Comparison of the predictive performance of eGFR formulae for mortality and graft failure in renal transplant recipients. Transplantation. 2009;87:384-392. [PMID: 19202443 DOI: 10.1097/tp.0b013e31819004a1]

Multivariable Predictors of Poorer Renal Function Among 1119 Deceased Donor Kidney Transplant Recipients During the First Year Post-Transplant, With a Particular Focus on the Influence of Individual KDRI Components and Donor AKI

Given our desire to reduce kidney transplant waiting times by utilizing more difficult-to-place ("higher-risk") DD kidneys, we wanted to better understand post-transplant renal function among 1119 adult DD recipients consecutively transplanted during 2016-2019. Stepwise linear regression of eGFR (CKD-EPI formula) at 3-, 6-, and 12-months post-transplant (considered as biomarkers for longer-term outcomes), respectively, was performed to determine the significant multivariable baseline predictors, using a type I error ≤ 0.01 to avoid spurious/weak associations. Three unfavorable characteristics were selected as highly significant in all three models: Older DonorAge (yr) (p < 0.000001), Longer StaticColdStorage Time (hr) (p < 0.000001), and Higher RecipientBMI (p ≤ 0.00003). Other significantly unfavorable characteristics included: Shorter DonorHeight (cm) (p ≤ 0.00001), Higher Natural Logarithm {Initial DonorCreatinine} (p ≤ 0.001), Longer MachinePerfusion Time (p ≤ 0.003), Greater DR Mismatches (p = 0.01), DonorHypertension (p ≤ 0.004), Recipient HIV+ (p ≤ 0.006), DCD Kidney (p = 0.002), Cerebrovascular DonorDeath (p = 0.01), and DonorDiabetes (p = 0.01). Variables not selected into any model included DonorAKI Stage (p ≥ 0.24), Any DonorAKI (p ≥ 0.04), and five KDRI components: two DonorAge splines at 18 years (p ≥ 0.52) and 50 years (p ≥ 0.28), BlackDonor (p ≥ 0.08), DonorHCV+ (p ≥ 0.06), and DonorWeight spline at 80 kg (p ≥ 0.03), indicating that DonorAKI and the weaker KDRI components have little, if any, prognostic impact on renal function during the first 12 months post-transplant. Additionally, biochemical determinations with skewed distributions such as DonorCreatinine are more accurately represented by natural logarithmic transformed values. In conclusion, one practical takeaway is that donor AKI may be ignored when evaluating DD risk.

Early Post-Transplant Renal Recovery Trajectory and Trajectory Velocity Functions Are Predictors of Estimated GFR at 1 Year: A Functional Data Analysis Approach

INTRODUCTION

Kidney function at 1-year post-transplant is an indicator of long-term graft function. Using functional data analysis (FDA), we evaluate the relationship between early renal recovery trajectories and kidney function at 1 year.

METHODS

We analyzed 1748 adults who underwent deceased-donor kidney transplantation between 2010 and 2021. Renal recovery trajectory functions were derived from longitudinal inverse creatinine values. Functional linear regression models were used to evaluate how well early (<90 days) renal recovery trajectory functions, and their rate of change explained 1-year eGFR. The explanatory power of the functional regression models was compared to results from ordinary least squares models, which used cross-sectional inverse creatinine values and linear slopes. Models were adjusted for age, sex, kidney donor profile index (KDPI), delayed graft function (DGF), race, body mass index (BMI), rejection, diabetes, hypertension, cytomegalovirus (CMV) serostatus risk, index admission length of stay, and immunosuppression agent. The R2 coefficient quantified the 1-year eGFR variation explained by model variables.

RESULTS

Adjusted functional linear models with renal recovery trajectory and trajectory velocity functions as independent variables explained 68% (65, 71), 70% (67, 74), 70% (66, 74), 70% (66, 75), and 73% (69, 79) of the variation in 1-year eGFR by 7, 14, 30, 60, and 90 days, respectively. By comparison, the ordinary least squares linear models explained a maximum of 69% of the variation in 1-year eGFR at 90 days.

CONCLUSION

Renal recovery patterns captured as continuous functions as early as 14 days are predictive of renal function at 1 year and may enable early personalized care of recipients at increased risk of poor graft function.

Factors Associated With Long-term Kidney Allograft Survival: A Contemporary Analysis of the UNOS Database

BACKGROUND

Various clinicopathologic markers, such as 1-year serum creatinine (Cr), have been used to prognosticate kidney allografts after transplantation. However, a contemporary analysis of their relationship with long-term graft survival is lacking. This study aimed to analyze recent data on the association of prognostic factors with kidney allograft survival in patients who underwent transplantation in the modern era.

METHODS

Adult kidney-transplant recipients in the UNOS database (2008-2020) were identified. Living and deceased donor allografts were analyzed separately and stratified by 1-year serum Cr level: ≤1.0, 1.0 to 1.5, 1.5 to 2.0, and >2.0 mg/dL. Time-to-event analysis was performed with long-term death-censored graft survival as the primary outcome. In addition, factors associated with raised 1-year serum Cr and with long-term allograft failure were identified.

RESULTS

174,547 patients were included. Ten-year survival decreased with increasing 1-year creatinine, and these trends persisted on adjusted analysis for both living donor (Cr ≤ 1.0 mg/dL: reference; Cr 1.0-1.5 mg/dL aHR = 1.77 [1.59-1.96]; Cr 1.5-2.0 mg/dL aHR = 3.24 [2.89-3.64] and; Cr > 2.0 mg/dL aHR = 9.78, [8.64-11.07], P < .01) as well as deceased donor allografts (Cr ≤ 1.0 mg/dL: reference; Cr 1.0-1.5 mg/dL aHR = 1.74 [1.63-1.86]; Cr 1.5-2.0 mg/dL aHR = 3.06 [2.84-3.30] and; Cr > 2.0 mg/dL aHR = 8.51, [7.89-9.18], P < .01).

CONCLUSION

These results characterize the association between 1-year serum creatinine levels and other clinicopathologic factors with long-term kidney allograft survival. We demonstrate the ability of prognostic factors to stratify patients by risk of graft failure in a contemporary patient cohort that is representative of current practice and outcomes.

