Prediction Of Delayed Graft Function Research Articles

Intraoperative Hemodynamic Parameters Associated With Delayed Graft Function in Kidney Transplant Patients

Intraoperative hemodynamic instability was proven to be associated with delayed graft function (DGF) after kidney transplantation. This retrospective study aims to find the specific intraoperative hemodynamic parameters as an efficient predicting factor of DGF. Patients who underwent kidney transplantation between 2020 and 2022 were enrolled and classified into DGF and non-DGF groups. Pediatric and multiorgan recipients were excluded. Hemodynamic parameters such as central venous pressure, mean arterial pressure, cardiac output, and cardiac index (CI) at the timings of wound incision, graft reperfusion, and operation completion were recorded, respectively. A comparison of parameters between these 2 groups was analyzed. We enrolled 42 recipients, with 26 in the DGF group and 16 in the non-DGF group. Compared with the DGF group, CI around graft reperfusion was significantly higher in the non-DGF group (3.97 vs 4.67 L/min/m2, P=.043). Other hemodynamic variables revealed no statistical difference. In the results of multivariate analysis, the deceased donor source, the greater volume of blood loss, and the lower CI around graft reperfusion were considered independent risk factors for DGF. Using CI around graft reperfusion to conduct a receiver operating characteristic (ROC) curve for DGF prediction, the area under the ROC curve achieved a value of 0.739 (95% confidence interval, 0.579-0.900), with the optimal cut-point value at CI=4.245 L/min/m2. The cardiac index value around graft reperfusion was statistically associated with the incidence of DGF and might be used as a valid predicting factor.

Prediction model of delayed graft function based on clinical characteristics combined with serum IL-2 levels

BackgroundKidney transplantation is an effective treatment for end-stage renal disease (ESRD). Delayed graft function (DGF) is a common complication after kidney transplantation and exerts substantial effects on graft function and long-term graft survival. Therefore, the construction of an effective model to predict the occurrence of DGF is particularly important.MethodsSeventy-one patients receiving their first kidney transplant at the First Affiliated Hospital of Nanchang University from October 2020 to October 2021 were enrolled in the discovery cohort. Based on clinical characteristics and serum markers, a logistic regression model was used to simulate the risk of DGF in the discovery cohort. The DGF prediction model was named the prediction system and was composed of risk factors related to DGF. Thirty-two patients receiving a kidney transplant at the First Affiliated Hospital of Nanchang University from October 2021 to February 2022 were enrolled in the validation cohort. The validation cohort was used to verify the accuracy and reliability of the prediction model.ResultsCold ischemia time (CIT), donor history of diabetes mellitus, donor interleukin-2 (IL-2) level and donor terminal creatinine level constitute the prediction system. In the validation test, the area under the receiver operating characteristic curve (AUC) was 0.867 for the prediction system, and good calibration of the model was confirmed in the validation cohort.ConclusionsThis study constructed a reliable and highly accurate prediction model that provides a practical tool for predicting DGF. Additionally, IL-2 participates in the kidney injury process and may be a potential marker of kidney injury.

Contrast-Enhanced Ultrasonography Value for Early Prediction of Delayed Graft Function in Renal Transplantation Patients.

Delayed graft function (DGF) is a common early complication after kidney transplantation. The aim of the present study was to evaluate the value of contrast-enhanced ultrasonography (CEUS) in the early prediction of DGF after kidney transplantation. A total of 89 renal transplant recipients were retrospectively enrolled and divided into DGF group or normal graft function (NGF) group according to the allograft function. Conventional Doppler ultrasound and CEUS examination data on the first postoperative day were collected and analyzed. The resistive indices of segmental and interlobar artery in the DGF group were significantly higher than those in the NGF group (0.71 ± 0.17 versus 0.63 ± 0.08, P=.006; 0.70 ± 0.16 versus 0.62 ± 0.08, P=.004, respectively). The patients experiencing DGF had significantly lower PI-c (14.7dB ± 6.1dB versus 18.5 dB ± 3.3dB, P=.001) and smaller AUC-c (779.8 ± 375.8dB·seconds versus 991.0 ± 211.7dB·seconds, P=.003), as well as significantly lower PI-m (12.6dB ± 5.9dB versus 15.9 dB ± 3.9dB, P=.006), shorter MTT-m (30.7 ± 9.4seconds versus 36.3 ± 7.1seconds, P=.01), and smaller AUC-m (P=.007). Multivariate analysis demonstrated that PI-c, AUC-c, and MTT-m were independent risk factors for DGF. The area under the receiver operating characteristic curve values of the combined predicted value (PI-c + MTT-m, PI-c + AUC-c + MTT-m) of DGF incidence were bigger than that of PI-c, AUC-c, or MTT-m. CEUS parameters of the cortex and medulla have a good value for an early prediction of DGF after renal transplantation.

