Simulation as Alternative to Diagnostic Catheterization
When coronary artery disease is suspected, the HeartFlow FFRCT Analysis (CT derived fractional flow reserve) combines deep learning, computational fluid dynamics (CFD) and coronary CT angiography to help physicians make accurate diagnoses. Obtaining FFR values was previously only possible with catheter examinations but can now be calculated with FFRCT. Together with HeartFlow, the company that developed FFRCT, Siemens Healthineers is working in cooperation to further foster this technology.
A feeling of tightness or pain in the chest and shortness of breath while exercising – when patients report symptoms such as these, their doctor will usually check whether the cause is a narrowing of the coronary arteries due to plaque. Around seven million people die every year due to untreated coronary artery disease (CAD) so symptoms like these are taken very seriously1. Coronary CT angiography is one highly sensitive way to diagnose or rule out CAD by visualizing the coronary arteries in detail. This means that if the doctor doesn’t see any arterial stenosis, there is almost one hundred percent certainty that there is none.
Measuring the extent of stenosis with a catheter
Should plaque, however, be detected in the arteries, the challenge is to assess the extent to which blood flow to the heart muscle is affected. Until now, an invasive FFR measurement with a special pressure catheter has been used to decide whether invasive treatment is necessary. A probe inserted into the coronary artery through femoral or radial access measures the pressure ahead of and after the stenosis. If the pressure ratio falls below a certain value, this indicates that the stenosis should be treated invasively, for instance with a stent.
Simulation as an alternative to catheter examination
The HeartFlow FFRCT Analysis is a non-invasive alternative to this type of catheter examination. The American startup uses the previously obtained CT images to first extract three-dimensional representations of a patient’s arteries. Excellent CT image quality is a prerequisite here. Initial clinical experiences have shown that scans taken with a Siemens Dual Source CT scanner have a 97 to 99 percent acceptance rate for HeartFlow FFRCT Analysis2.
A personalized arterial model is then created to simulate the blood flow in the patient using computational fluid dynamics (CFD). This is used to evaluate the extent of impairment due to arterial narrowing. Such computational models have been used for several years, for example in the aviation and automotive industries, where they serve as a cost-efficient alternative to tests in wind or water channels.
Dramatically reducing unnecessary catheter examinations
The simulation provided by FFRCT takes account of the special flow properties of blood and can thereby calculate blood pressure. Three major clinical trials from 2011 to 2013 showed that FFRCT demonstrated significantly higher diagnostic accuracy than other non-invasive testing methods3. A fourth clinical trial from 2016 also showed that the number of unnecessary catheter examinations could be reduced by 83 percent when FFRCT was integrated into the decision-making process.4 The HeartFlow Analysis has already been approved in the USA and Germany, as well as in many other countries.
Last year, Siemens Healthineers and HeartFlow launched a collaboration to give physicians more integrated access to this new technology. This cooperation is limited to the USA for the time being, but other countries are being evaluated accordingly. It includes a more integrated way of sending data to HeartFlow and receiving the completed analyses. This is achieved using cloud-to-cloud data transfer through Siemens Healthineers’ Digital Ecosystem – a secure, established IT environment – and embedded in teamplay. With this next step toward digitalized healthcare, hospitals and medical practices could save considerable time and costs – two critical factors in healthcare delivery.
2 Nørgaard, et al. JACC Cardiovasc Imaging. 2017 May; 10(5):541-550.
3 Nørgaard, et al. J Am Coll Cardiol. 2014.; Min, et al. JAMA. 2012.; Koo, et al. J Am Coll Cardiol. 2011.
4 Douglas, et al. Eur Heart J. 2015.
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