Utility of a mathematical nomogram to predict delayed graft function: a single-center experience

Postoperative day 1 serum cystatin C level predicts postoperative delayed graft function after kidney transplantation

Background

Delayed graft function (DGF) commonly occurs after kidney transplantation, but no clinical predictors for guiding post-transplant management are available.

Materials and methods

Data including demographics, surgery, anesthesia, postoperative day 1 serum cystatin C (S-CysC) level, kidney functions, and postoperative complications in 603 kidney transplant recipients who met the enrollment criteria from January 2017 to December 2018 were collected and analyzed to form the Intention-To-Treat (ITT) set. All perioperative data were screened using the least absolute shrinkage and selection operator. The discrimination, calibration, and clinical effectiveness of the predictor were verified with area under curve (AUC), calibration plot, clinical decision curve, and impact curve. The predictor was trained in Per-Protocol set, validated in the ITT set, and its stability was further tested in the bootstrap resample data.

Result

Patients with DGF had significantly higher postoperative day 1 S-CysC level (4.2 ± 1.2 vs. 2.8 ± 0.9 mg/L; P < 0.001), serum creatinine level (821.1 ± 301.7 vs. 554.3 ± 223.2 μmol/L; P < 0.001) and dialysis postoperative (74 [82.2%] vs. 25 [5.9%]; P < 0.001) compared with patients without DGF. Among 41 potential predictors, S-CysC was the most effective in the parsimonious model, and its diagnostic cut-off value was 3.80 mg/L with the risk score (OR, 13.45; 95% CI, 8.02–22.57; P < 0.001). Its specificity and sensitivity indicated by AUC was 0.832 (95% CI, 0.779–0.884; P < 0.001) with well fit calibration. S-CysC yielded up to 50% of clinical benefit rate with 1:4 of cost/benefit ratio.

Conclusion

The postoperative day 1 S-CysC level predicts DGF and may be used as a predictor of DGF but warrants further study.

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

BACKGROUND

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.

METHODS

We analyzed the Irish calculator (IC) in the DeKAF prospective cohort (incidence of DGF = 20.4%) and investigated potential improvements.

RESULTS

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.

CONCLUSIONS

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.

Prediction of delayed graft function using different scoring algorithms: A single-center experience

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.

Wedge Versus Core Biopsy at Time Zero: Which Provides Better Predictive Value for Delayed Graft Function With the Remuzzi Histological Scoring System?

BACKGROUND

Histopathological features on time-zero renal biopsies correlate with graft outcome after renal transplantation. With increasing numbers of marginal donors, assessment of pre-implantation graft quality is essential. The clinician's choice of wedge or core biopsy is performed without evidence of efficacy or safety. This study aims to compare the information derived from wedge biopsy versus core biopsy.

METHODS

Prospective evaluation of 37 wedge biopsies and 30 core biopsies was performed. Histopathological data were collected on number of glomeruli and arterioles observed, and Remuzzi scoring for glomerulosclerosis, tubular atrophy, interstitial fibrosis, and arteriolar narrowing was performed. Clinical data on delayed graft function (DGF) were also collated. Sensitivity, specificity, and positive and negative predictive values for DGF were compared.

RESULTS

Patient demographics between the two cohorts were comparable. No complications of biopsies occurred; 81% of wedge biopsies versus 50% of core biopsies had >10 glomeruli (P = .01), whereas 32% of wedge biopsies and 57% of core biopsies had >2 arterioles (P = .02). Wedge biopsies were more likely to identify pathology with more glomerulosclerosis, tubular atrophy (P < .01), and interstitial fibrosis (P < .01). There was a non-significant trend toward high Remuzzi scores in wedge biopsy (22% versus 7% with Remuzzi ≥ 4; P = .12). The sensitivity and positive predictive value of Remuzzi ≥ 4 for predicting DGF was better on wedge biopsy (45.5% versus 0%; P < .01 and 62.5% versus 0%; P < .01, respectively).

CONCLUSIONS

Wedge biopsies were safe and superior to core biopsies for identifying clinically significant histopathological findings on pre-implantation renal biopsy. We believe that the wedge biopsy is the method of choice for time-zero biopsies.