Hyperspectral Imaging for Assessment of Initial Graft Function in Human Kidney Transplantation.

The aim of our study was to evaluate hyperspectral imaging (HSI) as a rapid, non-ionizing technique for the assessment of organ quality and the prediction of delayed graft function (DGF) in kidney transplantation after static cold storage (SCS, n = 20), as well as hypothermic machine perfusion (HMP, n = 18). HSI assessment of the kidney parenchyma was performed during organ preservation and at 10 and 30 min after reperfusion using the TIVITA® Tissue System (Diaspective Vision GmbH, Am Salzhaff, Germany), calculating oxygen saturation (StO2), near-infrared perfusion index (NIR), tissue haemoglobin index (THI), and tissue water index (TWI). Recipient and donor characteristics were comparable between organ preservation groups. Cold ischemic time was significantly longer in the HMP group (14.1 h [3.6–23.1] vs. 8.7h [2.2–17.0], p = 0.002). The overall presence of DGF was comparable between groups (HMP group n = 10 (55.6%), SCS group n = 10 (50.0%)). Prediction of DGF was possible in SCS and HMP kidneys; StO2 at 10 (50.00 [17.75–76.25] vs. 63.17 [27.00–77.75]%, p = 0.0467) and 30 min (57.63 [18.25–78.25] vs. 65.38 [21.25–83.33]%, p = 0.0323) after reperfusion, as well as NIR at 10 (41.75 [1.0–58.00] vs. 48.63 [12.25–69.50], p = 0.0137) and 30 min (49.63 [8.50–66.75] vs. 55.80 [14.75–73.25], p = 0.0261) after reperfusion were significantly lower in DGF kidneys, independent of the organ preservation method. In conclusion, HSI is a reliable method for intraoperative assessment of renal microperfusion, applicable after organ preservation through SCS and HMP, and predicts the development of DGF.

Risk Prediction for Delayed Allograft Function: Analysis of the Deterioration of Kidney Allograft Function (DeKAF) Study Data.

Delayed graft function (DGF) of a kidney transplant results in increased cost and complexity of management. For clinical care or a DGF trial, it would be ideal to accurately predict individual DGF risk and provide preemptive treatment. A calculator developed by Irish et al has been useful for predicting population but not individual risk. We analyzed the Irish calculator (IC) in the DeKAF prospective cohort (incidence of DGF = 20.4%) and investigated potential improvements. We found that the predictive performance of the calculator in those meeting Irish inclusion criteria was comparable with that reported by Irish et al. For cohorts excluded by Irish: (a) in pump-perfused kidneys, the IC overestimated DGF risk; (b) in simultaneous pancreas kidney transplants, the DGF risk was exceptionally low. For all 3 cohorts, there was considerable overlap in IC scores between those with and those without DGF. Using a modified definition of DGF-excluding those with single dialysis in the first 24 h posttransplant-we found that the calculator had similar performance as with the traditional DGF definition. Studying whether DGF prediction could be improved, we found that recipient cardiovascular disease was strongly associated with DGF even after accounting for IC-predicted risk. The IC can be a useful population guide for predicting DGF in the population for which it was intended but has limited scope in expanded populations (SPK, pump) and for individual risk prediction. DGF risk prediction can be improved by inclusion of recipient cardiovascular disease.

A Review of Current and Emerging Trends in Donor Graft-Quality Assessment Techniques.

