Cost-Effectiveness and Budget Impact Analysis of Implementing a 'Soft Opt-Out' System for Kidney Donation in Australia.

INTRODUCTION: There is a severe shortage of donor organs globally. There is growing interest in understanding how a 'soft opt-out' organ donation system could help bridge the supply and demand gap for donor organs. This research aims to estimate the cost-effectiveness and budget impact of implementing a 'soft opt-out' organ donation system for kidney donation. METHODS: A decision-analytic model was developed to estimate the incremental costs from a health system's perspective, quality-adjusted life-years (QALYs), and death averted of people who have kidney failure, comparing a 'soft opt-out' organ donation system to an 'opt-in' system. This study analysed three scenarios where the 'soft opt-out' system generated a 20%, 30%, and 40% increase in deceased organ donation rates over 20 years. A 5-year time horizon was adopted for the budget impact analysis. RESULTS: A 20% increase in organ donation rates could have a cost saving of 650 million Australian dollars (A$) and a 10,400-QALY gain. A 20% increase would avert more than 1500 deaths, while a 40% increase would avert 3200 deaths over a time horizon of 20 years. Over the first 5 years, a 20% increase would have a net saving of A$53 million, increasing to A$106 million if the donation rate increases by 40%. CONCLUSION: A 'soft opt-out' organ donation system would return a cost saving for the healthcare system, a net gain in QALYs, and prevention of a significant number of deaths. Advantageous budgetary impact is important, but understanding the aversion for a 'soft opt-out' system in Australia is also important and remains a priority for further research.

Development and validation of a risk index to predict kidney graft survival: the kidney transplant risk index.

BACKGROUND: Kidney graft failure risk prediction models assist evidence-based medical decision-making in clinical practice. Our objective was to develop and validate statistical and machine learning predictive models to predict death-censored graft failure following deceased donor kidney transplant, using time-to-event (survival) data in a large national dataset from Australia. METHODS: Data included donor and recipient characteristics (n = 98) of 7,365 deceased donor transplants from January 1st, 2007 to December 31st, 2017 conducted in Australia. Seven variable selection methods were used to identify the most important independent variables included in the model. Predictive models were developed using: survival tree, random survival forest, survival support vector machine and Cox proportional regression. The models were trained using 70% of the data and validated using the rest of the data (30%). The model with best discriminatory power, assessed using concordance index (C-index) was chosen as the best model. RESULTS: Two models, developed using cox regression and random survival forest, had the highest C-index (0.67) in discriminating death-censored graft failure. The best fitting Cox model used seven independent variables and showed moderate level of prediction accuracy (calibration). CONCLUSION: This index displays sufficient robustness to be used in pre-transplant decision making and may perform better than currently available tools.

Time-to-event analysis in economic evaluations: a comparison of modelling methods to assess the cost-effectiveness of transplanting a marginal quality kidney.

BACKGROUND: Economic-evaluations using decision analytic models such as Markov-models (MM), and discrete-event-simulations (DES) are high value adds in allocating resources. The choice of modelling method is critical because an inappropriate model yields results that could lead to flawed decision making. The aim of this study was to compare cost-effectiveness when MM and DES were used to model results of transplanting a lower-quality kidney versus remaining waitlisted for a kidney. METHODS: Cost-effectiveness was assessed using MM and DES. We used parametric survival models to estimate the time-dependent transition probabilities of MM and distribution of time-to-event in DES. MMs were simulated in 12 and 6 monthly cycles, out to five and 20-year time horizon. RESULTS: DES model output had a close fit to the actual data. Irrespective of the modelling method, the cycle length of MM or the time horizon, transplanting a low-quality kidney as compared to remaining waitlisted was the dominant strategy. However, there were discrepancies in costs, effectiveness and net monetary benefit (NMB) among different modelling methods. The incremental NMB of the MM in the 6-months cycle lengths was a closer fit to the incremental NMB of the DES. The gap in the fit of the two cycle lengths to DES output reduced as the time horizon increased. CONCLUSION: Different modelling methods were unlikely to influence the decision to accept a lower quality kidney transplant or remain waitlisted on dialysis. Both models produced similar results when time-dependant transition probabilities are used, most notable with shorter cycle lengths and longer time-horizons.