Machine Learning Model to Predict Graft Rejection After Kidney Transplantation

BACKGROUND

There are few predictive studies about early posttransplant outcomes taking into account baseline and posttransplant variables. The objective of this study was to create a predictive model for 30-day graft rejection using machine learning techniques.

METHODS

Retrospective study with 1255 patients undergoing transplant from living and deceased donors at a tertiary health service in Brazil. Recipient, donor, transplantation, and postoperative period data were collected from physical and electronic records. We split the data into derivation (training) and validation (test) datasets. Five supervised machine learning algorithms were developed with this subset of variables in the training set: Simple Logistic Regression, Lasso, Multilayer Perceptron, XGBoost, and Light GBM.

RESULTS

There were 147 (12.48%) cases of graft rejection within 30 days of transplantation. The best model was XGBoost (accuracy, 0.839; receiver operating characteristic area under the curve, 0.715; precision, 0.900). The model showed that deceased donor transplantation, glomerulopathy as an underlying disease, and donor's use of vasoactive drugs had more than 20% importance as rejection risk factors. The variables with the greatest predictive values were thymoglobulin induction and delayed graft function.

CONCLUSIONS

We fitted a machine learning model to predict 30-day graft rejection after kidney transplantation that reaches a higher accuracy and precision. Machine learning models could contribute to predicting kidney survival using nontraditional approaches.

Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction

Machine learning (ML) models have recently shown potential for predicting kidney allograft outcomes. However, their ability to outperform traditional approaches remains poorly investigated. Therefore, using large cohorts of kidney transplant recipients from 14 centers worldwide, we developed ML-based prediction models for kidney allograft survival and compared their prediction performances to those achieved by a validated Cox-Based Prognostication System (CBPS). In a French derivation cohort of 4000 patients, candidate determinants of allograft failure including donor, recipient and transplant-related parameters were used as predictors to develop tree-based models (RSF, RSF-ERT, CIF), Support Vector Machine models (LK-SVM, AK-SVM) and a gradient boosting model (XGBoost). Models were externally validated with cohorts of 2214 patients from Europe, 1537 from North America, and 671 from South America. Among these 8422 kidney transplant recipients, 1081 (12.84%) lost their grafts after a median post-transplant follow-up time of 6.25 years (Inter Quartile Range 4.33-8.73). At seven years post-risk evaluation, the ML models achieved a C-index of 0.788 (95% bootstrap percentile confidence interval 0.736-0.833), 0.779 (0.724-0.825), 0.786 (0.735-0.832), 0.527 (0.456-0.602), 0.704 (0.648-0.759) and 0.767 (0.711-0.815) for RSF, RSF-ERT, CIF, LK-SVM, AK-SVM and XGBoost respectively, compared with 0.808 (0.792-0.829) for the CBPS. In validation cohorts, ML models' discrimination performances were in a similar range of those of the CBPS. Calibrations of the ML models were similar or less accurate than those of the CBPS. Thus, when using a transparent methodological pipeline in validated international cohorts, ML models, despite overall good performances, do not outperform a traditional CBPS in predicting kidney allograft failure. Hence, our current study supports the continued use of traditional statistical approaches for kidney graft prognostication.

Shifting Clinical Trial Endpoints in Kidney Transplantation: The Rise of Composite Endpoints and Machine Learning to Refine Prognostication

The measurement of outcomes in kidney transplantation has been more accurately documented than almost any other surgical procedure result in recent decades. With significant improvements in short- and long-term outcomes related to optimized immunosuppression, outcomes have gradually shifted away from conventional clinical endpoints (ie, patient and graft survival) to surrogate and composite endpoints. This article reviews how outcomes measurements have evolved in the past 2 decades in the setting of increased data collection and summarizes recent advances in outcomes measurements pertaining to clinical, histopathological, and immune outcomes. Finally, we discuss the use of composite endpoints and Bayesian concepts, specifically focusing on the integrative box risk prediction score, in conjunction with machine learning to refine prognostication.

Pretransplant characteristics of kidney transplant recipients that predict posttransplant outcome

Better characterization of the potential kidney transplant recipient using novel biomarkers, for example, pretransplant plasma endotrophin, will lead to improved outcome after transplantation. This mini-review will focus on current knowledge about pretransplant recipients’ characteristics, biomarkers, and immunology. Clinical characteristics of recipients including age, obesity, blood pressure, comorbidities, and estimated survival scores have been introduced for prediction of recipient and allograft survival. The pretransplant immunologic risk assessment include histocompatibility leukocyte antigens (HLAs), anti-HLA donor-specific antibodies, HLA-DQ mismatch, and non-HLA antibodies. Recently, there has been the hope that pretransplant determination of markers can further improve the prediction of posttransplant complications, both short-term and long-term outcomes including rejections, allograft loss, and mortality. Higher pretransplant plasma endotrophin levels were independently associated with posttransplant acute allograft injury in three prospective European cohorts. Elevated numbers of non-synonymous single-nucleotide polymorphism mismatch have been associated with increased allograft loss in a multivariable analysis. It is concluded that there is a need for integration of clinical characteristics and novel molecular and immunological markers to improve future transplant medicine to reach better diagnostic decisions tailored to the individual patient.

Interactions Between Donor Age and 12-Month Estimated Glomerular Filtration Rate on Allograft and Patient Outcomes After Kidney Transplantation

Reduced estimated glomerular filtration rate (eGFR) at 12-months after kidney transplantation is associated with increased risk of allograft loss, but it is uncertain whether donor age and types modify this relationship. Using Australia and New Zealand registry data, multivariable Cox proportional modelling was used to examine the interactive effects between donor age, types and 12-month eGFR on overall allograft loss. We included 11,095 recipients (4,423 received live-donors). Recipients with lowest 12-month eGFR (<30 ml/min/1.73 m²) experienced the greatest risk of allograft loss, with adjusted HR [95% CI) of 2.65 [2.38–2.95] compared to eGFR of 30–60 ml/min/1.73 m²; whereas the adjusted HR for highest eGFR (>60 ml/min/1.73 m²) was 0.67 [0.62–0.74]. The association of 12-month eGFR and allograft loss was modified by donor age (but not donor types) where a higher risk of allograft loss in recipients with lower compared with higher 12-month eGFR being most pronounced in the younger donor age groups (p < 0.01). Recipients with eGFR <30 ml/min/1.73 m² 12-months after transplantation experienced ≥2.5-fold increased risk of overall allograft loss compared to those with eGFR of >60 ml/min/1.73 m², and the magnitude of the increased risk is most marked among recipients with younger donors. Careful deliberation of other factors including donor age when considering eGFR as a surrogate for clinical endpoints is warranted.