The number of patients placed on kidney transplant waiting lists is rapidly increasing, resulting in a growing gap between organ demand and the availability of kidneys for transplantation. This organ shortage has forced medical professionals to utilize marginal kidneys from expanded criteria donors (ECD) to broaden the donor pool and shorten wait times for patients with end-stage renal disease. However, recipients of ECD kidney grafts tend to have worse outcomes compared to those receiving organs from standard criteria donors (SCD), specifically increased risks of delayed graft function (DGF) and primary nonfunction incidence. Thus, representative methods for graft-quality assessment are strongly needed, especially for ECDs. Currently, graft-quality evaluation is limited to interpreting the donor’s recent laboratory tests, clinical risk scores, the visual evaluation of the organ, and, in some cases, a biopsy and perfusion parameters. The last few years have seen the emergence of many new technologies designed to examine organ function, including new imaging techniques, transcriptomics, genomics, proteomics, metabolomics, lipidomics, and new solutions in organ perfusion, which has enabled a deeper understanding of the complex mechanisms associated with ischemia-reperfusion injury (IRI), inflammatory process, and graft rejection. This review summarizes and assesses the strengths and weaknesses of current conventional diagnostic methods and a wide range of new potential strategies (from the last five years) with respect to donor graft-quality assessment, the identification of IRI, perfusion control, and the prediction of DGF.

The value of cystatin C in predicting perioperative and long-term prognosis of renal transplantation

This study aimed to evaluate the value of cystatin C (Cys C) in predicting the perioperative and long-term prognosis of renal transplantation (RT). The clinical data of 198 RT recipients were collected. Blood samples were obtained daily until 7 d after transplantation and then discharge day to determine the serum levels of Cys C. The receiver-operating characteristic (ROC) analysis and the area under the curve (AUC) were used to determine the diagnostic accuracy of Cys C for delayed graft function (DGF). The presence of shrunken pore syndrome (SPS) with a cystatin C-based estimate of glomerular filtration rate less than 70% of a creatinine-based estimate, was also evaluated as a prognostic factor for the development of DGF. The serum Cys C levels of patients with DGF were higher than those of the non-DGF group. Cys C showed a higher AUC (0.928) in the ROC analysis than did sCr (0.862). Compared to the non-SPS group, there were more patients diagnosed with SPS in the DGF group (p < .05). The follow-up data showed that patients diagnosed with SPS had higher levels of sCr and Cys C compared to other patients, suggesting a poor long-term prognosis. Our findings suggest that Cys C is a sensitive indicator of renal function during the perioperative period. Cys C at a concentration of 4.9 mg/L had the highest sum of sensitivity and specificity for prediction of DGF, with a sensitivity of 0.889 and a specificity of 0.8. SPS is associated with the development of DGF and the poor long-term prognosis of RT.

Automated En Masse Machine Learning Model Generation Shows Comparable Performance as Classic Regression Models for Predicting Delayed Graft Function in Renal Allografts.

Several groups have previously developed logistic regression models for predicting delayed graft function (DGF). In this study, we used an automated machine learning (ML) modeling pipeline to generate and optimize DGF prediction models en masse. Deceased donor renal transplants at our institution from 2010 to 2018 were included. Input data consisted of 21 donor features from United Network for Organ Sharing. A training set composed of ~50%/50% split in DGF-positive and DGF-negative cases was used to generate 400 869 models. Each model was based on 1 of 7 ML algorithms (gradient boosting machine, k-nearest neighbor, logistic regression, neural network, naive Bayes, random forest, support vector machine) with various combinations of feature sets and hyperparameter values. Performance of each model was based on a separate secondary test dataset and assessed by common statistical metrics. The best performing models were based on neural network algorithms, with the highest area under the receiver operating characteristic curve of 0.7595. This model used 10 out of the original 21 donor features, including age, height, weight, ethnicity, serum creatinine, blood urea nitrogen, hypertension history, donation after cardiac death status, cause of death, and cold ischemia time. With the same donor data, the highest area under the receiver operating characteristic curve for logistic regression models was 0.7484, using all donor features. Our automated en masse ML modeling approach was able to rapidly generate ML models for DGF prediction. The performance of the ML models was comparable with classic logistic regression models.

