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.

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.

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.

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-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.

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.

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.

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.

Influence of lifestyle modification in renal transplant recipients with postprandial hyperglycemia.

INTRODUCTION: Lifestyle modification is recommended as first-line therapy to manage new-onset diabetes after transplantation (NODAT) and impaired glucose tolerance (IGT). No data currently demonstrate the efficacy of this approach specifically for transplant recipients. This study aimed to assess the benefit of intensive lifestyle modification in this high-risk group and to contrast this with the natural evolution of glucose metabolism after transplantation. METHODS: Baseline oral glucose tolerance test (OGTT) stratified 115 patients into two groups. Group 1 had glucose intolerance, IGT (n=28) and NODAT (n=8), and received intensive lifestyle modification (dietician referral, exercise program, weight loss advice). Group 2 had normal glucose tolerance (n=79) and received lifestyle modification leaflets. Both groups had follow-up OGTT after 6 months to assess change in glycemic status. RESULTS: Excluding all patients who received steroid weaning or withdrawal as part of their management, 111 patients were included in the analysis. Lifestyle modification in group 1 resulted in 15% improvement in 2-hr postprandial glucose versus 12% deterioration in group 2. In group 1, 44% (n=11) of IGT patients developed normal glucose tolerance, whereas only 4% (n=1) developed NODAT. Fifty-eight percent (n=4) of NODAT patients showed improvement (29% to IGT and 29% to normal). Glucose metabolism deteriorated in group 2 with 14% (n=10) developing IGT and 3% (n=2) developing NODAT. CONCLUSIONS: Glucose metabolism can deteriorate in transplant recipients despite passive lifestyle modification advice. This study shows active lifestyle modification benefits high-risk transplant recipients with glucose intolerance and should be aggressively pursued.

The cost of renal dialysis in a UK setting--a multicentre study.

BACKGROUND: The UK National Health Service (NHS) will fund renal services using Payment by Results (PbR), from 2009. Central to the success of PbR will be the creation of tariffs that reflect the true cost of medical services. We have therefore estimated the cost of different dialysis modalities in the Cardiff and Vale NHS Trust and six other hospitals in the UK. METHODS: We used semi-structured interviews with nephrologists, head nurses and business managers to identify the steps involved in delivering the different dialysis modalities. We assigned costs to these using published figures or suppliers' published price lists. The study used mixed costing methods. Dialysis costs were estimated by a combination of microcosting and a top-down approach. Where we did not have access to detailed accounts, we applied values for Cardiff. RESULTS: The most efficient modalities were automated peritoneal dialysis (APD) and continuous ambulatory peritoneal dialysis (CAPD), the mean annual costs of which were pound21 655 and pound15 570, respectively. Hospital-based haemodialysis (HD) cost pound35 023 per annum and satellite-unit-based HD cost pound32 669. The cost of home-based HD was pound20 764 per year (based on data from only one unit). The main cost drivers for PD were the costs of solutions and management of anaemia. For HD they were costs of disposables, nursing, the overheads associated with running the unit and management of anaemia. CONCLUSIONS: Renal tariffs for PbR need to reflect the true cost of dialysis provision if choices about modalities are not to be influenced by erroneous estimates of cost. Knowledge of the true costs of modalities will also maximize the number of established renal failure patients treated by dialysis within the limited funds available from the NHS.

The use of oral glucose tolerance tests to risk stratify for new-onset diabetes after transplantation: An underdiagnosed phenomenon.