Intraoperative biomarkers in renal transplantation

The emerging need for biomarkers in the management of renal transplantation is highlighted by the severity of related complications such as acute renal failure and ischaemia/reperfusion injury (IRI) and by the increasing efforts to identify novel markers of these events to predict and monitor delayed graft function (DGF) and long-term outcome. In clinical studies candidate markers such as kidney injury molecule-1, neutrophil gelatinase-associated lipocalin and interleukin-18 have been demonstrated to be valid biomarkers with high predictive value for DFG in a post-transplant setting. However, studies investigating biomarkers for early diagnosis of IRI and assumable DGF as well as identification of potential graft recipients at increased risk at the time point of transplantation lack further confirmation and translation into clinical practice. This review summarizes the current literature on the value of IRI biomarkers in outcome prediction following renal transplantation as well their capacity as surrogate end points from an intraoperative perspective.

Preoperative assessment of the deceased-donor kidney: from macroscopic appearance to molecular biomarkers

Variation in deceased-donor kidney quality can significantly affect outcomes after kidney transplantation. Suboptimal organ selection for a given recipient can result in primary nonfunction, premature graft failure, or inappropriate discard of a suitable organ. Appraisal and appropriate selection of deceased-donor kidneys for use in transplantation is therefore critical. A number of predictive tools have been developed to assist the transplant team in evaluating the suitability of a deceased-donor kidney for transplantation to a given recipient. These include stratification of donors into "standard-" or "expanded-criteria" categories based on clinical parameters, pre-implantation biopsy scores, donor risk scores, machine perfusion characteristics, functional kidney weight, donor biomarkers and molecular diagnostic tools, ex vivo viability assessment using postmortem normothermic perfusion, and overall macroscopic appraisal by the surgical team. Consensus as to the role and predictive value of each of these tools is lacking and clinical practice regarding evaluation and selection of kidneys varies considerably.In this review, we seek to critically appraise the literature and evaluate the levels of evidence for tools used to assess deceased-donor kidneys. Although a plethora of appraisal tools exist, very few demonstrate desirable predictive power to be useful in clinical decision-making. Further research using large, well-designed prospective studies is urgently needed to advance this important field of transplantation science.

The impact of vascular anastomosis time on early kidney transplant outcomes

Background

Most studies have found cold ischemic time to be an important predictor of delayed graft function in kidney transplantation. Relatively less is known about the warm time associated with vascular anastomosis and early outcomes.

Methods

A retrospective cohort of 298 consecutive solitary deceased donor kidney recipients from January 2006 to August 2012 was analyzed to examine the association between anastomosis time and delayed graft function (need for dialysis) and length of hospital stay.

Results

Delayed graft function (DGF) was observed in 56 patients (18.8%). The median anastomosis time was 30 minutes (interquartile range 24, 45 minutes). Anastomosis time was independently associated with DGF in a multivariable, binary logistic regression analysis (odds Ratio (OR) 1.037 per minute, 95% CI 1.016, 1.057, P = 0.001). An anastomosis time >29 minutes was also associated with a 3.5 fold higher (OR 3.5, 95% CI 1.6, 7.3, P = 0.001) risk of DGF. Median days in hospital was 9 (interquartile range 7, 14 days). Every 5 minutes of longer anastomosis time (0.20 days per minute, 95% CI 0.13, 0.27, P <0.001) was associated with 1 extra day in hospital in a multivariable linear regression model. An anastomosis time >29 minutes was associated with 3.8 (95% CI 1.6, 6.0, P <0.001) more days in hospital.

Conclusion

Anastomosis time may be an underappreciated but modifiable variable in dictating use of hospital resources. The impact of anastomosis time on longer term outcomes deserves further study.

Prediction of delayed graft function by means of a novel web-based calculator: a single-center experience

Renal failure persisting after renal transplant is known as delayed graft function (DGF). DGF predisposes the graft to acute rejection and increases the risk of graft loss. In 2010, Irish et al. developed a new model designed to predict DGF risk. This model was used to program a web-based DGF risk calculator, which can be accessed via http://www.transplantcalculator.com . The predictive performance of this score has not been tested in a different population. We analyzed 342 deceased-donor adult renal transplants performed in our hospital. Individual and population DGF risk was assessed using the web-based calculator. The area under the ROC curve to predict DGF was 0.710 (95% CI 0.653-0.767, p < 0.001). The "goodness-of-fit" test demonstrates that the DGF risk was well calibrated (p = 0.309). Graft survival was significantly better for patients with a lower DGF risk (5-year survival 71.1% vs. 60.1%, log rank p = 0.036). The model performed well with good discrimination ability and good calibration to predict DGF in a single transplant center. Using the web-based DGF calculator, we can predict the risk of developing DGF with a moderate to high degree of certainty only by using information available at the time of transplantation.