Value of Plasmatic Villin-1 in the prediction of early graft dysfunction in kidney transplant recipients among Egyptian population

Abstract Background One of the main complications of kidney transplantation is delayed graft function (DGF). There has been enormous growing research interest in the potential for the rapidly expanding fields of medical technology to generate new biomarkers with the aim of early prediction of DGF and modifying its therapy accordingly. Villin-1 has been detected in urine of patients with AKI. Additionally, it is redistributed during AKI from the brush borders of the proximal tubular cells toward the basolateral membrane which positions villin-1 closer to the renal vasculature and suggests that it could be released in the blood as well, so can be as a novel biomarker for DGF. Methods Of 70 patients with end-stage renal disease with one or more risk factor for developing delayed graft function attending renal transplantation preparation clinics in ASUSH, IMC and NILE Badrawi hospital in Egypt from May 2016 to July 2019, 41 patients were eligible and assigned into two groups according to the correlation with the serum creatinine levels in the first two days post-transplantation after signed informed consent for participation in our study into group 1 (DGF group) and group 2 (NGF group). We measured the presence of villin-1 by ELISA technique (quantitative) in comparison to serum creatinine levels in plasma at the time of declamping (zero level), 1st, 3rd, 5th, 7th, 12th, 24th, 48th, 72th 96th, 120th hour post declamping, also samples from normal population and chronic kidney disease stage V patients have been collected to establish reference range guide for plasmatic villin-1 in both groups. Results Statistically significant differences were noted in the comparison between both groups as regard the plasmatic villin-1 levels at the same measurement points in both groups, also plasmatic villin-1 started to rise above its reference range in the ESRD patients at the 5th hour post declamping while its peak was at the 7th hour in the DGF group’s recipients then started to decrease after that but didn’t settle down between the reference range guide during the regular follow up of its level till the 120th hour post declamping. the reference range guide was from 0.08 and 0.35 ng\ml in males and from 0.055 to 0.35 ng\ml in females in normal population while from 0.38 and 1.4 ng\ml in males and from 0.28 to 1.1 ng\ml in females in the ESRD patients. Conclusions Plasmatic villin-1 is a promising novel biomarker for detection of early graft dysfunction in kidney transplant. Due to restrictions of the study design these observations need further confirmation by prospective studies.

Plasma Macrophage Migration Inhibitory Factor Predicts Graft Function Following Kidney Transplantation: A Prospective Cohort Study.

Background: Delayed graft function (DGF) is a common complication after kidney transplantation (KT) with a poor clinical outcome. There are no accurate biomarkers for the early prediction of DGF. Macrophage migration inhibitory factor (MIF) release during surgery plays a key role in protecting the kidney, and may be a potential biomarker for predicting post-transplant renal allograft recovery.Methods: Recipients who underwent KT between July 2020 and December 2020 were enrolled in the study. Plasma MIF levels were tested in recipients at different time points, and the correlation between plasma MIF and DGF in recipients was evaluated. This study was registered in the Chinese Clinical Trial Registry (ChiCTR2000035596).Results: Intraoperative MIF levels were different between immediate, slowed, and delayed graft function groups (7.26 vs. 6.49 and 5.59, P < 0.001). Plasma MIF was an independent protective factor of DGF (odds ratio = 0.447, 95% confidence interval [CI] 0.264–0.754, P = 0.003). Combining plasma MIF level and donor terminal serum creatinine provided the best predictive power for DGF (0.872; 95%CI 0.795–0.949). Furthermore, plasma MIF was significantly associated with allograft function at 1-month post-transplant (R2 = 0.42, P < 0.001).Conclusion: Intraoperative MIF, as an independent protective factor for DGF, has excellent diagnostic performance for predicting DGF and is worthy of further exploration.

A novel genomic model for predicting the likelihood of delayed graft function in DCD kidney transplantation.