Application of the iBox prognostication system as a surrogate endpoint in the TRANSFORM randomised controlled trial: proof-of-concept study

OBJECTIVES

Development of pharmaceutical agents in transplantation is currently limited by long waits for hard endpoints. We applied a validated integrative risk-prognostication system integrative Box (iBox) as a surrogate endpoint to the TRANSFORM Study, a large randomised controlled trial, to project individual patient long-term kidney allograft survival from 1 year to 11 years after randomisation.

DESIGN

Post-hoc analysis of a randomised open-label controlled trial.

SETTING

Multicentre study including 186 centres in 42 countries worldwide.

PARTICIPANTS

2037 de novo kidney transplant recipients.

INTERVENTION

Participants were randomised (1:1) to receive everolimus with reduced-exposure calcineurin inhibitor (EVR+rCNI) or mycophenolic acid with standard-exposure CNI (MPA+sCNI).

PRIMARY OUTCOME MEASURE

The iBox scores were computed for each participant at 1 year after randomisation using functional, immunological and histological parameters. Individual long-term death-censored allograft survival over 4, 6 and 11 years after randomisation was projected with the iBox risk-prognostication system.

RESULTS

Overall, 940 patients receiving EVR+rCNI and 932 receiving MPA+sCNI completed the 1-year visit. iBox scores generated at 1 year yielded graft survival prediction rates of 90.9% vs 92.1%, 87.9% vs 89.5%, and 80.0% vs 82.4% in the EVR+rCNI versus MPA+sCNI arms at 4, 6, and 11 years post-randomisation, respectively (all differences below the 10% non-inferiority margin defined by study protocol). Inclusion of immunological and histological Banff diagnoses parameters in iBox scores resulted in comparable and non-inferior predicted graft survival for both treatments.

CONCLUSIONS

This proof-of-concept study provides the first application of a validated prognostication system as a surrogate endpoint in the field of transplantation. The iBox system, by projecting kidney allograft survival up to 11 years post-randomisation, confirms the non-inferiority of EVR+rCNI versus MPA+sCNI regimen. Given the current process engaged for surrogate endpoints qualification, this study illustrates the potential to fast track development of pharmaceutical agents.

TRIAL REGISTRATION NUMBER

TRANSFORM trial: NCT01950819.iBox prognostication system: NCT03474003.

Prognostic value for long-term graft survival of estimated glomerular filtration rate and proteinuria quantified at 3 months after kidney transplantation

Background

The estimated glomerular filtration rate (eGFR) measured at 1 year is the usual benchmark applied in kidney transplantation (KT). However, acting on earlier eGFR values could help in managing KT during the first post-operative year. We aimed to assess the prognostic value for long-term graft survival of the early (3 months) quantification of eGFR and proteinuria following KT.

Methods

The 3-, 6- and 12-month eGFR using the Modified Diet in Renal Disease equation (eGFR_MDRD) was determined and proteinuria was measured in 754 patients who underwent their first KT between 2000 and 2010 (with a mean follow-up of 8.3 years) in our centre. Adjusted associations with graft survival were estimated using a multivariable Cox model. The predictive accuracy was estimated using the C-index and net reclassification index. These same analyses were measured in a multicentre validation cohort of 1936 patients.

Results

Both 3-month eGFR_MDRD and proteinuria were independent predictors of return to dialysis (all P < 0.05) and there was a strong correlation between eGFR at 3 and 12 months (Spearman’s ρ = 0.76). The predictive accuracy of the 3-month eGFR was within a similar range and did not differ significantly from the 12-month eGFR in either the derivation cohort [C-index 62.6 (range 57.2–68.1) versus 66.0 (range 60.1–71.9), P = 0.41] or the validation cohort [C-index 69.3 (range 66.4–72.1) versus 71.7 (range 68.7–74.6), P = 0.25].

Conclusion

The 3-month eGFR was a valuable predictor of the long-term return to dialysis whose predictive accuracy was not significantly less than that of the 12-month eGFR in multicentre cohorts totalling >2500 patients. Three-month outcomes may be useful in randomized controlled trials targeting early therapeutic interventions.

Use of diuretics in shock: Temporal trends and clinical impacts in a propensity-matched cohort study

OBJECTIVE

Fluid overload is common among critically ill patients and is associated with worse outcomes. We aimed to assess the effect of diuretics on urine output, vasopressor dose, acute kidney injury (AKI) incidence, and need for renal replacement therapies (RRT) among patients who receive vasopressors.

PATIENTS AND METHODS

This is a single-center retrospective study of all adult patients admitted to the intensive care unit between January 2006 and December 2016 and received >6 hours of vasopressor therapy and at least one concomitant dose of diuretic. We excluded patients from cardiac care units. Hourly urine output and vasopressor dose for 6 hours before and after the first dose of diuretic therapy was compared. Rates of AKI development and RRT initiation were assessed with a propensity-matched cohort of patients who received vasopressors but did not receive diuretics.

RESULTS

There was an increasing trend of prescribing diuretics in patients receiving vasopressors over the course of the study. We included 939 patients with median (IQR) age of 68(57, 78) years old and 400 (43%) female. The average hourly urine output during the first six hours following time zero in comparison with average hourly urine output during the six hours prior to time zero was significantly higher in diuretic group in comparison with patients who did not receive diuretics [81 (95% CI 73-89) ml/h vs. 42 (95% CI 39-45) ml/h, respectively; p<0.001]. After propensity matching, the rate of AKI within 7 days of exposure and the need for RRT were similar between the study and matched control patients (66 (15.6%) vs. 83 (19.6%), p = 0.11, and 34 (8.0%) vs. 37 (8.7%), p = 0.69, respectively). Mortality, however, was higher in the group that received diuretics. Ninety-day mortality was 191 (45.2%) in the exposed group VS 156 (36.9%) p = .009.

