Virtual Coronary Intervention - A Treatment Planning Tool Based Upon the Angiogram
Figure 1: Example of VCI A) A 66-year-old man presented with chronic stable angina. The Left anterior descending artery had a severe mid vessel stenosis (arrow). The measured (m)FFR between the proximal and distal points marked with the dashed line was 0.77. B) The angiograms were used to model the virtual (v)FFR using the VIRTUheart™ system, which was calculated to be 0.75 over the same segment. C) After implantation of a 2.75x18mm stent at the stenosis, the mFFR was 0.88 over the same segment. D) Virtual coronary intervention (VCI) using the VIRTUheart™system was then used to implant a ‘virtual’ 2.75x18mm stent, and the recalculated vFFR was 0.88.
During this procedure, dye is injected into the coronary arteries and images are taken using x rays. These images are analysed by the cardiologist to determine whether there are any significant narrowings that warrant treatment either by inserting a stent (percutaneous coronary intervention (PCI)) or with coronary artery bypass surgery. Deciding whether a particular narrowing is responsible for the patient’s symptoms is not easy, and therefore decision making regarding treatment recommendations is often subjective. A more objective approach is to measure the pressure drop across a lesion using an invasive pressure wire. This is known as fractional flow reserve (FFR). Using FFR to guide treatment decisions is associated with improved outcomes yet it is only available at specialist centres and requires the administration of additional medication which can be uncomfortable for the patient. Moreover, there is currently no method available to predict the physiological response to PCI, i.e how much the pressure drop will improve if you insert a stent.
Within the mathematical modelling in medicine group in at the University of Sheffield, we have developed a virtual coronary intervention tool that can do just that. Using this tool, a 3-D reconstruction of the coronary artery is created from the angiographic images and using computational fluid dynamics, a virtual FFR can be computed without the need of an invasive pressure wire. The operator can then insert virtual stents and assess the physiological impact (predicted post-treatment FFR) of each strategy.
In the initial validation study, published in JACC: Cardiovascular imaging, our tool was able to predict the response to treatment with a high degree of accuracy. This exciting development allows the operator to trial numerous treatment strategies and compare the predicted outcomes of each, before proceeding to intervention in the patient. This has the potential to significantly improve treatment planning in patients with coronary artery disease.
This research has recently been published in ‘Journal of American College of Cardiology – Cardiovascular Imaging’
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