Med J (Engl) 2018; 130:2429-2434. [PMID: 29052563 PMCID: PMC5684627 DOI: 10.4103/0366-6999.216409] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Background: How to evaluate the quality of donation after cardiac death (DCD) kidneys has become a critical problem in kidney transplantation in China. Hence, the aim of this study was to develop a simple donor risk score model to evaluate the quality of DCD kidneys before DCD. Methods: A total of 543 qualified kidneys were randomized in a 2:1 manner to create the development and validation cohorts. The donor variables in the development cohort were considered as candidate univariate predictors of delayed graft function (DGF). Multivariate logistic regression was then used to identify independent predictors of DGF with P < 0.05. Date from validation cohort were used to validate the donor scoring model. Results: Based on the odds ratios, eight identified variables were assigned a weighted integer; the sum of the integer was the total risk score for each kidney. The donor risk score, ranging from 0 to 28, demonstrated good discriminative power with a C-statistic of 0.790. Similar results were obtained from validation cohort with C-statistic of 0.783. Based on the obtained frequencies of DGF in relation to different risk scores, we formed four risk categories of increasing severity (scores 0–4, 5–9, 10–14, and 15–28). Conclusions: The scoring model might be a good noninvasive tool for assessing the quality of DCD kidneys before donation and potentially useful for physicians to make optimal decisions about donor organ offers.
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Affiliation(s)
- Chen-Guang Ding
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Qian-Hui Tai
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Feng Han
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Yang Li
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Xiao-Hui Tian
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Pu-Xun Tian
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Xiao-Ming Ding
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Xiao-Ming Pan
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Jin Zheng
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - He-Li Xiang
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
| | - Wu-Jun Xue
- Department of Renal Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University; Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China
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8
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Selby PJ, Banks RE, Gregory W, Hewison J, Rosenberg W, Altman DG, Deeks JJ, McCabe C, Parkes J, Sturgeon C, Thompson D, Twiddy M, Bestall J, Bedlington J, Hale T, Dinnes J, Jones M, Lewington A, Messenger MP, Napp V, Sitch A, Tanwar S, Vasudev NS, Baxter P, Bell S, Cairns DA, Calder N, Corrigan N, Del Galdo F, Heudtlass P, Hornigold N, Hulme C, Hutchinson M, Lippiatt C, Livingstone T, Longo R, Potton M, Roberts S, Sim S, Trainor S, Welberry Smith M, Neuberger J, Thorburn D, Richardson P, Christie J, Sheerin N, McKane W, Gibbs P, Edwards A, Soomro N, Adeyoju A, Stewart GD, Hrouda D. Methods for the evaluation of biomarkers in patients with kidney and liver diseases: multicentre research programme including ELUCIDATE RCT. PROGRAMME GRANTS FOR APPLIED RESEARCH 2018. [DOI: 10.3310/pgfar06030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BackgroundProtein biomarkers with associations with the activity and outcomes of diseases are being identified by modern proteomic technologies. They may be simple, accessible, cheap and safe tests that can inform diagnosis, prognosis, treatment selection, monitoring of disease activity and therapy and may substitute for complex, invasive and expensive tests. However, their potential is not yet being realised.Design and methodsThe study consisted of three workstreams to create a framework for research: workstream 1, methodology – to define current practice and explore methodology innovations for biomarkers for monitoring disease; workstream 2, clinical translation – to create a framework of research practice, high-quality samples and related clinical data to evaluate the validity and clinical utility of protein biomarkers; and workstream 3, the ELF to Uncover Cirrhosis as an Indication for Diagnosis and Action for Treatable Event (ELUCIDATE) randomised controlled trial (RCT) – an exemplar RCT of an established test, the ADVIA Centaur® Enhanced Liver Fibrosis (ELF) test (Siemens Healthcare Diagnostics Ltd, Camberley, UK) [consisting of a panel of three markers – (1) serum hyaluronic acid, (2) amino-terminal propeptide of type III procollagen and (3) tissue inhibitor of metalloproteinase 1], for liver cirrhosis to determine its impact on diagnostic timing and the management of cirrhosis and the process of care and improving outcomes.ResultsThe methodology workstream evaluated the quality of recommendations for using prostate-specific antigen to monitor patients, systematically reviewed RCTs of monitoring strategies and reviewed the monitoring biomarker literature and how monitoring can have an impact on outcomes. Simulation studies were conducted to evaluate monitoring and improve the merits of health care. The monitoring biomarker literature is modest and robust conclusions are infrequent. We recommend improvements in research practice. Patients strongly endorsed the need for robust and conclusive research in this area. The