CONCLUSIONS

While the use of diuretic therapy in critically ill patients receiving vasopressor infusions augmented urine output, it was not associated with higher vasopressor requirements, AKI incidence, and need for renal replacement therapy.

Prediction system for risk of allograft loss in patients receiving kidney transplants: international derivation and validation study

OBJECTIVE

To develop and validate an integrative system to predict long term kidney allograft failure.

DESIGN

International cohort study.

SETTING

Three cohorts including kidney transplant recipients from 10 academic medical centres from Europe and the United States.

PARTICIPANTS

Derivation cohort: 4000 consecutive kidney recipients prospectively recruited in four French centres between 2005 and 2014. Validation cohorts: 2129 kidney recipients from three centres in Europe and 1428 from three centres in North America, recruited between 2002 and 2014. Additional validation in three randomised controlled trials (NCT01079143, EudraCT 2007-003213-13, and NCT01873157).

MAIN OUTCOME MEASURE

Allograft failure (return to dialysis or pre-emptive retransplantation). 32 candidate prognostic factors for kidney allograft survival were assessed.

RESULTS

Among the 7557 kidney transplant recipients included, 1067 (14.1%) allografts failed after a median post-transplant follow-up time of 7.12 (interquartile range 3.51-8.77) years. In the derivation cohort, eight functional, histological, and immunological prognostic factors were independently associated with allograft failure and were then combined into a risk prediction score (iBox). This score showed accurate calibration and discrimination (C index 0.81, 95% confidence interval 0.79 to 0.83). The performance of the iBox was also confirmed in the validation cohorts from Europe (C index 0.81, 0.78 to 0.84) and the US (0.80, 0.76 to 0.84). The iBox system showed accuracy when assessed at different times of evaluation post-transplant, was validated in different clinical scenarios including type of immunosuppressive regimen used and response to rejection therapy, and outperformed previous risk prediction scores as well as a risk score based solely on functional parameters including estimated glomerular filtration rate and proteinuria. Finally, the accuracy of the iBox risk score in predicting long term allograft loss was confirmed in the three randomised controlled trials.

CONCLUSION

An integrative, accurate, and readily implementable risk prediction score for kidney allograft failure has been developed, which shows generalisability across centres worldwide and common clinical scenarios. The iBox risk prediction score may help to guide monitoring of patients and further improve the design and development of a valid and early surrogate endpoint for clinical trials.

TRIAL REGISTRATION

Clinicaltrials.gov NCT03474003.

Complement-binding anti-HLA antibodies are independent predictors of response to treatment in kidney recipients with antibody-mediated rejection

A major hurdle to improving clinical care in the field of kidney transplantation is the lack of biomarkers of the response to antibody-mediated rejection (ABMR) treatment. To discover these we investigated the value of complement-binding donor-specific anti-HLA antibodies (DSAs) for evaluating the response to treatment. The study encompassed a prospective cohort of 139 kidney recipients with ABMR receiving the standard of care treatment, including plasma exchange, intravenous immunoglobulin and rituximab. Patients were systematically assessed at the time of diagnosis and three months after treatment initiation for clinical and allograft histological characteristics and anti-HLA DSAs, including their C1q-binding ability. After adjusting for clinical and histological parameters, post-treatment C1q-binding anti-HLA DSA was an independent and significant determinant of allograft loss (adjusted hazard ratio 2.57 (95% confidence interval 1.29-5.12). In 101 patients without post-treatment C1q-binding anti-HLA DSA there was a significantly improved glomerular filtration rate with significantly reduced glomerulitis, peritubular capillaritis, interstitial inflammation, tubulitis, C4d deposition, and endarteritis compared with 38 patients with posttreatment C1q-binding anti-HLA DSA. A conditional inference tree model identified five prognostic groups at the time of post-treatment evaluation based on glomerular filtration rate, presence of cg lesion and C1q-binding anti-HLA DSA (cross-validated accuracy: 0.77). Thus, circulating complement-binding anti-HLA DSAs are strong and independent predictors of allograft outcome after standard of care treatment in kidney recipients with ABMR.

[Multidimensional approaches for risk stratification in transplantation]

Despite considerable progress in the short-term outcomes of renal transplantation, there has been little improvement over the last 15years on long-term survival. The main limitation is the lack of precise knowledge of the determinants of renal allograft loss and robust prognostic systems providing an individual prediction. Kidney transplantation must address a pressing clinical need to accurately define the determinants of kidney renal allograft survival in order to improve risk stratification. To achieve this goal, four steps need to be considered in the development of prognostic tools: the characterization and identification of the phenotype of the pathology, the assessment of prognostic factors of the event of interest in the population, the assessment of the additional value provided by a newly identified prognostic factor to conventional factors already known in clinical practice as well as the construction of prognostic tools, on the basis of multidimensional integrative models allowing a precise stratification of the risk, at individual and population scale.

Estimating Glomerular Filtration Rate in Kidney Transplant Recipients: Comparing a Novel Equation With Commonly Used Equations in this Population

Background

Assessment of glomerular filtration rate (GFR) is important in kidney transplantation. The aim was to develop a kidney transplant specific equation for estimating GFR and evaluate against published equations commonly used for GFR estimation in these patients.

Methods

Adult kidney recipients (n = 594) were included, and blood samples were collected 10 weeks posttransplant. GFR was measured by ⁵¹Cr-ethylenediaminetetraacetic acid clearance. Patients were randomized into a reference group (n = 297) to generate a new equation and a test group (n = 297) for comparing it with 7 alternative equations.

Results

Two thirds of the test group were males. The median (2.5-97.5 percentile) age was 52 (23-75) years, cystatin C, 1.63 (1.00-3.04) mg/L; creatinine, 117 (63-220) μmol/L; and measured GFR, 51 (29-78) mL/min per 1.73 m². We also performed external evaluation in 133 recipients without the use of trimethoprim, using iohexol clearance for measured GFR. The Modification of Diet in Renal Disease equation was the most accurate of the creatinine-equations. The new equation, estimated GFR (eGFR) = 991.15 × (1.120^sex/([age^0.097] × [cystatin C^0.306] × [creatinine^0.527]); where sex is denoted: 0, female; 1, male, demonstrating a better accuracy with a low bias as well as good precision compared with reference equations. Trimethoprim did not influence the performance of the new equation.