BACKGROUND: Fasting glucose measurements are insensitive at detecting new-onset diabetes after transplantation (NODAT) and ignore the diagnosis of impaired glucose tolerance (IGT). Both NODAT and IGT confer a higher risk of developing cardiovascular disease. IGT is also a risk factor for NODAT. The aim of this study was to use an oral glucose tolerance test (OGTT) to risk stratify for NODAT and IGT in renal transplant recipients and to relate cardiovascular and phenotypic risk with glycemic dysregulation. METHODS: In all, 858 renal transplant recipients are under follow up at the University Hospital of Wales, Cardiff, UK. Excluded patients had pretransplant diabetes (78), NODAT (89), or were transplanted less than six months (47), leaving 646 recipients. All remaining recipients with two fasting blood glucoses between 5.6 and 6.9 mmol/L were invited to have an OGTT. A diagnosis of NODAT, IGT, and impaired fasting glucose (IFG) was based on World Health Organization guidelines. RESULTS: We identified 134 patients who fulfilled the inclusion criteria, of whom 122 had an OGTT (91% of cohort). In all, 51% of patients were found to have abnormal glucose metabolism: 10% NODAT, 14% combined IGT/IFG, 9% IGT alone, and 18% IFG alone. Clinical phenotype was not predictive of diabetic risk on multivariate analysis. CONCLUSIONS: Our results confirm fasting glucose underestimates the prevalence of NODAT and ignores the prevalence of IGT. These findings suggest routine use of an OGTT in renal transplant recipients is a valuable clinical tool to risk stratify each patient for the development of NODAT and cardiovascular disease.

Evaluation of the cost effectiveness of sirolimus versus tacrolimus for immunosuppression following renal transplantation in the UK.

INTRODUCTION: Immunosuppressive therapy is required to prevent graft rejection. Calcineurin inhibitors such as tacrolimus are paradoxically toxic to the kidney, whereas sirolimus (rapamycin; Rapamune) is not generally associated with the nephrotoxicity of CNIs. The purpose of this study was to evaluate the relative cost utility of sirolimus versus tacrolimus for the primary prevention of graft rejection in renal transplant recipients in the UK. METHODS: A stochastic simulation model was constructed using clinical trial and observational data comparing the two treatments. Time duration was up to 20 years. Costs were from a UK NHS perspective, valued at 2003 prices and discounted at 6%. Benefits were discounted at 1.5%. Simulated events included patient and graft survival, haemodialysis, peritoneal dialysis, re-transplants and acute rejection. Costs were summed for events and various maintenance therapies. Utility was differentially accredited depending upon survival and using the alternative renal replacement therapies. Outcome was predicted using post-transplant creatinine levels up to 3 years. Extensive statistical economic and sensitivity analyses were undertaken. RESULTS: Over the 10-year horizon, sirolimus gained 0.72 years (discounted) of functioning graft over tacrolimus, resulting in an incremental cost per year of functioning graft that was dominant. Over a 20-year time horizon, the cost effectiveness of sirolimus over tacrolimus further improved with an average discounted gain in years of a functioning graft of 1.8 years, resulting in an incremental cost-utility ratio that was also dominant. The number of haemodialysis events was 48,243 for sirolimus recipients versus 127,829 for those receiving tacrolimus and peritoneal dialysis events 40,872 versus 105,249, respectively. Similar values were obtained when real-life observational data on tacrolimus use in Cardiff, Wales were entered into the model. Using data from Cardiff, sirolimus remained dominant over tacrolimus under all scenarios. CONCLUSION: Our study suggests that sirolimus may be more cost effective than tacrolimus for the primary prevention of graft rejection in renal transplant recipients in the UK. Sirolimus was economically 'dominant' under almost all scenarios investigated. This finding was robust using statistical economic analysis and univariate sensitivity analysis.

Evaluation of the cost-effectiveness of sirolimus versus cyclosporin for immunosuppression after renal transplantation in the United Kingdom.

OBJECTIVE: The purpose of this study was to evaluate the cost-effectiveness of sirolimus compared with cyclosporin for the postsurgical management of renal transplant recipients, from the perspective of the UK National Health Service and the Personal Social Service. METHODS: A discrete event stochastic simulation model was developed to evaluate both cost-effectiveness and cost utility over 10 and 20 years after transplant using historical data on 937 renal transplant recipients from the University Hospital of Wales in Cardiff, United Kingdom. The simulation was designed to forecast the incidence of acute rejection events, graft failure, retransplant, frequency of hemodialysis (HD) and peritoneal dialysis (PD), and death. Cox proportional hazard models were derived from historical transplant data, in which 1-, 2-, and 3-year post-transplant serum creatinine levels were used as the key drivers for predicting graft success and survival. Costs were reported as year-2003 UK pounds sterling (1 UK pound = US $1.76). Probabilistic sensitivity analysis was conducted and results reported with particular attention to 2 threshold values, 30,000/QALY and 20,000/QALY RESULTS: Over a 10-year time horizon, treatment with sirolimus was projected to produce a gain of 0.60 discounted year of functioning graft with a cost savings of 276 UK pounds per patient. Over a 20-year time horizon these benefits increased to 1.59 discounted years of functioning graft and a cost savings of 7405 UK pounds per patient. Using sensitivity analysis of the 10-year model, the only factors found to cause the probability of exceeding a 30,000 ceiling to be >5% were the proportion of subjects maintaining continuous graft function and the use of low-dose cyclosporin. With the 20-year model, sirolimus maintained cost-effectiveness across most scenarios in sensitivity analysis. CONCLUSIONS: In this model analysis, sirolimus was cost-effective compared with cyclosporin for 10 to 20 years after renal transplantation in the United Kingdom, from the perspective of the UK National Health Service and Personal Social Service.