clinical translation workstream focused on analytical and clinical validity. Cohorts were established for renal cell carcinoma (RCC) and renal transplantation (RT), with samples and patient data from multiple centres, as a rapid-access resource to evaluate the validity of biomarkers. Candidate biomarkers for RCC and RT were identified from the literature and their quality was evaluated and selected biomarkers were prioritised. The duration of follow-up was a limitation but biomarkers were identified that may be taken forward for clinical utility. In the third workstream, the ELUCIDATE trial registered 1303 patients and randomised 878 patients out of a target of 1000. The trial started late and recruited slowly initially but ultimately recruited with good statistical power to answer the key questions. ELF monitoring altered the patient process of care and may show benefits from the early introduction of interventions with further follow-up. The ELUCIDATE trial was an ‘exemplar’ trial that has demonstrated the challenges of evaluating biomarker strategies in ‘end-to-end’ RCTs and will inform future study designs.ConclusionsThe limitations in the programme were principally that, during the collection and curation of the cohorts of patients with RCC and RT, the pace of discovery of new biomarkers in commercial and non-commercial research was slower than anticipated and so conclusive evaluations using the cohorts are few; however, access to the cohorts will be sustained for future new biomarkers. The ELUCIDATE trial was slow to start and recruit to, with a late surge of recruitment, and so final conclusions about the impact of the ELF test on long-term outcomes await further follow-up. The findings from the three workstreams were used to synthesise a strategy and framework for future biomarker evaluations incorporating innovations in study design, health economics and health informatics.Trial registrationCurrent Controlled Trials ISRCTN74815110, UKCRN ID 9954 and UKCRN ID 11930.FundingThis project was funded by the NIHR Programme Grants for Applied Research programme and will be published in full inProgramme Grants for Applied Research; Vol. 6, No. 3. See the NIHR Journals Library website for further project information.
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Affiliation(s)
- Peter J Selby
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Rosamonde E Banks
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Walter Gregory
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Jenny Hewison
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - William Rosenberg
- Institute for Liver and Digestive Health, Division of Medicine, University College London, London, UK
| | - Douglas G Altman
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
| | - Jonathan J Deeks
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Christopher McCabe
- Department of Emergency Medicine, University of Alberta Hospital, Edmonton, AB, Canada
| | - Julie Parkes
- Primary Care and Population Sciences Academic Unit, University of Southampton, Southampton, UK
| | | | | | - Maureen Twiddy
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Janine Bestall
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | | | - Tilly Hale
- LIVErNORTH Liver Patient Support, Newcastle upon Tyne, UK
| | - Jacqueline Dinnes
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Marc Jones
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | | | | | - Vicky Napp
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Alice Sitch
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Sudeep Tanwar
- Institute for Liver and Digestive Health, Division of Medicine, University College London, London, UK
| | - Naveen S Vasudev
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Paul Baxter
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Sue Bell
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - David A Cairns
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | | | - Neil Corrigan
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Francesco Del Galdo
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK
| | - Peter Heudtlass
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Nick Hornigold
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Claire Hulme
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Michelle Hutchinson
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Carys Lippiatt
- Department of Specialist Laboratory Medicine, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | | | - Roberta Longo
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Matthew Potton
- Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | - Stephanie Roberts
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Sheryl Sim
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Sebastian Trainor
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Matthew Welberry Smith
- Clinical and Biomedical Proteomics Group, Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
- Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - James Neuberger
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | | | - Paul Richardson
- Royal Liverpool and Broadgreen University Hospitals NHS Trust, Liverpool, UK
| | - John Christie
- Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Neil Sheerin
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - William McKane
- Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Paul Gibbs
- Portsmouth Hospitals NHS Trust, Portsmouth, UK