BackgroundThe high incidence of delayed graft function (DGF) following kidney transplantation with donation after cardiac death allografts (DCD-KT) poses great challenges to transplant clinicians. This study aimed to explore the DGF-related biomarkers and establish a genomic model for DGF prediction specific to DCD KT.MethodsBy data mining a public dataset (GSE43974), the key DGF-related genes in DCD kidney biopsies taken after short-time reperfusion (45–60 min) were identified by differential expression analysis and a LASSO-penalized logistic regression model. Their coefficients for modeling were calculated by multivariate logistic regression. Receiver operating characteristic curves and a nomogram were generated to evaluate its predictive ability for DGF occurrence. Gene set enrichment analysis (GSEA) was performed to explore biological pathways underlying DGF in DCD KT.ResultsFive key DGF-related genes (CHST3, GOLPH3, ZBED5, AKR1C4, and ERRFI1) were first identified, all of which displayed good discrimination for DGF occurrence after DCD KT (all P<0.05). A five-mRNA-based risk score was further established and showed excellent predictive ability (AUC =0.9708, P<0.0001), which was obviously higher than that of the five genes alone. Eight DGF-related biological pathways in DCD kidneys, such as “arachidonic acid metabolism”, “lysosome”, “proximal tubule bicarbonate reclamation”, “glutathione metabolism”, were identified by GSEA (all P<0.05). Moreover, a convenient and visual nomogram based on the genomic risk score was also constructed and displayed high accuracy for DGF prediction specific to DCD KT.ConclusionsThe novel genomic model may effectively predict the likelihood of DGF immediately after DCD KT or even prior to transplantation in the context of normothermic machine perfusion in the future.

A prediction model of delayed graft function in deceased donor for renal transplant: a multi-center study from China

Background Kidneys obtained from deceased donors increase the incidence of delayed graft function (DGF) after renal transplantation. Here we investigated the influence of the risk factors of donors with DGF, and developed a donor risk scoring system for DGF prediction. Methods This retrospective study was conducted in 1807 deceased kidney donors and 3599 recipients who received donor kidneys via transplants in 29 centers in China. We quantified DGF associations with donor clinical characteristics. A donor risk scoring system was developed and validated using an independent sample set. Results The incidence of DGF from donors was 19.0%. Six of the donor characteristics analyzed, i.e., age, cause of death, history of hypertension, terminal serum creatinine, persistence of hypotension, and cardiopulmonary resuscitation (CPR) time were risk factors for DGF. A 49-point scoring system of donor risk was established for DGF prediction and exhibited a superior degree of discrimination. External validation of DGF prediction revealed area under the receiver-operating characteristic (AUC) curves of 0.7552. Conclusions Our study determined the deceased donor risk factors related to DGF after renal transplantation pertinent to the Chinese cohort. The scoring system developed here had superior diagnostic significance and consistency and can be used by clinicians to make evidence-based decisions on the quality of kidneys from deceased donors and guide renal transplantation therapy.

A PREDICTION MODEL OF DELAY GRAFT FUNCTION FROM DECEASED DONOR RISK FACTOR: A MULTI-CENTER STUDY FROM CHINA

Background: The quality of deceased donation seriously increased the incidence of delayed graft function (DGF) after renal transplantation. However, there is still no recognized donor evaluation system for DGF prediction in China. This study investigated the impact of donor risk factor on DGF and developed a donor risk score system for DGF prediction. Methods: A total of 1807 deceased donors and 3599 their donated renal transplant performed in 22 center of China were included this retrospective study. Associations of DGF with the clinical characteristics of donors were quantified by univariate analysis. A donor risk scoring system was developed by multivariate logistic regression and validated using an independent sample. Results: In this retrospective multi-center study, the incidence of DGF was 18.7% from donor aspect. Six donor characteristics and different layings including age, primary disease, hypertension history, terminal serum creatininer, and hypotension persistent and CPR tine were risk factors of DGF. A 49-point score system of donor risk was established for DGF prediction which showed a good degree of discrimination. The prediction model was achieved an area under the curve of 0.7423 (95% CI 0.69-0.79) in predicting DGF in internal validation. The AUROC was 0.7552 (95% CI0.67-0.84) in external validation by a independent one center sample compared to Irish model which demonstrated an AUROC of 0.6181 (95% CI 0.53-0.70). Conclusion: The study determined deceased donor risk factors related to DGF after renal transplantation in China and firstly develop a scoring system in line with the characteristics of the Chinese population for DGF prediction more effectively. The scoring system had good diagnostic significance and consistency and can be used by clinicians to make evidence-based decisions on the use of renal from deceased donor and guiding renal transplantation therapy.

