Quantifying the benefit of early living-donor renal transplantation with a simulation model of the Dutch renal replacement therapy population.

BACKGROUND: Early living-donor transplantation improves patient- and graft-survival compared with possible cadaveric renal transplantation (RTx), but the magnitude of the survival gain is unknown. For patients starting renal replacement therapy (RRT), we aimed to quantify the survival benefit of early living-donor transplantation compared with dialysis and possible cadaveric transplantation and to estimate the population benefit from increasing the early transplantation rate. METHODS: We used a decision-analytic computer-simulation model, with a lifetime time horizon, simulating patients starting RRT, using data from the Dutch End-Stage Renal Disease Registry and published data. We compared the (quality adjusted) life expectancy (LE) of 'early living-donor RTx' and 'dialysis' (with possible cadaveric RTx if available). RESULTS: LE and quality-adjusted LE benefits of the early living-donor RTx compared with the dialysis strategy for 40-year-old patients ranged from 7.5 to 9.9 life years (LYs) [6.7-8.8 quality-adjusted life years (QALYs)] depending on the primary renal disease. For 70-year-old patients, the benefit was 4.3-6.0 LYs (4.3-6.0 QALYs). Increasing the early transplantation rate from currently 5.8 to 22.2% (the highest in Europe) would increase average LE by 1.2 LYs and total LE for annual incident cases in the Netherlands by >1800 LYs. CONCLUSIONS: Efforts to increase early living-donor RTx could potentially substantially increase LE for patients starting RRT, especially in younger patients.

Propensity scores in the presence of effect modification: A case study using the comparison of mortality on hemodialysis versus peritoneal dialysis.

PURPOSE: To control for confounding bias from non-random treatment assignment in observational data, both traditional multivariable models and more recently propensity score approaches have been applied. Our aim was to compare a propensity score-stratified model with a traditional multivariable-adjusted model, specifically in estimating survival of hemodialysis (HD) versus peritoneal dialysis (PD) patients. METHODS: Using the Dutch End-Stage Renal Disease Registry, we constructed a propensity score, predicting PD assignment from age, gender, primary renal disease, center of dialysis, and year of first renal replacement therapy. We developed two Cox proportional hazards regression models to estimate survival on PD relative to HD, a propensity score-stratified model stratifying on the propensity score and a multivariable-adjusted model, and tested several interaction terms in both models. RESULTS: The propensity score performed well: it showed a reasonable fit, had a good c-statistic, calibrated well and balanced the covariates. The main-effects multivariable-adjusted model and the propensity score-stratified univariable Cox model resulted in similar relative mortality risk estimates of PD compared with HD (0.99 and 0.97, respectively) with fewer significant covariates in the propensity model. After introducing the missing interaction variables for effect modification in both models, the mortality risk estimates for both main effects and interactions remained comparable, but the propensity score model had nearly as many covariates because of the additional interaction variables. CONCLUSION: Although the propensity score performed well, it did not alter the treatment effect in the outcome model and lost its advantage of parsimony in the presence of effect modification.

Reimbursement and economic factors influencing dialysis modality choice around the world.

The worldwide incidence of kidney failure is on the rise and treatment is costly; thus, the global burden of illness is growing. Kidney failure patients require either a kidney transplant or dialysis to maintain life. This review focuses on the economics of dialysis. Alternative dialysis modalities are haemodialysis (HD) and peritoneal dialysis (PD). Important economic factors influencing dialysis modality selection include financing, reimbursement and resource availability. In general, where there is little or no facility or physician reimbursement or payment for PD, the share of PD is very low. Regarding resource availability, when centre HD capacity is high, there is an incentive to use that capacity rather than place patients on home dialysis. In certain countries, there is interest in revising the reimbursement structure to favour home-based therapies, including PD and home HD. Modality selection is influenced by employment status, with an association between being employed and PD as the modality choice. Cost drivers differ for PD and HD. PD is driven mainly by variable costs such as solutions and tubing, while HD is driven mainly by fixed costs of facility space and staff. Many cost comparisons of dialysis modalities have been conducted. A key factor to consider in reviewing cost comparisons is the perspective of the analysis because different costs are relevant for different perspectives. In developed countries, HD is generally more expensive than PD to the payer. Additional research is needed in the developing world before conclusive statements may be made regarding the relative costs of HD and PD.