Patterns of graft and patient survival following renal transplantation and evaluation of serum creatinine as a predictor of survival: a review of data collected from one clinical centre over 34 years.

BACKGROUND: The pattern of renal transplantation has never been described since the introduction of the technique. The purpose of this study was therefore to characterise the pattern of renal transplantation from 1967 to 2000, focusing on renal graft function as a predictor of survival. METHODS: This study was a retrospective analysis of an electronic database. The setting was a single renal transplant centre in the United Kingdom covering a population of 2.2 million and included patients who received at least one renal transplant over the study period (n = 1516). The main outcome measures were patient and graft survival, acute rejection episodes and patterns of graft function, as measure by creatinine levels. RESULTS: There were 559 (36.8%) female patients; 109 (7.2%) patients had pre-existing diabetes. Patient survival was adversely affected by increased age at transplant (p < 0.001): 5-year patient survival from first transplant was 82% for patients aged 0 to 17 years, 80% for 18 to 49 years and 61% for > 49 years. Pre-existing diabetes also adversely affected survival (p < 0.01): 5-year graft survival was 63% for patients with diabetes versus 74% for those without. Graft survival was significantly associated with serum creatinine levels recorded 1 year post-primary transplant (p < 0.001) and with three or more acute rejection episodes (p < 0.05). Neither gender nor diabetes status were statistically significant in predicting graft survival. The number of acute rejection episodes was significantly greater in patients with pre-existing diabetes than those without (61% versus 42%, respectively; p < 0.001). There were no differences in the number of acute rejection episodes occurring across age groups. CONCLUSION: Patient and graft survival improved markedly over the 34-year study period, although patient survival has changed little since 1990. Serum creatinine levels are a reliable predictor of graft survival.

A phase III prospective, randomized study to evaluate concentration-controlled sirolimus (rapamune) with cyclosporine dose minimization or elimination at six months in de novo renal allograft recipients.

BACKGROUND: This study evaluated the safety and efficacy of sirolimus plus steroids as a maintenance regimen with or without small-dose cyclosporine (CsA) adjunctive therapy in renal transplant recipients. METHODS: A total of 133 recipients of kidney allograft transplantations recruited in the United Kingdom and Ireland were enrolled into the study and are presented in this interim analysis. In the first 3 months, all patients received CsA plus sirolimus and small-dose steroids after transplantation. At 3 months, patients were randomized 1:1 to CsA elimination (e)CsA or CsA dose minimization (m)CsA. Dosing of agents was concentration-controlled and open label. RESULTS: Patient and graft survival were 97.7% and 95.5%, respectively (n = 133), whereas the biopsy-proven acute rejection rate in the first 6 months was 19.5% (26 episodes in 133 patients); incidents of acute rejection rates comprised 22 episodes (16.5%) during the first 3 months of the study and four episodes (3%) after randomization. Eighty-seven patients were randomized to receive eCsA or mCsA. At 6 months, creatinine clearance was significantly higher in the eCsA group versus mCsA group, respectively (65 mL/min vs. 57 mL/min; P = 0.027). There was no significant difference in serum cholesterol, triglycerides, low-density lipoprotein, or high-density lipoprotein levels between the groups. CONCLUSION: These data demonstrate that withdrawal of CsA from a small-dose sirolimus maintenance regimen is safe and is associated with an improvement in renal function. The study also suggests that the addition of small-dose CsA to a sirolimus maintenance regimen does not increase immunosuppressive efficacy.

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.

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.