Comprehensive assessment of deceased donor kidneys with clinical characteristics, pre-implant biopsy histopathology and hypothermic mechanical perfusion parameters is highly predictive of delayed graft function

Background Due to the current high demand for transplant tissue, an increasing proportion of kidney donors are considered extended criteria donors, which results in a higher incidence of delayed graft function (DGF) in organ recipients. Therefore, it is important to fully investigate the risk factors of DGF, and establish a prediction system to assess donor kidney quality before transplantation. Methods A total of 333 donation after cardiac death kidney transplant recipients were included in this retrospective study. Both univariate and multivariate analyses were used to analyze the risk factors of DGF occurrence. Receiver operating characteristic (ROC) curves were used to analyze the predictive value of variables on DGF posttransplant. Results The donor clinical scores, kidney histopathologic Remuzzi scores and hypothermic mechanical perfusion (HMP) parameters (flow and resistance index) were all correlated. 46 recipients developed DGF postoperatively, with an incidence of 13.8% (46/333). Multivariate logistic regression analysis of the kidney transplants revealed that the independent risk factors of DGF occurrence post-transplantation included donor score (OR = 1.12, 95% CI 1.06–1.19, p < 0.001), Remuzzi score (OR = 1.21, 95% CI 1.02–1.43, p = 0.029) and acute tubular injury (ATI) score (OR = 4.72, 95% CI 2.32–9.60, p < 0.001). Prediction of DGF with ROC curve showed that the area under the curve was increased to 0.89 when all variables (donor score, Remuzzi score, ATI score and HMP resistance index) were considered together. Conclusions Combination of donor clinical information, kidney pre-implant histopathology and HMP parameters provide a more accurate prediction of DGF occurrence post-transplantation than any of the measures alone.

The Systemic Immune-Inflammation Index Predicts Clinical Outcomes in Kidney Transplant Recipients.

Outcomes after kidney transplantation (KTx) remain limited by delayed graft function (DGF) and acute rejection. Non-invasive biomarkers may help identify patients at increased risk for these events. We examined the association between the systemic immune-inflammation index (SII), a novel inflammatory biomarker, and outcomes after KTx and evaluated its ability to predict post-transplant prognosis. Adult patients who underwent primary KTx at our institution between 2016-2019 were included. SII was calculated from pre-transplant complete blood counts as the ratio of the neutrophil count to the lymphocyte count multiplied by the platelet count. The cutoff between high and low SII was determined by maximizing the area under the curve. Multivariable logistic and Cox regression were used to identify factors associated with DGF and patient, rejection-free, and graft survival respectively. Overall, 378 KTx recipients were included; 224 (59.3%) had high SII. On unadjusted analysis, high SII was associated with reduced odds of DGF, and improved patient and rejection-free survival. After adjustment, high SII was independently associated with improved patient survival alone. Multivariable models incorporating SII performed well for the prediction of DGF (c-statistic=0.755) and patient survival (c-statistic=0.786), though rejection-free survival was more difficult to predict (c-statistic=0.635). SII demonstrated limited utility as an independent predictor of outcomes after KTx. However, in combination with other clinically relevant parameters, SII is a useful predictor of post-KTx prognosis. Validation of this novel inflammatory biomarker in a multi-institutional study is needed to further elucidate its practical applications in transplantation.