Development of a questionnaire to assess the quality of care in Dutch dialysis centers from the patient's perspective.

This study aimed to develop and test a questionnaire in order to assess the quality of care in Dutch dialysis centers from the patient's perspective. The questionnaire is referred to as the Quality of Care in Dialysis centers Questionnaire. Focus group sessions were organized and the results were transformed into a 68-item test version of the Quality of Care in Dialysis centers Questionnaire. Factor analyses and item reduction were performed to construct the Quality of Care in Dialysis centers Questionnaire. The questionnaire has four dimensions: doctors, nurses, other staff members and facilities; with eight descriptive items plus one item to measure satisfaction per dimension. A visual analog scale was added to determine overall satisfaction. The Quality of Care in Dialysis centers Questionnaire is used in Dutch dialysis centers. Further research should be conducted to establish preference weights per dimension on the basis of the visual analog scale scores.

Economic impact of extended time on peritoneal dialysis as a result of using polyglucose: the application of a Markov chain model to forecast changes in the development of the ESRD programme over time.

BACKGROUND: The use of polyglucose as a peritoneal dialysis (PD) fluid extends time on PD treatment. It is anticipated, therefore, that the share of patients treated with PD will be positively influenced. The relationship between extension of PD treatment time and an increase of the PD treatment share, however, is complex and needs further investigation. In this paper, a Markov chain model was applied to investigate the impact of extended time on PD treatment for the PD share in all dialysis patients in The Netherlands. Furthermore, the economic impact of the extended time on treatment (ETOT) was explored. METHODS: Scenarios were forecast over a 10 year period using aggregate data from the End-Stage Renal Registry in The Netherlands (Renine). Three scenarios were simulated in which the median PD technique survival was extended by 8, 10 and 12 months. Two other scenarios explored the impact of the combined effect of ETOT of 10 months together with a 10% and 20% increase of PD inflow shares. Reductions of costs to society due to ETOT were estimated using Dutch cost data on renal replacement therapies. RESULTS: PD share increases from 30.0% in the null scenario to 34.5% in the scenario with an ETOT of 10 months and an increased PD inflow share of 20%. The reduction in total costs to society of the renal replacement therapies is 0.96%. The average societal costs per discounted patient year for haemodialysis (HD) are 84 100 euros. For PD, these costs are 60 300 euros. A shift from HD to PD results in average cost savings of 28% per patient year. CONCLUSIONS: In view of high dialysis costs to society, a reduction of 0.96% can be considered to be relevant for healthcare policy makers.

Health profiles and health preferences of dialysis patients.

BACKGROUND: Health-related quality of life (HRQOL) of haemodialysis (HD) and peritoneal dialysis (PD) patients has been assessed with health profiles and health preferences methods. Few studies have used both types of HRQOL instruments. The main objective of this study was to assess the relationship between information from the two types of HRQOL instruments in dialysis patients. METHODS: We interviewed 135 patients, using two health profiles (Short Form 36 and EuroQol/EQ-5D) and two health preferences methods (Standard Gamble and Time Trade Off). Socio-demographic, clinical, and treatment-related background data were collected from patient charts and during the interview. Relationships between the outcome measures were assessed with Pearson correlation coefficients. Multiple regression models were used to study the relationship of HRQOL outcomes to background variables. RESULTS: The HRQOL of dialysis patients as measured with health profiles was severely impaired. The health preferences scores were higher (0.82-0.89) than scores previously reported in the literature. Correlations between health profiles and health preferences were poor to modest. HRQOL outcomes were poorly explained by background characteristics. Differences between HD and PD groups could not be demonstrated. CONCLUSIONS: Health profiles and health preferences represent different aspects of HRQOL. An impaired health status may not be reflected in the preference scores. Coping strategies and other attitudes towards health may affect the preference scores more than they influence health profile outcomes. The added value of health preferences methods in clinical research is limited.