Donor Kidney Quality and Transplant Outcome: An Economic Evaluation of Contemporary Practice.

OBJECTIVES: The study had two main aims. First, we assessed the cost-effectiveness of transplanting deceased donor kidneys of differing quality levels based on the Kidney Donor Profile Index (KDPI). Second, we assessed the cost-effectiveness of remaining on the waiting list until a high-quality kidney becomes available compared to transplanting a lower-quality kidney. METHODS: A decision analytic model to estimate cost-effectiveness was developed using a Markov process. Separate models were developed for 4 separate KDPI bands, with higher values indicating lower quality. Models were simulated in 1-year cycles for a 20-year time horizon, with transitions through distinct health states relevant to the kidney recipient from the healthcare payer's perspective. Weibull regression was used to calculate the time-dependent transition probabilities in the base analysis. The impact uncertainty arising in model parameters was included by probabilistic sensitivity analysis using the Monte Carlo simulation method. Willingness to pay was considered as Australian $28 000. RESULTS: Transplanting a kidney of any quality is cost-effective compared to remaining on a waitlist. Transplanting a lower KDPI kidney is cost-effective compared to a higher KDPI kidney. Transplanting lower KDPI kidneys to younger patients and higher KDPI kidneys to older patients is also cost-effective. Depending on dialysis in hopes of receiving a lower KDPI kidney is not a cost-effective strategy for any age group. CONCLUSION: Efforts should be made by the health systems to reduce the discard rates of low-quality kidneys with the view of increasing the transplant rates.

Deceased donor kidney allocation: an economic evaluation of contemporary longevity matching practices.

BACKGROUND: Matching survival of a donor kidney with that of the recipient (longevity matching), is used in some kidney allocation systems to maximize graft-life years. It is not part of the allocation algorithm for Australia. Given the growing evidence of survival benefit due to longevity matching based allocation algorithms, development of a similar kidney allocation system for Australia is currently underway. The aim of this research is to estimate the impact that changes to costs and health outcomes arising from 'longevity matching' on the Australian healthcare system. METHODS: A decision analytic model to estimate cost-effectiveness was developed using a Markov process. Four plausible competing allocation options were compared to the current kidney allocation practice. Models were simulated in one-year cycles for a 20-year time horizon, with transitions through distinct health states relevant to the kidney recipient. Willingness to pay was considered as AUD 28000. RESULTS: Base case analysis indicated that allocating the worst 20% of Kidney Donor Risk Index (KDRI) donor kidneys to the worst 20% of estimated post-transplant survival (EPTS) recipients (option 2) and allocating the oldest 25% of donor kidneys to the oldest 25% of recipients are both cost saving and more effective compared to the current Australian allocation practice. Option 2, returned the lowest costs, greatest health benefits and largest gain to net monetary benefits (NMB). Allocating the best 20% of KDRI donor kidneys to the best 20% of EPTS recipients had the lowest expected incremental NMB. CONCLUSION: Of the four longevity-based kidney allocation practices considered, transplanting the lowest quality kidneys to the worst kidney recipients (option 2), was estimated to return the best value for money for the Australian health system.

Cost-utility analysis in chronic kidney disease patients undergoing kidney transplant; what pays? A systematic review.

BACKGROUND: Health systems are under pressure to deliver more effective care without expansion of resources. This is particularly pertinent to diseases like chronic kidney disease (CKD) that are exacting substantial financial burden to many health systems. The aim of this study is to systematically review the Cost Utility Analysis (CUA) evidence generated across interventions for CKD patients undergoing kidney transplant (KT). METHODS: A systemic review of CUA on the interventions for CKD patients undergoing KT was carried out using a search of the MEDLINE, CINAHL, EMBASE, PsycINFO and NHS-EED. The CHEERS checklist was used as a set of good practice criteria in determining the reporting quality of the economic evaluation. Quality of the data used to inform model parameters was determined using the modified hierarchies of data sources. RESULTS: A total of 330 articles identified, 16 met the inclusion criteria. Almost all (n = 15) the studies were from high income countries. Out of the 24 characteristics assessed in the CHEERS checklist, more than 80% of the selected studies reported 14 of the characteristics. Reporting of the CUA were characterized by lack of transparency of model assumptions, narrow economic perspective and incomplete assessment of the effect of uncertainty in the model parameters on the results. The data used for the economic model were satisfactory quality. The authors of 13 studies reported the intervention as cost saving and improving quality of life, whereas three studies were cost increasing and improving quality of life. In addition to the baseline analysis, sensitivity analysis was performed in all the evaluations except one. Transplanting certain high-risk donor kidneys (high risk of HIV and Hepatitis-C infected kidneys, HLA mismatched kidneys, high Kidney Donor Profile Index) and a payment to living donors, were found to be cost-effective. CONCLUSIONS: The quality of economic evaluations reviewed in this paper were assessed to be satisfactory. Implementation of these strategies will significantly impact current systems of KT and require a systematic implementation plan and coordinated efforts from relevant stakeholders.

Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models.

INTRODUCTION: Machine learning has been increasingly used to develop predictive models to diagnose different disease conditions. The heterogeneity of the kidney transplant population makes predicting graft outcomes extremely challenging. Several kidney graft outcome prediction models have been developed using machine learning, and are available in the literature. However, a systematic review of machine learning based prediction methods applied to kidney transplant has not been done to date. The main aim of our study was to perform an in-depth systematic analysis of different machine learning methods used to predict graft outcomes among kidney transplant patients, and assess their usefulness as an aid to decision-making. METHODS: A systemic review of machine learning methods used to predict graft outcomes among kidney transplant patients was carried out using a search of the Medline, the Cumulative Index to Nursing and Allied Health Literature, EMBASE, PsycINFO and Cochrane databases. RESULTS: A total of 295 articles were identified and extracted. Of these, 18 met the inclusion criteria. Most of the studies were published in the United States after 2010. The population size used to develop the models varied from 80 to 92,844, and the number of features in the models ranged from 6 to 71. The most common machine learning methods used were artificial neural networks, decision trees and Bayesian belief networks. Most of the machine learning based predictive models predicted graft failure with high sensitivity and specificity. Only one machine learning based prediction model had modelled time-to-event (survival) information. Seven studies compared the predictive performance of machine learning models with traditional regression methods and the performance of machine learning methods was found to be mixed, when compared with traditional regression methods. CONCLUSION: There was a wide variation in the size of the study population and the input variables used. However, the prediction accuracy provided mixed results when machine learning and traditional predictive methods are compared. Based on reported gains in predictive performance, machine learning has the potential to improve kidney transplant outcome prediction and aid medical decision making.

Frequency and Consequences of Acute Kidney Injury in Patients With CKD: A Registry Study in Queensland Australia.

BACKGROUND: Acute kidney injury (AKI) contributes to and complicates chronic kidney disease (CKD). We describe AKI documented in hospital encounters in patients with CKD from the CKD Queensland registry. STUDY DESIGN: A retrospective cohort study during 2011 to 2016. SETTING & PARTICIPANTS: Participants had been admitted to a hospital in Queensland. PREDICTORS: AKI was identified from International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification codes. OUTCOMES: All-cause mortality with or without kidney replacement therapy (KRT), start-up KRT and maintenance KRT, costs of care. ANALYTICAL APPROACH: Time to outcomes for those with versus without AKI was evaluated using Cox regression models. Mann-Whitney test was used to compare number of admissions, hospitalized days and costs by AKI status. RESULTS: Among 6,365 patients followed up for up to 5.4 years, 2,199 (35%) had 4,711 hospital encounters with an AKI diagnosis. Those with AKI were older (68 vs 64 years old), were more often men (36.7% vs 32.2%; P < 0.001), had more advanced CKD stages (stage 3b, 34%; stage 4, 35%; and stage 5, 10%), had more admissions (12 vs 5; P < 0.001), and stayed in the hospital longer (56 vs 14 days; P < 0.001) than those without AKI. Almost 90% of AKI admissions were through the emergency department. Of those with AKI, 554 (25%) subsequently died without any form of KRT and 285 (13%) started KRT, compared with 282 (6.8%) who died and 315 (7.6%) who started KRT among those without AKI; P < 0.001 for each. Adjusted for other significant factors, hazard ratios for all deaths or death without KRT were 2.95 (95% CI, 2.56-3.39; P < 0.001) and 3.02 (95% CI, 2.60-3.51; P < 0.001), respectively, in patients with AKI relative to those without AKI. The hazard ratio for all KRT was 1.40 (95% CI, 1.18-1.66; P < 0.001), and for maintenance KRT was 1.21 (95% CI, 0.98-1.48; P = 0.07). Mean total hospital cost in patients with AKI was more than triple that of patients with no AKI (A $93,042 vs A $30,778; P < 0.001). LIMITATIONS: These findings may not be generalizable to CKD populations from the general community or in other health care environments. CONCLUSIONS: AKI is associated with strikingly increased deaths, increased rates of KRT, and higher hospital costs.

Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study.

Background: A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients.  Several kidney graft outcome prediction models, developed using machine learning methods, are available in the literature.  However, most of those models used small datasets and none of the machine learning-based prediction models available in the medical literature modelled time-to-event (survival) information, but instead used the binary outcome of failure or not. The objective of this study is to develop two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using time-to-event data in a large national dataset from Australia.   Methods: The dataset provided by the Australia and New Zealand Dialysis and Transplant Registry will be used for the analysis. This retrospective dataset contains the cohort of patients who underwent a kidney transplant in Australia from January 1 st, 2007, to December 31 st, 2017.  This included 3,758 live donor transplants and 7,365 deceased donor transplants.  Three machine learning methods (survival tree, random survival forest and survival support vector machine) and one traditional regression method, Cox proportional regression, will be used to develop the two predictive models.  The best predictive model will be selected based on the model's performance. Discussion: This protocol describes the development of two separate machine learning-based predictive models to predict graft failure following live and deceased donor kidney transplant, using a large national dataset from Australia.   Furthermore, these two models will be the most comprehensive kidney graft failure predictive models that have used survival data to model using machine learning techniques.  Thus, these models are expected to provide valuable insight into the complex interactions between graft failure and donor and recipient characteristics.

The economic burden of posttransplant events in renal transplant recipients in Europe.

BACKGROUND: This study aims to describe the healthcare resource utilization and costs of managing renal posttransplant patients over 3 years posttransplant in nine European countries and to stratify them by year 1 glomerular filtration rate (GFR). METHODS: A retrospective observational and database analysis of renal transplant patients and a physician questionnaire study were conducted to collect recipient and donor characteristics, posttransplant events, and healthcare resource utilization related to these posttransplant events. In each country, local published costs were applied to the resource use identified. The results were stratified by the patient GFR reading at a time point 1 year after renal transplant. RESULTS: The database study identified 3,181 patients who met the inclusion criteria, along with 2,818 transplants carried out in the centers surveyed by questionnaire. Total 3-year costs derived from the questionnaire analysis vary depending on local treatment practices, from a minimum of &OV0556;33,602 per patient in the Czech Republic to &OV0556;77,461 per patient in the Netherlands. Consistently across countries, estimated costs appear to decrease with improved graft functioning status (increased GFR) at 1 year. The average 3-year costs, discounting immunosuppression therapy and certain posttransplant events, per patient with a GFR greater than or equal to 60 at 1 year are estimated to be around 35% lower than those with 15≤GFR<30. CONCLUSION: This study demonstrates that in Europe, worsening posttransplant renal function may contribute to substantive increases in resource use, with some variation across regions. Therefore, management strategies that promote renal function after transplantation have the potential to provide important resource savings.

Long-term progression of abnormal glucose tolerance and its relationship with the metabolic syndrome after kidney transplantation.

BACKGROUND: Metabolic syndrome (MS) diagnosed early after kidney transplantation is a risk factor for developing new-onset diabetes. The aim of this study was to examine whether glucose intolerance and MS identified late after transplantation influence the progression of glycemic abnormalities in kidney transplant recipients. METHODS: This is a retrospective study in which 76 non-diabetic renal transplant recipients underwent oral glucose tolerance tests (OGTT) in 2005 to 2006 (baseline) and then in 2011 to 2012 (follow-up). MS was identified using the International Diabetes Federation criteria and OGTT was interpreted according to the WHO classification. RESULTS: At follow-up, median time from transplantation was 11.1 years (range 6.2-23.8). Mean 0-hour and 2-hour plasma glucose levels were significantly higher at follow-up compared to baseline (5.7 ± 0.7 vs. 5.9 ± 0.9 mmol/L, P=0.03 and 6.7 ± 1.9 vs. 7.5 ± 2.8 mmol/L, P=0.03, respectively). The proportion of patients with an abnormal OGTT increased from 42% at baseline to 61% at follow-up (P=0.007). Patients with MS were more likely to progress to a higher degree of glucose intolerance compared to those without MS (58% vs. 27%, P=0.01). On multivariable logistic regression adjusted for age and gender, MS was significantly associated with the progression of glucose intolerance (OR 3.5, CI 1.2-9.9, P=0.01), as was a fasting glucose greater than 5.6 mmol/L (OR 4.8, CI 1.6-14.8, P=0.006). CONCLUSION: MS is a risk factor for the progression of glucose intolerance in renal transplant recipients in the late posttransplant period. Therefore, MS has to be considered in tandem with OGTT results to assess cardiovascular risk.