Prediction of delayed graft function after renal transplantation

INTRODUCTION

Delayed graft function (DGF), defined as the need for dialysis during the first week after renal transplantation, is an important adverse clinical outcome. A previous model relied on 16 variables to quantify the risk of DGF, thereby undermining its clinical usefulness. We explored the possibility of developing a simpler, equally accurate and more user-friendly paradigm for renal transplant recipients from deceased donors.

METHODS

Logistic regression analyses addressed the occurrence of DGF in 532 renal transplant recipients from deceased donors. Predictors consisted of recipient age, gender, race, weight, number of HLA-A, HLA-B and HLA-DR mismatches, maximum and last titre of panel reactive antibodies, donor age and cold ischemia time. Accuracy was quantified with the area under the curve. Two hundred bootstrap resamples were used for internal validation.

RESULTS

Delayed graft function occurred in 103 patients (19.4%). Recipient weight (p < 0.001), panel of reactive antibodies (p < 0.001), donor age (p < 0.001), cold ischemia time (p = 0.005) and HLA-DR mismatches (p = 0.05) represented independent predictors. The multivariable nomogram relying on 6 predictors was 74.3% accurate in predicting the probability of DGF.

CONCLUSION

Our simple and user-friendly model requires 6 variables and is at least equally accurate (74%) to the previous nomogram (71%). We demonstrate that DGF can be accurately predicted in different populations with this new model.

Post-transplant nuclear renal scans correlate with renal injury biomarkers and early allograft outcomes

BACKGROUND

Clinical- and histopathology-based scores are limited predictors of allograft outcome. In addition, more objective markers of early transplant function are needed to identify and validate biomarkers and predictive scores. We evaluated existing scores and transcriptome biomarkers of kidney injury as predictors of early transplant function measured by renal scan.

METHODS

Clinical, histopathologic and transcriptome data were collected in 143 consecutive kidney transplant recipients. A post-operative renal scan was performed within 48 h. Prediction scores for early outcomes were calculated.

RESULTS

Patients were stratified into three groups by renal scan: normal, mild-to-moderate or severe dysfunction. Kidneys with severe dysfunction were more often from deceased donors (P < 0.001), had greater HLA antigen mismatches (P < 0.001), were transplanted into older recipients (P = 0.040), had lower urine output during the first 8 h (P < 0.001), higher Day 7 serum creatinine (P < 0.001) and higher incidence of delayed graft function (P < 0.001). Clinical- and pathology-based scores did not discriminate between scan groups. In contrast, the overall transcriptome (P < 0.001) and transcripts of preselected acute kidney injury (AKI) genes were significantly different between the groups, with kidney injury molecule 1 (P = 0.001) and neutrophil gelatinase-associated lipocalin (P = 0.002) being most highly expressed and genes associated with glutathione metabolism (GSTA1, 3 and 4) most down-regulated in kidneys with subsequent severe dysfunction.

CONCLUSIONS

Renal scans reflect early transplant function and allow for a more objective assessment of scores predicting early outcome and for identification of biomarkers. The study shows that transcript levels of AKI genes correlate better with renal scans than clinical- or histopathology-based scores.