Conclusions

The new equation demonstrated superior accuracy, precision, and low bias. The Modification of Diet in Renal Disease equation was the most accurate of the creatinine-based equations.

A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study

Accurate prediction of graft survival after kidney transplant is limited by the complexity and heterogeneity of risk factors influencing allograft survival. In this study, we applied machine learning methods, in combination with survival statistics, to build new prediction models of graft survival that included immunological factors, as well as known recipient and donor variables. Graft survival was estimated from a retrospective analysis of the data from a multicenter cohort of 3,117 kidney transplant recipients. We evaluated the predictive power of ensemble learning algorithms (survival decision tree, bagging, random forest, and ridge and lasso) and compared outcomes to those of conventional models (decision tree and Cox regression). Using a conventional decision tree model, the 3-month serum creatinine level post-transplant (cut-off, 1.65 mg/dl) predicted a graft failure rate of 77.8% (index of concordance, 0.71). Using a survival decision tree model increased the index of concordance to 0.80, with the episode of acute rejection during the first year post-transplant being associated with a 4.27-fold increase in the risk of graft failure. Our study revealed that early acute rejection in the first year is associated with a substantially increased risk of graft failure. Machine learning methods may provide versatile and feasible tools for forecasting graft survival.

Factors that Influence Graft Function at 1-Year Posttransplantation and Correlation with Baseline Donated Kidney Function Measured with Radioisotopes

Introduction. Assessment of renal function is a crucial component of donor evaluation. The higher measured donor GFR is independently associated with a better allograft outcomes in living donor kidney transplantation (LDKT). Monitoring graft function and estimation of GFR is a recommended method for patients’ follow-up in posttransplantation period. The aim of our study was to investigate the correlation of directly measured GFR of donated kidney with estimated GFR through creatininebased formulas and to detect impact factors on the graft function at 12 months posttransplantation. Methods. Fifty LDKT patients (related and nonrelated donors) with stable renal function in a period of 12 months after transplantation were included in our study. The mean recipient age was 30.7±9.6 years, and donor age 55.45±9.41 years. The mean directly measured donated kidney GFR was 47.61±5.72 ml/min. Graft function was estimated at 3, 6 and 12 months by 3 formulas: Cockcroft- Gault (C-G), MDRD 6 variables and Nankivell. Direct correlation of estimated with measured radiolabeled 99mTc DTPA GFR was performed. Various impact factors such as donor age, dialysis vintage and different calcineurin inhibitors as a part of immunosupression were evaluated. Results. Estimated GFR at 12 months with MDRD, Cockroft Gault, and Nankivell formulas was 72.65±22.6, 94.25±36.42, and 81.78±17.89 ml/min, respectively. The highest estimated GFR was obtained with C-G formula at all three time points. The estimated allograft GFR did not correlate with directly measured GFR of donated kidney. Donor age well correlated with the graft function at 12 months. Allografts from standard criteria donors-SCD (<60 years) had better function than allografts form expanded criteria donors-ECD (>60 years). The highest GFR was estimated with C-G equation (106.08±39.26 ml/min), while GFR estimated with Nankivell was 86.86±15.30 ml/min, and with MDRD 79.67±20.28 ml/min, presenting patients in stage 2 of chronic kidney disease. Duration of hemodialysis treatment under 24 months showed better graft function estimated by C-G at 12 months (102.23±38.86 ml/min), compared to that above 24 months of HD (77.84±18.11 ml/ min). Different type of calcineurin inhibitors did not influence on the graft function at any time point. Conclusion. Creatinine-based formulas for estimation of the graft function did not correlate with directly measured function of the donated kidney with radiolabeled isotopes, nor between each other. Hence, the monitoring of the graft function should be done by a single formula in the posttransplantation period. Expectedly, a better graft function was observed in young donors (standard criteria) and in patients with shorter hemodialysis treatment.

Serum uromodulin predicts graft failure in renal transplant recipients

OBJECTIVE AND METHODS

Test the ability of serum uromodulin concentrations 1-3 months after renal transplantation to predict all-cause mortality (ACM) and graft loss (GL) in 91 patients.

RESULTS

uromodulin predicted GL equivalently to the other markers studied: the risk for GL was reduced by 0.21 per one standard deviation (SD) increase (cystatin C: hazard ratio [HR] 4.57, creatinine: HR 4.53, blood-urea-nitrogen [BUN]: HR 2.50, estimated glomerular filtration rate [eGFR]: HR 0.10). In receiver-operating-characteristic (ROC) analysis, uromodulin predicted GL with an area-under-the curve of 0.782 at an optimal cut-off (OCO) of 24.0 ng/ml with a sensitivity of 90.0% and a specificity of 70.2%.

CONCLUSION

Serum uromodulin predicted GL equivalently compared to conventional biomarkers of glomerular filtration.

Classification of Kidney Transplant Recipients Using a Combination of Estimated GFR and Albuminuria Reflects Risk

BACKGROUND

The 2012 Kidney Dialysis Initiative Global Outcomes chronic kidney disease (CKD) classification scheme subdivides stage 3 CKD and incorporates the urinary albumin-to-creatinine ratio (ACR). The aim of this study was to evaluate whether the novel scheme provides graded risk in kidney transplant recipients (KTRs).

METHODS

Prevalent KTRs with available laboratory data were included. The primary outcome was a composite of doubling of serum creatinine, graft failure, or death. Patients were stratified using the CKD-Epidemiolgic Collaboration equation, and ACR and the event rate per 1000 patient-years in each CKD category were calculated.

RESULTS

There were 269 KTRs with a mean follow-up of 4.5 ± 2.0 years. There was a graded increase in outcomes with increasing ACR and decreasing estimated glomerular filtration rate (eGFR). For the primary outcome, the event rate was 15.3 (95% confidence interval, 4.2-39.2) per 1000 patient-years for those with an eGFR greater than 60 mL/min per 1.73 m2 and an ACR less than 30 mg/g, whereas it was 375 (95% confidence interval, 193.8-655.1) for those with an eGFR less than 30 mL/min per 1.73 m2 and an ACR greater than 300 mg/g.