Complications associated with new-onset diabetes after kidney transplantation.

New-onset diabetes mellitus after kidney transplantation (NODAT) is widely acknowledged to be associated with increased morbidity and mortality, as well as poor quality of life. Clear evidence links the occurrence of NODAT to accelerated progression of some macrovascular and/or microvascular complications. However, the evidence that some complications commonly attributed to diabetes mellitus occur in the context of transplantation lacks robustness. Certain complications are transplantation-specific and prevalent, but others are not frequently observed or documented. For this reason, it is essential that clinicians are aware of the array of potential complications associated with NODAT in kidney allograft recipients. Rather than simply translating evidence from the general population to the high-risk transplant recipient, this Review aims to provide specific guidance on diabetes-related complications in the context of a complex transplantation environment.

Update on dialysis economics in the UK.

The burgeoning population of patients requiring renal replacement therapy contributes a disproportionate strain on National Health Service resources. Although renal transplantation is the preferred treatment modality for patients with established renal failure, achieving both clinical and financial advantages, limitations to organ donation and clinical comorbidities will leave a significant proportion of patients with established renal failure requiring expensive dialysis therapy in the form of either hemodialysis or peritoneal dialysis. An understanding of dialysis economics is essential for both healthcare providers and clinical leaders to establish clinically efficient and cost-effective treatment modalities that maximize service provision. In light of changes to the provision of healthcare funds in the form of "Payment by Results," it is imperative for UK renal units to adopt clinically effective and financially accountable dialysis programs. This article explores the role of dialysis economics and implications for UK renal replacement therapy programs.

Risk factors for new-onset diabetes after kidney transplantation.

New-onset diabetes after transplantation, a common complication following kidney transplantation, is associated with adverse patient and graft outcomes. Our understanding of the risk factors associated with this metabolic disorder is improving and both transplantation-specific and nonspecific factors are clearly involved. Knowledge of these risk factors is important so that clinicians can implement pre-emptive risk stratification strategies and to guide therapeutic, risk-attenuation approaches in patients who develop transplant-associated hyperglycemia. In this Review, we explore the current understanding of the diverse range of risk factors that contribute to abnormal glucose metabolism after transplantation, with the aim of helping to guide clinical decision-making using appropriate risk stratification.

Impending hyperglycemia in normoglycemic renal transplant recipients--an experimental predictive surrogate.

BACKGROUND: beta-Cell dysfunction and insulin resistance combine to cause new-onset diabetes after transplantation. The product of these two parameters, quantitatively measured as disposition index (DI), is a mathematical constant in normoglycemia and declines in advance of impending hyperglycemia. The aim of this study was to derive a simple surrogate for the DI to expose predysglycemic abnormalities posttransplantation. METHODS: First-phase insulin secretion and sensitivity were determined by mathematical minimal model analysis of 58 frequently sampled, intravenous glucose tolerance tests in 58 non-diabetic renal transplant recipients and correlated against surrogate indexes based on fasting blood samples. Products of insulin secretion/resistance indexes were correlated against calculated DI, regression analysis performed for hyperbolic compatibility, autocorrelation studies conducted, and surrogates tested in various subgroups of renal transplant recipients to ensure robustness in a heterogeneous group. RESULTS: The best correlation was achieved with "HOMA(sec) (first-phase insulin secretion)xMcAuley's index (insulin resistance)" (r=0.594, P<0.001). Regression analysis was consistent with a mathematical hyperbola (ln HOMA(sec) vs. ln McAuley's index, r=-0.639 [95% confidence interval, -1.772 to -0.950]), statistical autocorrelation was excluded (in a subset of 20 patients with repeat metabolic investigations), and the surrogate remained valid in different subgroups of transplant recipients. CONCLUSIONS: Our surrogate "HOMA(sec)xMcAuley's index," requiring only fasting glucose, insulin, and triglycerides, is a simple and noninvasive surrogate for the DI. Its predictive utility for identifying impending hyperglycemia posttransplantation should be investigated further to ascertain whether its experimental nature can translate to clinical validity.