| | | | - Naeem Soomro
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | | | - Grant D Stewart
- NHS Lothian, Edinburgh, UK
- Academic Urology Group, University of Cambridge, Cambridge, UK
| | - David Hrouda
- Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, UK
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9
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Zhang H, Zheng L, Qin S, Liu L, Yuan X, Fu Q, Li J, Deng R, Deng S, Yu F, He X, Wang C. Evaluation of predictive models for delayed graft function of deceased kidney transplantation. Oncotarget 2017; 9:1735-1744. [PMID: 29416727 PMCID: PMC5788595 DOI: 10.18632/oncotarget.22711] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2017] [Accepted: 10/27/2017] [Indexed: 12/18/2022] Open
Abstract
Background This study aimed to evaluate the predictive power of five available delayed graft function (DGF)-prediction models for kidney transplants in the Chinese population. Results Among the five models, the Irish 2010 model scored the best in performance for the Chinese population. Irish 2010 model had an area under the receiver operating characteristic (ROC) curve of 0.737. Hosmer-Lemeshow goodness-of-fit test showed that the Irish 2010 model had a strong correlation between the calculated DGF risk and the observed DGF incidence (p = 0.887). When Irish 2010 model was used in the clinic, the optimal upper cut-off was set to 0.5 with the best positive likelihood ratio, while the lower cut-off was set to 0.1 with the best negative likelihood ratio. In the subgroup of donor aged ≤ 5, the observed DGF incidence was significantly higher than the calculated DGF risk by Irish 2010 model (27% vs. 9%). Materials and Methods A total of 711 renal transplant cases using deceased donors from China Donation after Citizen's Death Program at our center between February 2007 and August 2016 were included in the analysis using the five predictive models (Irish 2010, Irish 2003, Chaphal 2014, Zaza 2015, Jeldres 2009). Conclusions Irish 2010 model has the best predictive power for DGF risk in Chinese population among the five models. However, it may not be suitable for allograft recipients whose donor aged ≤ 5-year-old.
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Affiliation(s)
- Huanxi Zhang
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
| | - Linli Zheng
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
| | - Shuhang Qin
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China
| | - Longshan Liu
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
| | - Xiaopeng Yuan
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
| | - Qian Fu
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
| | - Jun Li
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
| | - Ronghai Deng
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
| | - Suxiong Deng
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
| | - Fangchao Yu
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China
| | - Xiaoshun He
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.,Guangdong Provincial Key Laboratory on Organ Donation and Transplant Immunology, Guangzhou 510080, China
| | - Changxi Wang
- Organ Transplant Center, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510080, China.,Guangdong Provincial Key Laboratory on Organ Donation and Transplant Immunology, Guangzhou 510080, China
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10
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Michalak M, Wouters K, Fransen E, Hellemans R, Van Craenenbroeck AH, Couttenye MM, Bracke B, Ysebaert DK, Hartman V, De Greef K, Chapelle T, Roeyen G, Van Beeumen G, Emonds MP, Abramowicz D, Bosmans JL. Prediction of delayed graft function using different scoring algorithms: A single-center experience. World J Transplant 2017; 7:260-268. [PMID: 29104860 PMCID: PMC5661123 DOI: 10.5500/wjt.v7.i5.260] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Revised: 03/23/2017] [Accepted: 05/05/2017] [Indexed: 02/05/2023] Open
Abstract
AIM To compare the performance of 3 published delayed graft function (DGF) calculators that compute the theoretical risk of DGF for each patient.
METHODS This single-center, retrospective study included 247 consecutive kidney transplants from a deceased donor. These kidney transplantations were performed at our institution between January 2003 and December 2012. We compared the occurrence of observed DGF in our cohort with the predicted DGF according to three different published calculators. The accuracy of the calculators was evaluated by means of the c-index (receiver operating characteristic curve).
RESULTS DGF occurred in 15.3% of the transplants under study. The c index of the Irish calculator provided an area under the curve (AUC) of 0.69 indicating an acceptable level of prediction, in contrast to the poor performance of the Jeldres nomogram (AUC = 0.54) and the Chapal nomogram (AUC = 0.51). With the Irish algorithm the predicted DGF risk and the observed DGF probabilities were close. The mean calculated DGF risk was significantly different between DGF-positive and DGF-negative subjects (P < 0.0001). However, at the level of the individual patient the calculated risk of DGF overlapped very widely with ranges from 10% to 51% for recipients with DGF and from 4% to 56% for those without DGF. The sensitivity, specificity and positive predictive value of a calculated DGF risk ≥ 30% with the Irish nomogram were 32%, 91% and 38%.