Comparison of urine and blood NGAL for early prediction of delayed graft function in adult kidney transplant recipients: a meta-analysis of observational studies

BackgroundNeutrophil gelatinase-assoicated lipocalin (NGAL) appears to be a promising proximal tubular injury biomarker for early prediction of delayed graft function (DGF) in kidney transplant recipients. However, its predictive values in urine and blood were varied among different studies. Here, we performed the meta-analysis to compare the predictive values of urine NGAL (uNGAL) and blood NGAL (bNGAL) for DGF in adult kidney transplant recipients.MethodsWe systematically searched Medline, Cochrane library and Embase for relevant studies from inception to May 2018. The summary receiver operating characteristic (SROC) curves, the pooled sensitivity, specificity and diagnostic odds ratio (DOR) were used to evaluate the prognostic performance of uNGAL and bNGAL for the identification of DGF.ResultsA total of 1036 patients from 14 eligible studies were included in the analysis. 8 studies focused on NGAL in urine and 6 reported NGAL in serum or plasma. The composite area under the ROC (AUC) for 24 h uNGAL was 0.91 (95% CI, 0.89–0.94) and the overall DOR for 24 h uNGAL was 24.17(95% CI, 9.94–58.75) with a sensitivity of 0.88 (95% CI, 0.75–0.94) and a specificity of 0.81 (95% CI, 0.68–0.89). The composite AUC for 24 h bNGAL was 0.95 (95% CI, 0.93–0.97) and the overall DOR for 24 h bNGAL was 43.11 (95% CI, 16.43–113.12) with a sensitivity of 0.91 (95% CI, 0.81–0.96) and a specificity of 0.86 (95% CI, 0.78–0.92).ConclusionsUrine and serum/plasma NGAL were valuable biomarkers for early identification of DGF in kidney transplantation. In addition, the bNGAL was superior to uNGAL in early prediction of DGF.

Furosemide Stress Test as a Predicting Biomarker for Delayed Graft Function in Kidney Transplantation

Background: Delayed graft function (DGF) could worsen early and long-term outcomes of kidney transplantation (KT). DGF is caused by several pre-transplantation and perioperative factors in both donors and recipients. At present, there are no biomarkers or tests during the immediate post-KT period that can accurately predict the development of DGF. Materials and methods: This prospective study was conducted in deceased donor KT (DDKT) at King Chulalongkorn Memorial Hospital, Thailand. All recipients underwent furosemide stress test (FST) by receiving a single dose of intravenous furosemide, 1.5 mg/kg at 3 h after allograft reperfusion. We determined the correlations between DGF (requiring dialysis within the first week after transplantation) and the values of urine volume recorded hourly after FST until 6 h, the parameters of postoperative dynamic tests, including resistive index (RI) of renal arteries and effective renal plasma flow (ERPF), and urine neutrophil gelatinase-associated lipocalin (NGAL). Results: Of the 59 total DDKT recipients enrolled, 24 developed DGF. The FST is a more accurate biomarker than urine NGAL, RI of renal arteries, and ERPF in the prediction of DGF. The 4-h urine volume less than 350 mL (FST non-responsive) was the best cut-off value in predicting DGF with 87.5% sensitivity, 82.9% specificity, and 82.5% accuracy. Multiple logistic regression analyses showed an odds ratio of 0.993 (0.986–0.999, p = 0.035) for the 4-h urine volume to predict DGF. Conclusions: The FST is a simple and accurate biomarker for predicting DGF in early post-KT period. Close monitoring and well prepared dialysis are suggested in patients with urine volume < 350 mL after 4 h of FST. The FST non-responsive patients could be the target for further DGF preventive intervention. ClinicalTrials.gov identifier: NCT03071536.

New Factors Predicting Delayed Graft Function: a Multi-Center Cohort Study of Kidney Donation After Brain Death Followed by Circulatory Death