Assessment of kidney organ quality and prediction of outcome at time of transplantation

The critical importance of donor organ quality, i.e., number of surviving nephrons, ability to withstand injury, and capacity for repair in determining short- and long-term outcomes is becoming increasingly clear. This review provides an overview of studies to assess donor kidney quality and subsequent transplant outcomes based on clinical pathology and transcriptome-based variables available at time of transplantation. Prediction scores using clinical variables function when applied to large data sets but perform poorly for the individual patient. Histopathology findings in pre-implantation or post-reperfusion biopsies help to assess structural integrity of the donor kidney, provide information on pre-existing donor disease, and can serve as a baseline for tracking changes over time. However, more validated approaches of analysis and prospective studies are needed to reduce the number of discarded organs, improve allocation, and allow prediction of outcomes. Molecular profiling detects changes not seen by morphology or captured by clinical markers. In particular, molecular profiles provide a quantitative measurement of inflammatory burden or immune activation and reflect coordinated changes in pathways associated with injury and repair. However, description of transcriptome patterns is not an end in itself. The identification of predictive gene sets and the application to an individualized patient management needs the integration of clinical and pathology-based variables, as well as more objective reference markers of transplant function, post-transplant events, and long-term outcomes.

The deceased donor score system in kidney transplants from deceased donors after cardiac death

SUMMARY

A clinical score to identify kidneys from donors after cardiac death (DCD) with a high risk of dysfunction following transplantation could be a useful tool to guide the introduction of new algorithms for the preservation of these organs and improve their outcome after transplantation. We investigated whether the deceased donor score (DDS) system could identify DCD kidneys with higher risk of early post-transplant dysfunction. The DDS was validated in a cohort of 168 kidney transplants from donors after brain death (DBD) and then applied to a cohort of 56 kidney transplants from DCD. In the DBD cohort, the DDS grade predicted the incidence of delayed graft function (DGF) and levels of serum creatinine at 3 and 12 months post-transplant. Similarly, in the DCD cohort, the DDS grade correlated with DGF and also predicted the levels of serum creatinine at 3 and 12 months. Interestingly, the DDS identified a subgroup of marginal DCD kidneys in which minimization of cold ischemia time produced better early clinical outcome. These results highlight the impact of early interventions on clinical outcome of marginal DCD kidneys and open the possibility of using the DDS to identify those kidneys that may benefit most from therapeutic interventions before transplantation.

"Nature versus nurture" study of deceased-donor pairs in kidney transplantation

Donor characteristics such as age and cause of death influence the incidence of delayed graft function (DGF) and graft survival; however, the relative influence of donor characteristics ("nature") versus transplant center characteristics ("nurture") on deceased-donor kidney transplant outcomes is unknown. We examined the risks for DGF and allograft failure within 19,461 recipient pairs of the same donor's kidneys using data from the US Renal Data System. For the 11,894 common-donor pairs transplanted at different centers, a recipient was twice as likely to develop DGF when the recipient of the contralateral kidney developed DGF (odds ratio [OR] 2.05; 95% confidence interval [CI] 1.82 to 2.30). Similarly, for 7567 common-donor pairs transplanted at the same center, the OR for DGF was 3.02 (95% CI 2.62 to 3.48). For pairs transplanted at the same center, there was an additional 42% risk for DGF compared with pairs transplanted at different centers. After adjustment for DGF, the within-pair ORs for allograft failure by 1 yr were 1.92 (95% CI 1.33 to 2.77) and 1.77 (95% CI 1.25 to 2.52) for recipients who underwent transplantation at the same center and different centers, respectively. These data suggest that both unmeasured donor characteristics and transplant center characteristics contribute to the risk for DGF and that the former also contribute significantly to allograft failure.

Prediction of graft survival of living-donor kidney transplantation: nomograms or artificial neural networks?

BACKGROUND

An artificial neural networks (ANNs) model was developed to predict 5-year graft survival of living-donor kidney transplants. Predictions from the validated ANNs were compared with Cox regression-based nomogram.

METHODS

Out of 1900 patients with living-donor kidney transplant; 1581 patients were used for training of the ANNs (training group), the remainder 319 patients were used for its validation (testing group). Many variables were correlated with the graft survival by univariate analysis. Significant ones were used for ANNs construction of a predictive model. The same variables were subjected to a multivariate statistics using Cox regression model; their result was the basis of a nomogram construction. The ANNs predictive model and the nomogram were used to predict the graft survival of the testing group. The predicted probability(s) was compared with the actual survival estimates.