CONCLUSION Predictive models for DGF after kidney transplantation are performant in the population in which they were derived, but less so in external validations.
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Affiliation(s)
- Magda Michalak
- Department of Nephrology-Hypertension, Antwerp University Hospital, B-2650 Edegem, Belgium
| | - Kristien Wouters
- Department of Medical Statistics, Antwerp University Hospital, B-2650 Edegem, Belgium
| | - Erik Fransen
- StatUa Center for Statistics, University of Antwerp, B-2610 Wilrijk, Belgium
| | - Rachel Hellemans
- Department of Nephrology-Hypertension, Antwerp University Hospital, B-2650 Edegem, Belgium
| | | | - Marie M Couttenye
- Department of Nephrology-Hypertension, Antwerp University Hospital, B-2650 Edegem, Belgium
| | - Bart Bracke
- Department of Hepatobiliary, Endocrine and Transplantation Surgery, Antwerp University Hospital, B-2650 Edegem, Belgium
| | - Dirk K Ysebaert
- Department of Hepatobiliary, Endocrine and Transplantation Surgery, Antwerp University Hospital, B-2650 Edegem, Belgium
| | - Vera Hartman
- Department of Hepatobiliary, Endocrine and Transplantation Surgery, Antwerp University Hospital, B-2650 Edegem, Belgium
| | - Kathleen De Greef
- Department of Hepatobiliary, Endocrine and Transplantation Surgery, Antwerp University Hospital, B-2650 Edegem, Belgium
| | - Thiery Chapelle
- Department of Hepatobiliary, Endocrine and Transplantation Surgery, Antwerp University Hospital, B-2650 Edegem, Belgium
| | - Geert Roeyen
- Department of Hepatobiliary, Endocrine and Transplantation Surgery, Antwerp University Hospital, B-2650 Edegem, Belgium
| | - Gerda Van Beeumen
- Department of Nephrology-Hypertension, Antwerp University Hospital, B-2650 Edegem, Belgium
| | - Marie-Paule Emonds
- Histocompatibility and Immunogenetic Laboratory, Belgian Red Cross-Flanders, 2800 Mechelen, Belgium
| | - Daniel Abramowicz
- Department of Nephrology-Hypertension, Antwerp University Hospital, B-2650 Edegem, Belgium
| | - Jean-Louis Bosmans
- Department of Nephrology-Hypertension, Antwerp University Hospital, B-2650 Edegem, Belgium
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11
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Autophagy, Innate Immunity and Tissue Repair in Acute Kidney Injury. Int J Mol Sci 2016; 17:ijms17050662. [PMID: 27153058 PMCID: PMC4881488 DOI: 10.3390/ijms17050662] [Citation(s) in RCA: 83] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2016] [Revised: 04/14/2016] [Accepted: 04/20/2016] [Indexed: 01/09/2023] Open
Abstract
Kidney is a vital organ with high energy demands to actively maintain plasma hemodynamics, electrolytes and water homeostasis. Among the nephron segments, the renal tubular epithelium is endowed with high mitochondria density for their function in active transport. Acute kidney injury (AKI) is an important clinical syndrome and a global public health issue with high mortality rate and socioeconomic burden due to lack of effective therapy. AKI results in acute cell death and necrosis of renal tubule epithelial cells accompanied with leakage of tubular fluid and inflammation. The inflammatory immune response triggered by the tubular cell death, mitochondrial damage, associative oxidative stress, and the release of many tissue damage factors have been identified as key elements driving the pathophysiology of AKI. Autophagy, the cellular mechanism that removes damaged organelles via lysosome-mediated degradation, had been proposed to be renoprotective. An in-depth understanding of the intricate interplay between autophagy and innate immune response, and their roles in AKI pathology could lead to novel therapies in AKI. This review addresses the current pathophysiology of AKI in aspects of mitochondrial dysfunction, innate immunity, and molecular mechanisms of autophagy. Recent advances in renal tissue regeneration and potential therapeutic interventions are also discussed.