Background/Aims: Delayed graft function (DGF) is a common complication following kidney transplantation adversely affecting graft outcomes. Donation after brain death followed by circulatory death (DBCD), a novel donation pattern, is expected to correlate with high incidence of DGF. However, little information is available about factors associated with DGF in DBCD. Methods: A total of 383 kidney transplants from DBCD donation in three institutions were enrolled. Associations of DGF with the clinical characteristics of recipients and donors were quantified. Results: In this retrospective multi-center study, the incidence of DGF was 19.3%. Lower incidence of DGF was found in recipients for whom antithymocyte globulin was used for induction (p < 0.05), which was an independent protective factor against DGF (odds ratio [OR] = 0.48; 95% CI 0.27-0.86). Two novel explicative variables were recognized as independent risk factors, including use of vasoactive drugs (OR = 3.15; 95% CI 1.39-7.14) and cardiopulmonary resuscitation (OR = 2.51; 95% CI 1.05-6.00), which contributed significantly to increased risk of DGF (p < 0.05). Prolonged warm ischemia time (> 18 min; OR = 2.42; 95% CI 1.36-4.32), was also predictive of DGF in DBCD. A prediction model was developed and achieved an area under the curve of 0.89 in predicting DGF when combined with reported parameters. Conclusion: The novel factors, confirmed for the first time in our study, will help to improve risk prediction of DGF and to determine optimal interventions to prevent DGF in clinical practice.

Urinary Tissue Inhibitor of Metalloproteinase and Insulin-like Growth Factor–7 as Early Biomarkers of Delayed Graft Function After Kidney Transplantation

BackgroundRecently, urinary tissue inhibitor of metalloproteinase–2 (TIMP-2) and insulin-like growth factor–7 (IGFBP-7), markers for G1 cell cycle arrest, have been identified and validated in predicting the development of acute kidney injury in critically ill patients. It is unknown, however, whether these two biomarkers could predict the development of delayed graft function (DGF) after kidney transplantation (KT). MethodsThis is a single-center, prospective, observational study. We enrolled 74 patients who underwent KT between August 2013 and December 2016. Urine sample were collected immediately after the operation. The primary outcome was development of DGF as defined by need for dialysis of more than 1 session within 7 days of KT. ResultsTwenty-three patients (31%) were diagnosed with DGF. In univariate analysis, kidneys from expanded criteria donors, higher donor serum creatinine, lower donor estimated glomerular filtration rate, antithymoglobulin exposure, neutrophil gelatinase associated lipocalin, and urinary [TIMP-2]·[IGFBP7] were significantly different between early graft function and DGF. However, in multivariate analysis adjusting other factors, deceased donor and urinary [TIMP-2]·[IGFBP7] at 0 hours post-transplantation could predict the development of DGF. The receiver operating characteristic curve for prediction of DGF showed an area under the curve of 0.867 (sensitivity 0.86, specificity 0.71) for a cutoff value of 1.39. ConclusionsOur results indicate that urine [TIMP-2]·[IGFBP7] immediately after transplantation could be an early, predictive biomarker of DGF in kidney transplantation.

Evaluation of biomarkers of cell cycle arrest and inflammation in prediction of dialysis or recovery after kidney transplantation.

Early prediction of delayed graft function (DGF) after kidney transplantation would facilitate patient management. Cell cycle arrest and inflammation are implicated in the pathogenesis of DGF. We assessed the utility of two novel acute kidney injury (AKI) biomarkers, urinary tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7), and five inflammatory markers to predict DGF following deceased-donor kidney transplantation. Serial urine concentrations of TIMP-2 and IGFBP7 were measured immediately after transplantation in 56 recipients along with vascular endothelial growth factor-A (VEGF-A), macrophage migration inhibitory factor (MIF), monocyte chemotactic protein-1 (MCP-1), trefoil factor 3 (TFF3) and chemokine (C-X-C) ligand 16 (CXCL16). Delayed graft function (DGF) was defined as requirement for dialysis within 7days. Integrated discrimination improvement analysis was used to evaluate whether these biomarkers enhanced prediction of DGF independently of a validated clinical risk prediction model. DGF occurred in 22 patients (39%). At 4h after kidney reperfusion, the area under the receiver operator characteristic curve (AUC) for VEGF-A was good (AUC>0.80); for TIMP-2, IGFBP7 and [TIMP-2]×[IGFBP7] fair (AUCs 0.70-0.79); and for MIF, MCP-1, TFF3 and CXCL16 poor (AUC<0.70). Only TIMP-2 and VEGF significantly enhanced the DGF prediction at 4 and 12h. The cell cycle arrest marker urinary TIMP-2 and the inflammatory biomarker VEGF-A are potentially useful adjuncts to clinical data for early prediction of DGF.