CONCLUSIONS

The novel Kidney Dialysis Initiative Global Outcomes classification scheme provides graded risk for important clinical events in KTRs. This information can be used to identify high-risk patients and to tailor follow-up and management strategies aimed at improving outcomes.

Predicting Individual Renal Allograft Outcomes Using Risk Models with 1-Year Surveillance Biopsy and Alloantibody Data

The ability to predict outcomes for individual patients would be a significant advance for not only counseling, but also identifying those for whom interventions may be needed. The goals of this study were to validate an existing risk prediction score that incorporates easily obtainable clinical factors and determine if histologic findings at 1-year surveillance biopsy and/or serum donor-specific alloantibody status could improve predictability of graft loss by 5 years. We retrospectively studied 1465 adults who received a solitary kidney transplant between January of 1999 and December of 2008 and had sufficiently detailed 5-year follow-up data for modeling. In this cohort, the Birmingham risk model (incorporating recipient factors at 1 year, including age, sex, ethnicity, renal function, proteinuria, and prior acute rejection) predicted death-censored and overall graft survival (c statistics =0.84 and 0.78, respectively). The presence of glomerulitis or chronic interstitial fibrosis (g and ci scores by Banff, respectively) on 1-year biopsy specimens independently correlated with graft loss by 5 years. Adding these variables to the model for death-censored graft loss increased predictability (c statistic =0.90), improved calibration (ability to stratify risk from high to low), and reclassified risk of failure in 29% of patients. Adding the presence of donor-specific alloantibody at 1 year did not improve predictability or reclassification but did improve calibration marginally. We conclude that, at 1 year after kidney transplant, a risk model of graft survival that incorporates clinical factors and histologic findings at surveillance biopsy is highly predictive of individual risk and well calibrated.

Estimating glomerular filtration rate in kidney transplantation: Still searching for the best marker

Kidney transplantation is the treatment of choice for end-stage renal disease. The evaluation of graft function is mandatory in the management of renal transplant recipients. Glomerular filtration rate (GFR), is generally considered the best index of graft function and also a predictor of graft and patient survival. However GFR measurement using inulin clearance, the gold standard for its measurement and exogenous markers such as radiolabeled isotopes ((51)Cr EDTA, (99m)Tc DTPA or (125)I Iothalamate) and non-radioactive contrast agents (Iothalamate or Iohexol), is laborious as well as expensive, being rarely used in clinical practice. Therefore, endogenous markers, such as serum creatinine or cystatin C, are used to estimate kidney function, and equations using these markers adjusted to other variables, mainly demographic, are an attempt to improve accuracy in estimation of GFR (eGFR). Nevertheless, there is some concern about the inability of the available eGFR equations to accurately identify changes in GFR, in kidney transplant recipients. This article will review and discuss the performance and limitations of these endogenous markers and their equations as estimators of GFR in the kidney transplant recipients, and their ability in predicting significant clinical outcomes.

Cardiovascular events and all-cause mortality in a cohort of 57,946 patients with type 2 diabetes: associations with renal function and cardiovascular risk factors

Background

Diabetes and chronic kidney disease (CKD) are independent predictors of death and cardiovascular events and their concomitant prevalence has increased in recent years. The aim of this study was to characterize the effect of the estimated glomerular filtration rate (eGFR) and other factors on the risk of death and cardiovascular events in patients with type 2 diabetes.

Methods

A cohort of 57,946 patients with type 2 diabetes who were aged 20–89 years in 2000–2005 was identified from The Health Improvement Network, a UK primary care database. Incidence rates of death, myocardial infarction (MI), and ischemic stroke or transient ischemic attack (IS/TIA) were calculated overall and by eGFR category at baseline. eGFR was calculated using the Modification of Diet in Renal Disease (MDRD) study equation. Death, MI and IS/TIA cases were detected using an automatic computer search and IS/TIA cases were further ascertained by manual review of medical records. Hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) for death, MI, and IS/TIA associated with eGFR category and other factors were estimated using Cox regression models adjusted for potential confounders.

Results

Overall incidence rates of death (mean follow-up time of 6.76 years), MI (6.64 years) and IS/TIA (6.56 years) were 43.65, 9.26 and 10.39 cases per 1000 person-years, respectively. A low eGFR (15–29 mL/min) was associated with an increased risk of death (HR: 2.79; 95% CI: 2.57–3.03), MI (HR: 2.33; 95% CI: 1.89–2.87) and IS/TIA (HR: 1.77; 95% CI: 1.43–2.18) relative to eGFR ≥ 60 mL/min. Other predictors of death, MI and IS/TIA included age, longer duration of diabetes, poor control of diabetes, hyperlipidemia, smoking and a history of cardiovascular events.

Conclusions

In patients with type 2 diabetes, management of cardiovascular risk factors and careful monitoring of eGFR may represent opportunities to reduce the risks of death, MI and IS/TIA.

Electronic supplementary material

The online version of this article (doi:10.1186/s12933-015-0204-5) contains supplementary material, which is available to authorized users.

Influence of renal function on long-term graft survival and patient survival in renal transplant recipients

OBJECTIVES

Renal function post kidney transplantation is an outcome of interest for both clinicians and regulators evaluating immunosuppressive treatments post-transplantation. The current review sought to provide a synopsis of currently available literature examining the relationship between post-transplantation renal function and long-term graft survival and patient survival.

METHODS

A systematic literature review was performed using the PubMed, EMBASE and Cochrane Library databases. The search strategy was designed based on high level Medical Subject Heading (MeSH) terms and designed to capture studies published in English to 2012 and identified a total of 2683 unique hits; for inclusion studies were required to have >100 patients. Following two rounds of screening, a total of 27 studies were included in the final review (26 of which were identified via the literature review and one study was identified via searches of the reference sections of included studies).

RESULTS

The consensus among studies was that lower post-transplantation GFR, in particular 12 month GFR, was consistently and significantly associated with an increased risk for overall graft loss, death-censored graft loss and all-cause mortality in both univariate and multivariate analyses. The magnitude of the association between reduced GFR and outcomes was greater for death-censored graft loss versus overall graft loss and for graft loss in comparison with overall patient mortality. The predictive utility of GFR alone in predicting long-term outcomes was reported to be limited.