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12
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Endre ZH, Pianta TJ, Pickering JW. Timely Diagnosis of Acute Kidney Injury Using Kinetic eGFR and the Creatinine Excretion to Production Ratio, E/eG - Creatinine Can Be Useful! Nephron Clin Pract 2016; 132:312-6. [DOI: 10.1159/000444456] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 02/01/2016] [Indexed: 11/19/2022] Open
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13
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A triple-biomarker approach for the detection of delayed graft function after kidney transplantation using serum creatinine, cystatin C, and malondialdehyde. Clin Biochem 2015; 48:1033-8. [DOI: 10.1016/j.clinbiochem.2015.07.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Revised: 07/01/2015] [Accepted: 07/06/2015] [Indexed: 12/28/2022]
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14
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Silver SA, Cardinal H, Colwell K, Burger D, Dickhout JG. Acute kidney injury: preclinical innovations, challenges, and opportunities for translation. Can J Kidney Health Dis 2015; 2:30. [PMID: 26331054 PMCID: PMC4556308 DOI: 10.1186/s40697-015-0062-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Accepted: 07/02/2015] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Acute kidney injury (AKI) is a clinically important condition that has attracted a great deal of interest from the biomedical research community. However, acute kidney injury AKI research findings have yet to be translated into significant changes in clinical practice. OBJECTIVE This article reviews many of the preclinical innovations in acute kidney injury AKI treatment, and explores challenges and opportunities to translate these finding into clinical practice. SOURCES OF INFORMATION MEDLINE, ISI Web of Science. FINDINGS This paper details areas in biomedical research where translation of pre-clinical findings into clinical trials is ongoing, or nearing a point where trial design is warranted. Further, the paper examines ways that best practice in the management of AKI can reach a broader proportion of the patient population experiencing this condition. LIMITATIONS This review highlights pertinent literature from the perspective of the research interests of the authors for new translational work in AKI. As such, it does not represent a systematic review of all of the AKI literature. IMPLICATIONS Translation of findings from biomedical research into AKI therapy presents several challenges. These may be partly overcome by targeting populations for interventional trials where the likelihood of AKI is very high, and readily predictable. Further, specific clinics to follow-up with patients after AKI events hold promise to provide best practice in care, and to translate therapies into treatment for the broadest possible patient populations.
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Affiliation(s)
- Samuel A. Silver
- />Division of Nephrology, St. Michael’s Hospital, University of Toronto, Toronto, Canada
| | - Héloise Cardinal
- />Division of Nephrology, Centre Hospitalier de l’Université de Montréal and CHUM research center, Montreal, Quebec Canada
| | - Katelyn Colwell
- />Department of Medicine, Division of Nephrology, McMaster University and St. Joseph’s Healthcare Hamilton, Hamilton, Ontario Canada
| | - Dylan Burger
- />Kidney Research Centre, Ottawa Hospital Research Institute, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario Canada
| | - Jeffrey G. Dickhout
- />Department of Medicine, Division of Nephrology, McMaster University and St. Joseph’s Healthcare Hamilton, 50 Charlton Avenue East, Hamilton, Ontario L8N 4A6 Canada
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15
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Pianta TJ, Peake PW, Pickering JW, Kelleher M, Buckley NA, Endre ZH. Evaluation of biomarkers of cell cycle arrest and inflammation in prediction of dialysis or recovery after kidney transplantation. Transpl Int 2015; 28:1392-404. [DOI: 10.1111/tri.12636] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2014] [Revised: 12/30/2014] [Accepted: 07/07/2015] [Indexed: 11/28/2022]
Affiliation(s)
- Timothy J. Pianta
- Prince of Wales Clinical School; University of New South Wales; Sydney NSW Australia
- Northern Clinical School; Melbourne Medical School; University of Melbourne; Epping Vic Australia
| | - Philip W. Peake
- Prince of Wales Clinical School; University of New South Wales; Sydney NSW Australia
| | - John W. Pickering
- Department of Medicine; University of Otago; Christchurch New Zealand
| | - Michaela Kelleher