RESULTS

The ANNs sensitivity was 88.43% (95% confidence interval [CI] 86.4-90.3), specificity was 73.26% (95% CI 70-76.3), and predictive accuracy was 88% (95% CI 87-90) in the testing group, whereas nomogram sensitivity was 61.84% (95% CI 50-72.8) with 74.9% (95% CI 69-80.2) specificity and predictive accuracy was 72% (95% CI 67-77). The positive predictive value of graft survival was 82.1% and 43.5% for the ANNs and Cox regression-based nomogram, respectively, and the negative predictive value was 82% and 86.3% for the ANNs and Cox regression-based nomogram, respectively. Predictions by both models fitted well with the observed findings.

CONCLUSIONS

These results suggest that ANNs was more accurate and sensitive than Cox regression-based nomogram in predicting 5-year graft survival.

Arteriolar hyalinization predicts delayed graft function in deceased donor renal transplantation

Delayed renal graft function (DGF) remains a largely unpredictable and burdensome consequence of deceased donor renal transplantation. There is growing evidence that histologic and molecular analyses of baseline donor kidney biopsies can predict both short- and long-term graft outcome. We performed histologic analyses of 172 preimplantation kidney biopsies to determine reliable histologic risk factors for DGF. Fifty-six recipients presented a DGF (incidence 32%). Univariate analysis revealed that arteriolar hyalinization (P=0.019), arterial intima fibrosis (0.004), donor age (P=0.001), duration of cold ischemia time (P=0.001), and recipient age (P=0.001) were significantly associated with DGF. Multivariate analysis revealed that the only independent histologic factor was arteriolar hyalinization (P=0.036). This histologic predictive factor, together with previously identified clinical risk factors, could guide clinical decisions regarding use, allocation, or immunosuppression protocols for minimization of DGF.

Association between primary graft dysfunction among lung, kidney and heart recipients from the same multiorgan donor

Even organs from an ideal donor will occasionally develop primary graft dysfunction (PGD) causing a significant morbidity and mortality after transplantation. It is likely that this situation represents subtle undetectable levels of ongoing donor organ dysfunction. The aim of this study is to investigate the association of PGD between lung, kidney and heart recipients from the one donor. From 202 multiorgan donors, contributed 231 consecutive lung transplants at the Alfred Hospital, 378 kidney and 114 heart transplants were subsequently performed at multiple centers across Australia and New Zealand. Eight hundred seventy-five organs were used for 723 transplants. The incidence of PGD after lung, kidney and heart transplants was 20% (47/231), 24% (92/378) and 20% (23/114), respectively. In paired single organ recipients, PGD in one of the pair was a significant risk factor for the development of PGD in the other [lung: odds ratio = 5.63 (1.72-18.43), p = 0.004; kidney: odds ratio = 3.19 (1.90-5.35), p < 0.0001]. In multivariate analysis, same donor heart PGD [3.37 (1.19-9.50), p = 0.02] was an independent risk factor for lung PGD and same donor lung PGD was significant risk factor for kidney PGD [1.94 (1.01-3.73), p = 0.04], if the PGD status of the paired kidney was not known. There was a significant association for the development of PGD across different organs transplanted from the same donor.

The transcriptome of the implant biopsy identifies donor kidneys at increased risk of delayed graft function

Improved assessment of donor organ quality at time of transplantation would help in management of potentially usable organs. The transcriptome might correlate with risk of delayed graft function (DGF) better than conventional risk factors. Microarray results of 87 consecutive implantation biopsies taken postreperfusion in 42 deceased (DD) and 45 living (LD) donor kidneys were compared to clinical and histopathology-based scores. Unsupervised analysis separated the 87 kidneys into three groups: LD, DD1 and DD2. Kidneys in DD2 had a greater incidence of DGF (38.1 vs. 9.5%, p < 0.05) than those in DD1. Clinical and histopathological risk scores did not discriminate DD1 from DD2. A total of 1051 transcripts were differentially expressed between DD1 and DD2, but no transcripts separated DGF from immediate graft function (adjusted p < 0.01). Principal components analysis revealed a continuum from LD to DD1 to DD2, i.e. from best to poorest functioning kidneys. Within DD kidneys, the odds ratio for DGF was significantly increased with a transcriptome-based score and recipient age (p < 0.03) but not with clinical or histopathologic scores. The transcriptome reflects kidney quality and susceptibility to DGF better than available clinical and histopathological scoring systems.