Cost-effectiveness Analysis of Vascular Access Referral Policies in CKD

BACKGROUND

The optimal timing of vascular access referral for patients with chronic kidney disease who may need hemodialysis (HD) is a pressing question in nephrology. Current referral policies have not been rigorously compared with respect to costs and benefits and do not consider patient-specific factors such as age.

STUDY DESIGN

Monte Carlo simulation model.

SETTING & POPULATION

Patients with chronic kidney disease, referred to a multidisciplinary kidney clinic in a universal health care system.

MODEL, PERSPECTIVE, & TIMEFRAME

Cost-effectiveness analysis, payer perspective, lifetime horizon.

INTERVENTION

The following vascular access referral policies are considered: central venous catheter (CVC) only, arteriovenous fistula (AVF) or graft (AVG) referral upon HD initiation, AVF (or AVG) referral when HD is forecast to begin within 12 (or 3 for AVG) months, AVF (or AVG) referral when estimated glomerular filtration rate is <15 (or <10 for AVG) mL/min/1.73m².

OUTCOMES

Incremental cost-effectiveness ratios (ICERs, in 2014 US dollars per quality-adjusted life-year [QALY] gained).

RESULTS

The ICER of AVF (AVG) referral within 12 (3) months of forecasted HD initiation, compared to using only a CVC, is ∼$105k/QALY ($101k/QALY) at a population level (HD costs included). Pre-HD AVF or AVG referral dominates delaying referral until HD initiation. The ICER of pre-HD referral increases with patient age. Results are most sensitive to erythropoietin costs, ongoing HD costs, and patients' utilities for HD. When ongoing HD costs are excluded from the analysis, pre-HD AVF dominates both pre-HD AVG and CVC-only policies.

LIMITATIONS

Literature-based estimates for HD, AVF, and AVG utilities are limited.

CONCLUSIONS

The cost-effectiveness of vascular access referral is largely driven by the annual costs of HD, erythropoietin costs, and access-specific utilities. Further research is needed in the field of dialysis-related quality of life to inform decision making regarding vascular access referral.

Increasing the Efficiency of Monte Carlo Cohort Simulations with Variance Reduction Techniques

The authors discuss techniques for Monte Carlo (MC) cohort simulations that reduce the number of simulation replications required to achieve a given degree of precision for various output measures. Known as variance reduction techniques, they are often used in industrial engineering and operations research models, but they are seldom used in medical models. However, most MC cohort simulations are well suited to the implementation of these techniques. The authors discuss the cost of implementation versus the benefit of reduced replications.

State-space size considerations for disease-progression models

Markov models of disease progression are widely used to model transitions in patients' health state over time. Usually, patients' health status may be classified according to a set of ordered health states. Modelers lump together similar health states into a finite and usually small, number of health states that form the basis of a Markov chain disease-progression model. This increases the number of observations used to estimate each parameter in the transition probability matrix. However, lumping together observably distinct health states also obscures distinctions among them and may reduce the predictive power of the model. Moreover, as we demonstrate, precision in estimating the model parameters does not necessarily improve as the number of states in the model declines. This paper explores the tradeoff between lumping error introduced by grouping distinct health states and sampling error that arises when there are insufficient patient data to precisely estimate the transition probability matrix.

Treatment Evolution and New Standards of Care

Background. Traditional approaches to cost-effectiveness analysis have not considered the downstream possibility of a new standard of care coming out of the research and development pipeline. However, the treatment landscape for patients may change significantly over the course of their lifetimes. Objective. To present a Markov modeling framework that incorporates the possibility of treatment evolution into the incremental cost-effectiveness ratio (ICER) that compares treatments available at the present time. Design. Markov model evaluated by matrix algebra. Measurements. The author evaluates the difference between the new and traditional ICER calculations for patients with chronic diseases facing a lifetime of treatment. Results. The bias of the traditional ICER calculation may be substantial, with further testing revealing that it may be either positive or negative depending on the model parameters. The author also performs probabilistic sensitivity analyses with respect to the possible timing of a new treatment discovery and notes the increase in the magnitude of the bias when the new treatment is likely to appear sooner rather than later. Limitations. The modeling framework is intended as a proof of concept and therefore makes simplifying assumptions such as time stationarity of model parameters and consideration of a single new drug discovery. Conclusions. For diseases with a more active research and development pipeline, the possibility of a new treatment paradigm may be at least as important to consider in sensitivity analysis as other parameters that are often considered.

A clinically based discrete-event simulation of end-stage liver disease and the organ allocation process

BACKGROUND

The optimal allocation of scarce donor livers is a contentious health care issue requiring careful analysis. The objective of this article was to design a biologically based discrete-event simulation to test proposed changes in allocation policies.

METHODS

The authors used data from multiple sources to simulate end-stage liver disease and the complex allocation system. To validate the model, they compared simulation output with historical data.

RESULTS

Simulation outcomes were within 1% to 2% of actual results for measures such as new candidates, donated livers, and transplants by year. The model overestimated the yearly size of the waiting list by 5% in the last year of the simulation and the total number of pretransplant deaths by 10%.

CONCLUSION

The authors created a discrete-event simulation model that represents the biology of end-stage liver disease and the health care organization of transplantation in the United States.

A methodological framework for optimally reorganizing liver transplant regions

BACKGROUND

The United States is divided currently into 11 transplant regions, which vary in area and number of organ procurement organizations (OPOs). Region size affects organ travel time and organ viability at transplant.

PURPOSE

To develop a methodologic framework for determining optimal configurations of regions maximizing transplant allocation efficiency and geographic parity.

METHODS

An integer program was designed to maximize a weighted combination of 2 objectives: 1) intraregional transplants, 2) geographic parity-maximizing the lowest intraregional transplant rate across all OPOs. Two classes of functions relating liver travel time to liver viability were also examined as part of the sensitivity analyses.

RESULTS

Preliminary results indicate that reorganizing regions, while constraining their number to 11, resulted in up to 17 additional transplants/year depending on the travel-viability function; when not constrained, it resulted in up to 18/year of increase.

CONCLUSION

Our analysis indicates that liver transplantation may benefit through region reorganization. The analytic method developed here should be applicable to other organs and sets of organs.