- Department of Nephrology; Prince of Wales Hospital; Sydney NSW Australia
| | | | - Zoltan H. Endre
- Prince of Wales Clinical School; University of New South Wales; Sydney NSW Australia
- Department of Medicine; University of Otago; Christchurch New Zealand
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16
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Pianta TJ, Endre ZH, Pickering JW, Buckley NA, Peake PW. Kinetic Estimation of GFR Improves Prediction of Dialysis and Recovery after Kidney Transplantation. PLoS One 2015; 10:e0125669. [PMID: 25938452 PMCID: PMC4418565 DOI: 10.1371/journal.pone.0125669] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2014] [Accepted: 03/23/2015] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND The early prediction of delayed graft function (DGF) would facilitate patient management after kidney transplantation. METHODS In a single-centre retrospective analysis, we investigated kinetic estimated GFR under non-steady-state conditions, KeGFR, in prediction of DGF. KeGFR(sCr) was calculated at 4h, 8h and 12h in 56 recipients of deceased donor kidneys from initial serum creatinine (sCr) concentrations, estimated creatinine production rate, volume of distribution, and the difference between consecutive sCr values. The utility of KeGFR(sCr) for DGF prediction was compared with, sCr, plasma cystatin C (pCysC), and KeGFR(pCysC) similarly derived from pCysC concentrations. RESULTS At 4h, the KeGFR(sCr) area under the receiver operator characteristic curve (AUC) for DGF prediction was 0.69 (95% CI: 0.56-0.83), while sCr was not useful (AUC 0.56, (CI: 0.41-0.72). Integrated discrimination improvement analysis showed that the KeGFR(sCr) improved a validated clinical prediction model at 4h, 8h, and 12h, increasing the AUC from 0.68 (0.52-0.83) to 0.88 (0.78-0.99) at 12h (p = 0.01). KeGFR(pCysC) also improved DGF prediction. In contrast, sCr provided no improvement at any time point. CONCLUSIONS Calculation of KeGFR from sCr facilitates early prediction of DGF within 4 hours of renal transplantation.
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Affiliation(s)
- Timothy J. Pianta
- Prince of Wales Clinical School, University of New South Wales, Sydney, Australia
- Melbourne Medical School, University of Melbourne, Melbourne, Australia
| | - Zoltan H. Endre
- Prince of Wales Clinical School, University of New South Wales, Sydney, Australia
- Department of Medicine, University of Otago, Christchurch, New Zealand
| | - John W. Pickering
- Department of Medicine, University of Otago, Christchurch, New Zealand
| | | | - Philip W. Peake
- Prince of Wales Clinical School, University of New South Wales, Sydney, Australia
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17
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Zaza G, Ferraro PM, Tessari G, Sandrini S, Scolari MP, Capelli I, Minetti E, Gesualdo L, Girolomoni G, Gambaro G, Lupo A, Boschiero L. Predictive model for delayed graft function based on easily available pre-renal transplant variables. Intern Emerg Med 2015; 10:135-41. [PMID: 25164408 DOI: 10.1007/s11739-014-1119-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Accepted: 08/12/2014] [Indexed: 02/06/2023]
Abstract
Identification of pre-transplant factors influencing delayed graft function (DGF) could have an important clinical impact. This could allow clinicians to early identify dialyzed chronic kidney disease (CKD) patients eligible for special transplant programs, preventive therapeutic strategies and specific post-transplant immunosuppressive treatments. To achieve these objectives, we retrospectively analyzed main demographic and clinical features, follow-up events and outcomes registered in a large dedicated dataset including 2,755 patients compiled collaboratively by four Italian renal/transplant units. The years of transplant ranged from 1984 to 2012. Statistical analysis clearly demonstrated that some recipients' characteristics at the time of transplantation (age and body weight) and dialysis-related variables (modality and duration) were significantly associated with DGF development (p ≤ 0.001). The area under the receiver-operating characteristic (ROC) curve of the final model based on the four identified variables predicting DGF was 0.63 (95 % CI 0.61, 0.65). Additionally, deciles of the score were significantly associated with the incidence of DGF (p value for trend <0.001). Therefore, in conclusion, in our study we identified a pre-operative predictive model for DGF, based on inexpensive and easily available variables, potentially useful in routine clinical practice in most of the Italian and European dialysis units.