CONCLUSIONS

Lower GFR and greater rates of decline in GFR post-transplantation are associated with an increased risk for graft loss (overall and death-censored) and all-cause mortality; however, the predictive utility of GFR alone in predicting long-term outcomes is limited.

Cystatin C and serum creatinine as predictors of kidney graft outcome

PURPOSE

Serum cystatin C (Cys C) was evaluated as a predictor of kidney graft failure progression, and its predictive ability was compared to other markers of graft function.

METHODS

The following kidney graft markers were determined in 91 patients who came for regular checkups of kidney graft function to our outpatient service in February 2008: Cys C, serum creatinine (sCr), 24-h proteinuria and 24-h urinary creatinine clearance (CCr). Glomerular filtration rate (eGFR) was estimated using sCr-based and Cys C formula. Patients were regularly monitored until February 2013 or to graft failure.

RESULTS

During follow-up, graft failure occurred in 21 recipients. The Cys C ≥2.65 mg/l discriminated patients with and without graft failure (sensitivity of 80.95% and specificity of 92.86%). According to c statistic, the highest performance was achieved for Cys C (0.874). In addition, Cys C area under the curve (AUC) was significantly better than CCr AUC (p = 0.007), 24-h proteinuria AUC (p = 0.03), eGFR estimated by the chronic kidney disease epidemiology collaboration (EPI) AUC (p = 0.05), but not better than sCr or eGFR AUCs calculated by other formulas. In the multivariable model, sCr, CCr, Cys C and eGFRs were predictors of graft failure. Combination of Cys C, sCr and logarithm of 24 h proteinuria (0.883) or Cys C, CCr and logarithm of 24-h proteinuria (0.884) had the highest AUC for predicting graft outcome that exceed insignificantly Cys C or sCr areas.

CONCLUSIONS

The most reliable predictors of graft outcome were Cys C, sCr and proteinuria. Because Cys C is unavailable in many transplant centers, from the practical point of view, sCr remains the most sensitive predictor of graft outcome.

Kidney function endpoints in kidney transplant trials: a struggle for power

Kidney function endpoints are commonly used in randomized controlled trials (RCTs) in kidney transplantation (KTx). We conducted this study to estimate the proportion of ongoing RCTs with kidney function endpoints in KTx where the proposed sample size is large enough to detect meaningful differences in glomerular filtration rate (GFR) with adequate statistical power. RCTs were retrieved using the key word "kidney transplantation" from the National Institute of Health online clinical trial registry. Included trials had at least one measure of kidney function tracked for at least 1 month after transplant. We determined the proportion of two-arm parallel trials that had sufficient sample sizes to detect a minimum 5, 7.5 and 10 mL/min difference in GFR between arms. Fifty RCTs met inclusion criteria. Only 7% of the trials were above a sample size of 562, the number needed to detect a minimum 5 mL/min difference between the groups should one exist (assumptions: α = 0.05; power = 80%, 10% loss to follow-up, common standard deviation of 20 mL/min). The result increased modestly to 36% of trials when a minimum 10 mL/min difference was considered. Only a minority of ongoing trials have adequate statistical power to detect between-group differences in kidney function using conventional sample size estimating parameters. For this reason, some potentially effective interventions which ultimately could benefit patients may be abandoned from future assessment.

Rate of renal graft function decline after one year is a strong predictor of all-cause mortality

The slope of GFR associates with an increased risk for death in patients with native CKD but whether a similar association exists in kidney transplantation is not known. We studied an inception cohort of 488 kidney transplant recipients (mean follow-up of 12 ± 4 years) for whom GFR was longitudinally measured by inulin clearance (mGFR) at 1 year and then every 5 years. Association of mGFR at 1 year posttransplant and GFR slope after the first year with all-cause mortality was studied with a Cox regression model and a Fine and Gray competing risk model. While in Crude analysis, the mGFR value at 1 year posttransplant and the rate of mGFR decline were both associated with a higher risk of all-cause mortality, only the slope of mGFR remained a significant and strong predictor of death in multivariate analysis. Factors independently associated with a more rapid mGFR decline were feminine gender, higher HLA mismatch, retransplantation, longer duration of transplantation, CMV infection during the first year and higher rate of proteinuria. Our data suggest that the rate of renal graft function decline after 1 year is a strong predictor of all-cause mortality in kidney transplantation.

Cystatin C and albuminuria as predictors of long-term allograft outcomes in kidney transplant recipients

Although cystatin C (Cys) and albuminuria (Alb) are predictors of end-stage renal disease in the general population, there are limited data about the performance of these markers alone or combined with respect to the prediction of the kidney transplant outcome. We assessed the ability of one-yr creatinine (Cr), MDRD equation, Cys, Hoek equation, Alb, the logarithm of albuminuria (LogAlb), and two products of these variables for predicting death-censored graft loss (DCGL) in 127 kidney transplant recipients. Mean follow-up time was 5.6 ± 1.7 yr. During this time, 18 patients developed DCGL. The area under the receiver operating characteristic curve for DCGL ranged from 71.1% to 85.4%, with Cys*LogAlb being the best predictor. Cys-based variables and variables combining LogAlb and renal function estimates have better discrimination ability than Cr-based variables alone. After multivariate analysis, quartiles of all one-yr variables (except of Cr and MDRD) were independent predictors for DCGL. Predictors combining Alb and a Cr- or Cys-based estimate of renal function performed better than those markers alone to predict DCGL. Cys-based predictors performed better than Cr-based predictors. Using a double-marker in kidney transplantation, it is possible to identify the highest risk group in which to prioritize specialty care.

Joint relationship between renal function and proteinuria on mortality of patients with type 2 diabetes: the Taichung Diabetes Study

BACKGROUND

Estimated glomerular filtration rate (eGFR) is a powerful predictor of mortality in diabetic patients with limited proteinuria data. In this study, we tested whether concomitant proteinuria increases the risk of mortality among patients with type 2 diabetes.

