Abstract
On account of the high prevalence of cardiovascular disease in patients with kidney failure, clinical practice guidelines recommend regular screening for asymptomatic coronary artery disease (CAD) in patients on the kidney transplant waitlist. To date, the cost-effectiveness of such screening has not been evaluated. A Canadian-Australasian randomized controlled trial of screening kidney transplant candidates for CAD (CARSK) is currently is being conducted to answer this question. We conducted a cost-utility analysis to determine, before completion of the trial, the cost-effectiveness of no further screening versus regular screening for asymptomatic CAD and to evaluate potential influential variables that may affect results of the economic evaluation. A modeled cost-utility analysis. A theoretical cohort of adult Australian and New Zealand kidney transplant candidates on the waitlist. No further screening for asymptomaticCAD versus regular protocolized screening (annual or second yearly)for CADafter kidney transplant waitlisting. Incremental cost-effectiveness ratio, reported as cost per quality-adjusted life-year (QALY). Markov microsimulation model, health system perspective and over a lifetime horizon. In the base case, the incremental cost-effectiveness ratio of no further screening was $11,122 per QALY gained when compared with regular screening. No further screening increased survival by 0.49 life-year or 0.35 QALY. One-way sensitivity analyses identified the costs of transplantation in the first year and CAD prevalence as the mostinfluential variables. Probabilistic sensitivity analyses showed that 94% of the simulations were cost-effective below a willingness-to-paythreshold of $50,000 per QALY gained. Rates of cardiovascular events in waitlisted candidates and transplant recipients are limited in the contemporary era. The results may not be generalizable to populations outside Australia and New Zealand. No further screening for CAD after waitlisting is likely to be cost-effective and may improve survival. Precision around CAD prevalence estimates and health careresource use will reduce existing uncertainty.
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