Insulin resistance indexes in renal transplant recipients maintained on tacrolimus immunosuppression.

BACKGROUND: Insulin resistance is common posttransplantation and contributes to both new onset diabetes after transplantation and the metabolic syndrome. Insulin resistance indexes have never been validated in transplant recipients on tacrolimus compared with cyclosporine, although it is more diabetogenic. We aimed to assess these indexes in renal transplant recipients on tacrolimus as primary immunosuppressant. METHODS: Retrospective analysis of 76 frequently sampled, intravenous glucose tolerance tests (for insulin sensitivity) in 38 nondiabetic renal transplant recipients on tacrolimus-centered immunosuppression. Indexes tested were fasting glucose/insulin ratio, homeostasis model assessment (HOMA) index, 1/HOMA, log (HOMA), quantitative insulin sensitivity check index, and the McAuley's index. Indexes were also compared against waist/hip ratio and C-reactive protein (CRP). Multivariate linear regression analysis was performed to determine independent variables predictive for insulin resistance. RESULTS: Insulin sensitivity successfully correlated with all indexes: fasting glucose/insulin ratio (r=0.246, P=0.033), HOMA index (r=-0.240, P=0.038), 1/HOMA (r=0.282, P=0.014), log (HOMA) (r=-0.316, P=0.006), quantitative insulin sensitivity check index (r=0.320, P=0.005), and McAuley's index (r=0.323, P=0.005). McAuley's index also correlated strongest with waist/hip ratio (r=-0.425, P<0.001). All indexes failed to correlate with CRP. Variables independently associated with insulin sensitivity were HbA1c (r=0.189, P=0.019), pulse pressure (r=0.146, P=0.021), and CRP (r=0.210, P=0.010). CONCLUSIONS: Insulin resistance indexes are valid in transplant recipients taking tacrolimus, with McAuley's index the strongest surrogate.

Comparison of rival metabolic syndrome classifications against pathophysiological markers in renal transplant recipients.

BACKGROUND: Metabolic syndrome posttransplantation is associated with adverse outcomes. Diagnostic controversy exists, with adult treatment panel (ATP) III and International Diabetes Federation (IDF) classifications differing in prerequisite requirement of central obesity. In addition, correlation between classifications and putative pathophysiological mechanisms posttransplantation are lacking and may be obscured by immunosuppressants. We compared the two classifications against insulin resistance, subclinical inflammation, and central obesity in renal transplant recipients. METHODS: Ninety-six sets of metabolic investigations were analyzed in a cohort of 58 nondiabetic renal transplant recipients. Mathematical model analysis of the frequently sampled, intravenous glucose tolerance test was performed to determine insulin sensitivity (10(-5)min(-1)/mU/mL). We used waist/hip ratio as a surrogate for central obesity and C-reactive protein (mg/L) for subclinical inflammation, respectively. Clinical/biochemical parameters were also assessed at each metabolic investigation. RESULTS: Fifty-nine percent of the study cohort was classed with metabolic syndrome using ATP III criteria, but only 43% using IDF criteria. IDF-classified recipients were more likely to have insulin resistance (3.7 vs. 4.9, P=0.034), raised waist/hip ratio (0.96 vs. 0.88, P<0.001), and elevated C-reactive protein (7.2 vs. 2.9, P=0.004) than those without the syndrome. Using ATP III criteria, there was a significant association with waist/hip ratio alone (syndrome vs. no syndrome, 0.95 vs. 0.86, P<0.001). Recipients with IDF-classified metabolic syndrome had significantly lower estimated glomerular filtration rate (mL/min) compared with those without (61.8 vs. 73.6, P=0.015). CONCLUSION: The IDF-classified metabolic syndrome is superior to ATP III for association with pathophysiological mechanisms posttransplantation.