METHODS

Participants included 6523 patients > 30 years with type 2 diabetes who were enrolled in a management program of a medical center before 2007. Renal function was assessed by eGFR according to the Modification of Diet in Renal Disease Study equation for Chinese. Proteinuria was assessed by urine dipstick.

RESULTS

A total of 573 patients (8.8%) died over a median follow-up time of 4.91 years (ranging from 0.01 year to 6.42 years). The adjusted expanded cardiovascular disease (CVD)-related mortality rates among patients with proteinuria were more than three folds higher for those with an eGFR of 60 mL/min/1.73 m2 or less compared with those with an eGFR of 90 mL/min/1.73 m2 or greater [hazard ratio, HR, 3.15 (95% confidence interval, CI, 2.0-5.1)]. The magnitude of adjusted HR was smaller in patients without proteinuria [1.98 (95% CI, 1.1-3.7)]. An eGFR of 60 mL/min/1.73 m2 to 89 mL/min/1.73 m2 significantly affected all-cause mortality and mortality from expanded CVD-related causes only in patients with proteinuria. Similarly, proteinuria affected all outcomes only in patients with an eGFR of <60 mL/min/1.73 m2.

CONCLUSION

The risks of all-cause mortality, as well as expanded and non-expanded mortality from CVD-related causes associated with proteinuria or an eGFR of 90 mL/min/1.73 m2 or greater are independently increased. Therefore, the use of proteinuria measurements with eGFR increases the precision of risk stratification for mortality.

Diagnosis and prevention of chronic kidney allograft loss

Kidney transplantation is the best possible treatment for many patients with end-stage renal failure, but progressive dysfunction and eventual allograft loss with return to dialysis is associated with increased mortality and morbidity. Immune injury from acute or chronic rejection and non-immune causes, such as nephrotoxicity from calcineurin inhibitors, ischaemia-reperfusion injury, recurrent glomerular disease, and allograft BK viral infection, are potential threats. Serial monitoring of renal function enables early recognition of chronic allograft dysfunction, and investigations such as therapeutic drug concentrations, urinalysis, imaging, and a diagnostic biopsy should be undertaken before irreversible nephron loss has occurred. Specific interventions targeting the pathophysiological cause of dysfunction include strengthening of immunosuppression for chronic rejection, or calcineurin inhibitor minimisation, substitution, or elimination if nephrotoxicity dominates. Recommended proactive preventive measures are control of hypertension, proteinuria, dyslipidaemia, diabetes, smoking, and other comorbidities. Strategies to maintain transplant function and improve long-term graft survival are important goals of translational research.

The relationship between kidney function and long-term graft survival after kidney transplant

BACKGROUND

Whether chronic kidney disease (CKD) staging provides a useful framework for predicting outcomes after kidney transplant is unclear.

STUDY DESIGN

Retrospective cohort study.

SETTING & PARTICIPANTS

We used data from the Patient Outcomes in Renal Transplantation (PORT) Study, including 13,671 transplants from 12 centers during 10 years of follow-up.

PREDICTOR

Estimated glomerular filtration rate (eGFR; in milliliters per minute per 1.73 m(2)) at 12 months posttransplant.

OUTCOMES

All-cause graft failure (a composite end point consisting of return to dialysis therapy, pre-emptive retransplant, or death with function), death-censored graft failure, and death with a functioning graft.

MEASUREMENTS

The relationship between 12-month eGFR and subsequent graft outcomes through 10 years posttransplant was assessed using Cox proportional hazards analyses.

RESULTS

Stage 3 included 63% of patients and was subdivided into stages 3a (eGFR, 45-59 mL/min/1.73 m(2); 34%) and 3b (eGFR, 30-44 mL/min/1.73 m(2); 29%). Compared with stage 2 (eGFR, 60-89 mL/min/1.73 m(2); 24%), adjusted Cox proportional HRs for graft failure were 1.12 (95% CI, 1.01-1.24; P = 0.04) for stage 3a, 1.50 (95% CI, 1.35-1.66; P < 0.001) for stage 3b, 2.86 (95% CI, 2.53-3.22; P < 0.001) for stage 4 (eGFR, 15-29 mL/min/1.73 m(2); 9%), and 13.2 (95% CI, 10.7-16.4; P < 0.001) for stage 5 (eGFR <15 mL/min/1.73 m(2); 1%). For stage 1 (eGFR ≥ 90 mL/min/1.73 m(2); 3%), risk of graft failure was increased (1.41 [95% CI, 1.13-1.75]; P < 0.001), likely due to serum creatinine associations independent of kidney function. Similar associations were seen between CKD stages and mortality.

LIMITATIONS

Retrospective study; lack of gold-standard measurements of true GFR; lack of measures of comorbidity, inflammation, muscle mass, proteinuria, and other noncreatinine markers of eGFR.

CONCLUSIONS

CKD stages validated in the general population provide a useful framework for predicting outcomes after kidney transplant.

Metabolic syndrome in pediatric renal transplant recipients: comparing early discontinuation of steroids vs. steroid group

Steroids have played a valuable role in transplantation as a treatment option. The purpose of this study is to assess the prevalence of MS in pediatric RT patients receiving SG or early SWG; SG discontinued five days after transplantation. We retrospectively reviewed 58 pediatric RT patients between 2000 and 2007. MS criterion was defined as the presence of any three of five criteria: (i) BMI >97th percentile, (ii) hypertension (SBP/DBP > 95th percentile or on medications); (iii) triglycerides > 95thpercentile, (iv) HDL cholesterol < 5th percentile, (v) fasting glucose > 100 mg/dL. Twenty-five patients (43%) received SG and 33 patients (57%) received SWG. The prevalence of MS in SG was 68% compared to 15% in SWG. At six months and one yr after transplantation, mean serum glucose, total cholesterol, and triglycerides were significantly lower in the SWG. The prevalence of hypertension was significantly lower in the SWG, and patients in the SWG received significantly less lipid-lowering and anti-hypertensive medications than SG. Mean BMI percentile was significantly higher in SG one yr after transplantation but not after six months, although always significantly higher in patients with MS (p < 0.05). From this study, we conclude that for pediatric RT patients, cardiovascular risk factors are significantly lower in SG withdrawal groups.