Modelling the cost-effectiveness of cardiac interventions: the case of sirolimus-eluting stents

Br J Cardiol (Acute Interv Cardiol) 2005:12:AIC 83–AIC 91 Leave a comment
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This article aims to provide a primer on decision modelling to assess the cost-effectiveness of interventions in cardiology. The paper uses a cost-effectiveness model developed to compare alternative coronary stents. This decision analytic model assesses costs to the UK health service and health benefits in terms of quality-adjusted life-years (QALYs). Data were taken from a range of sources, including 12-month follow-up data from three important double-blind randomised controlled trials: RAVEL, SIRIUS and E-SIRIUS. Methods are employed to show the uncertainty in cost-effectiveness.
Sirolimus-eluting stents were compared to ‘bare metal’ stents in constructing this decision model. The patients included were those individuals with stable coronary disease randomised to the three trials.
The main outcome measures were: mean QALYs, mean health service costs, incremental cost per additional QALY, and the probability that sirolimus- eluting stents are more cost-effective than bare metal stents.
Mean QALY gains per patient from the sirolimus-eluting stent range from 0.011 to 0.017 over 12 months. Although the list price of the sirolimus- eluting stent is £617 more than the bare metal stent, its additional total mean cost per patient, including ‘cost offsets’ from a lower rate of subsequent events, ranges from £53 to £166. The incremental cost of the sirolimus-eluting stent per additional QALY ranges from £3,181 to £15,198. The probability that the sirolimus-eluting stent is less costly than the bare metal stent ranges from 0.13 to 0.34. If the health service is willing to pay up to £40,000 per additional QALY, the probability of the newer stent being the more cost-effective ranges between 0.8 and 1.0. These results are sensitive to assumptions about the price differential between the two forms of stent.
Cost-effectiveness analyses based on models are used increasingly as a basis for decision making. It is essential that these models are developed with clinical input regarding appropriate assumptions and interpretation of evidence.