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Affiliation(s)
- Gianluigi Zaza
- Renal Unit, Department of Medicine, University-Hospital of Verona, Piazzale A. Stefani 1, 37126, Verona, Italy,
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18
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Chapal M, Le Borgne F, Legendre C, Kreis H, Mourad G, Garrigue V, Morelon E, Buron F, Rostaing L, Kamar N, Kessler M, Ladrière M, Soulillou JP, Launay K, Daguin P, Offredo L, Giral M, Foucher Y. A useful scoring system for the prediction and management of delayed graft function following kidney transplantation from cadaveric donors. Kidney Int 2014; 86:1130-9. [PMID: 24897036 DOI: 10.1038/ki.2014.188] [Citation(s) in RCA: 84] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2013] [Revised: 03/04/2014] [Accepted: 04/03/2014] [Indexed: 02/07/2023]
Abstract
Delayed graft function (DGF) is a common complication in kidney transplantation and is known to be correlated with short- and long-term graft outcomes. Here we explored the possibility of developing a simple tool that could predict with good confidence the occurrence of DGF and could be helpful in current clinical practice. We built a score, tentatively called DGFS, from a French multicenter and prospective cohort of 1844 adult recipients of deceased donor kidneys collected since 2007, and computerized in the Données Informatisées et VAlidées en Transplantation databank. Only five explicative variables (cold ischemia time, donor age, donor serum creatinine, recipient body mass index, and induction therapy) contributed significantly to the DGF prediction. These were associated with a good predictive capacity (area under the ROC curve at 0.73). The DGFS calculation is facilitated by an application available on smartphones, tablets, or computers at www.divat.fr/en/online-calculators/dgfs. The DGFS should allow the simple classification of patients according to their DGF risk at the time of transplantation, and thus allow tailored-specific management or therapeutic strategies.
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Affiliation(s)
- Marion Chapal
- 1] Institut de Transplantation et de Recherche en Transplantation, ITUN, CHU Nantes, RTRS « Centaure », Nantes and Inserm U1064 (Immunointervention dans les Allo et Xénotransplantation), Nantes University, boulevard Jean Monnet, Nantes, France [2] Centre d'Investigation Clinique biothérapie, Labex Transplantex, boulevard Jean Monnet, Nantes, France
| | - Florent Le Borgne
- EA 4275 SPHERE-Biostatistics, Clinical Research and Pharmaco-Epidemiology, Nantes University, Nantes, France
| | - Christophe Legendre
- 1] Service de Transplantation Rénale et de Soins Intensifs, Hôpital Necker, APHP, Paris, France [2] Universités Paris Descartes et Sorbonne Paris Cité, Paris, France
| | - Henri Kreis
- 1] Service de Transplantation Rénale et de Soins Intensifs, Hôpital Necker, APHP, Paris, France [2] Universités Paris Descartes et Sorbonne Paris Cité, Paris, France
| | - Georges Mourad
- Service de Néphrologie, Dialyse et Transplantation, Hôpital Lapeyronie, Montpellier, Université Montpellier I, Montpellier, France
| | - Valérie Garrigue
- Service de Néphrologie, Dialyse et Transplantation, Hôpital Lapeyronie, Montpellier, Université Montpellier I, Montpellier, France
| | - Emmanuel Morelon
- Service de Néphrologie, Transplantation et Immunologie Clinique, Hôpital Edouard Herriot, Lyon, France
| | - Fanny Buron
- Service de Néphrologie, Transplantation et Immunologie Clinique, Hôpital Edouard Herriot, Lyon, France
| | - Lionel Rostaing
- 1] Service de Néphrologie, HTA, Dialyse et Transplantation d'Organes, CHU Rangueil, Toulouse, France [2] Université Paul Sabatier, Toulouse, France
| | - Nassim Kamar
- 1] Service de Néphrologie, HTA, Dialyse et Transplantation d'Organes, CHU Rangueil, Toulouse, France [2] Université Paul Sabatier, Toulouse, France
| | - Michèle Kessler
- Service de Transplantation Rénale, CHU Brabois, Nancy, France
| | - Marc Ladrière
- Service de Transplantation Rénale, CHU Brabois, Nancy, France
| | - Jean-Paul Soulillou
- 1] Institut de Transplantation et de Recherche en Transplantation, ITUN, CHU Nantes, RTRS « Centaure », Nantes and Inserm U1064 (Immunointervention dans les Allo et Xénotransplantation), Nantes University, boulevard Jean Monnet, Nantes, France [2] Centre d'Investigation Clinique biothérapie, Labex Transplantex, boulevard Jean Monnet, Nantes, France
