Aims: New generation dual-source coronary CT (NGCCT) scanners with more than 64 slices were evaluated for patients with (known) or suspected of coronary artery disease (CAD) who are difficult to image: obese, coronary calcium score > 400, arrhythmias, previous revascularization, heart rate > 65 beats per minute, and intolerance of betablocker. A cost-effectiveness analysis of NGCCT compared with invasive coronary angiography (ICA) was performed for these difficult-to-image patients for England and Wales. Methods and results: Five models (diagnostic decision model, four Markov models for CAD progression, stroke, radiation and general population) were integrated to estimate the cost-effectiveness of NGCCT for both suspected and known CAD populations. The lifetime costs and effects from the National Health Service perspective were estimated for three strategies: (1) patients diagnosed using ICA, (2) using NGCCT, and (3) patients diagnosed using a combination of NGCCT and, if positive, followed by ICA. In the suspected population, the strategy where patients only undergo a NGCCT is a cost-effective option at accepted cost-effectiveness thresholds. The strategy of using NGCCT in combination with ICA is the most favourable strategy for patients with known CAD. The most influential factors behind these results are the percentage of patients being misclassified (a function of both diagnostic accuracy and the prior likelihood), the complication rates of the procedures, and the cost price of a NGCCT scan. Conclusion: The use of NGCCT might be considered cost-effective in both populations since it is cost-saving compared to ICA and generates similar effects.

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doi.org/10.1007/s10198-016-0824-z, hdl.handle.net/1765/93423
The European Journal of Health Economics
Institute for Medical Technology Assessment (iMTA)

Burgers, L., Redekop, K., Al, M., Lhachimi, S.K., Armstrong, N., Walker, S., … Severens, H. (2017). Cost-effectiveness analysis of new generation coronary CT scanners for difficult-to-image patients. The European Journal of Health Economics, 18(6), 731–742. doi:10.1007/s10198-016-0824-z