| | - Katy Launay
- 1] Institut de Transplantation et de Recherche en Transplantation, ITUN, CHU Nantes, RTRS « Centaure », Nantes and Inserm U1064 (Immunointervention dans les Allo et Xénotransplantation), Nantes University, boulevard Jean Monnet, Nantes, France [2] EA 4275 SPHERE-Biostatistics, Clinical Research and Pharmaco-Epidemiology, Nantes University, Nantes, France
| | - Pascal Daguin
- Institut de Transplantation et de Recherche en Transplantation, ITUN, CHU Nantes, RTRS « Centaure », Nantes and Inserm U1064 (Immunointervention dans les Allo et Xénotransplantation), Nantes University, boulevard Jean Monnet, Nantes, France
| | - Lucile Offredo
- EA 4275 SPHERE-Biostatistics, Clinical Research and Pharmaco-Epidemiology, Nantes University, Nantes, France
| | - Magali Giral
- 1] Institut de Transplantation et de Recherche en Transplantation, ITUN, CHU Nantes, RTRS « Centaure », Nantes and Inserm U1064 (Immunointervention dans les Allo et Xénotransplantation), Nantes University, boulevard Jean Monnet, Nantes, France [2] Centre d'Investigation Clinique biothérapie, Labex Transplantex, boulevard Jean Monnet, Nantes, France
| | - Yohann Foucher
- 1] Institut de Transplantation et de Recherche en Transplantation, ITUN, CHU Nantes, RTRS « Centaure », Nantes and Inserm U1064 (Immunointervention dans les Allo et Xénotransplantation), Nantes University, boulevard Jean Monnet, Nantes, France [2] EA 4275 SPHERE-Biostatistics, Clinical Research and Pharmaco-Epidemiology, Nantes University, Nantes, France
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Zaza G, Rascio F, Pontrelli P, Granata S, Stifanelli P, Accetturo M, Ancona N, Gesualdo L, Lupo A, Grandaliano G. Karyopherins: potential biological elements involved in the delayed graft function in renal transplant recipients. BMC Med Genomics 2014; 7:14. [PMID: 24625024 PMCID: PMC3975142 DOI: 10.1186/1755-8794-7-14] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2013] [Accepted: 03/03/2014] [Indexed: 12/11/2022] Open
Abstract
Background Immediately after renal transplantation, patients experience rapid and significant improvement of their clinical conditions and undergo considerable systemic and cellular modifications. However, some patients present a slow recovery of the renal function commonly defined as delayed graft function (DGF). Although clinically well characterized, the molecular mechanisms underlying this condition are not totally defined, thus, we are currently missing specific clinical markers to predict and to make early diagnosis of this event. Methods We investigated, using a pathway analysis approach, the transcriptomic profile of peripheral blood mononuclear cells (PBMC) from renal transplant recipients with DGF and with early graft function (EGF), before (T0) and 24 hours (T24) after transplantation. Results Bioinformatics/statistical analysis showed that 15 pathways (8 up-regulated and 7 down-regulated) and 11 pathways (5 up-regulated and 6 down-regulated) were able to identify DGF patients at T0 and T24, respectively. Interestingly, the most up-regulated pathway at both time points was NLS-bearing substrate import into nucleus, which includes genes encoding for several subtypes of karyopherins, a group of proteins involved in nucleocytoplasmic transport. Signal transducers and activators of transcription (STAT) utilize karyopherins-alpha (KPNA) for their passage from cytoplasm into the nucleus. In vitro functional analysis demonstrated that in PBMCs of DGF patients, there was a significant KPNA-mediated nuclear translocation of the phosphorylated form of STAT3 (pSTAT3) after short-time stimulation (2 and 5 minutes) with interleukin-6. Conclusions Our study suggests the involvement, immediately before transplantation, of karyopherin-mediated nuclear transport in the onset and development of DGF. Additionally, it reveals that karyopherins could be good candidates as potential DGF predictive clinical biomarkers and targets for pharmacological interventions in renal transplantation. However, because of the low number of patients analyzed and some methodological limitations, additional studies are needed to validate and to better address these points.
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Affiliation(s)
- Gianluigi Zaza
- Renal Unit, Department of Medicine, University-Hospital of Verona, Piazzale A, Stefani 1, 37126 Verona (VR), Italy.
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