Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience

Matthias Eberhard; Tin Nadarevic; Andrej Cousin; Jochen von Spiczak; Ricarda Hinzpeter; Andre Euler; Fabian Morsbach; Robert Manka; Dagmar I Keller; Hatem Alkadhi

doi:10.21037/cdt-20-381

Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience

Cardiovasc Diagn Ther. 2020 Aug;10(4):820-830. doi: 10.21037/cdt-20-381.

Authors

Affiliations

¹ Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
² Department of Radiology, Clinical Hospital Center Rijeka, Rijeka, Croatia.
³ Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.
⁴ Department of Cardiology, University Heart Center Zurich, University of Zurich, Zurich, Switzerland.
⁵ Emergency Department, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

Abstract

Background: Computed tomography (CT)-derived fractional flow reserve (FFR_CT) enables the non-invasive functional assessment of coronary artery stenosis. We evaluated the feasibility and potential clinical role of FFR_CT in patients presenting to the emergency department with acute chest pain who underwent chest-pain CT (CPCT).

Methods: For this retrospective IRB-approved study, we included 56 patients (median age: 62 years, 14 females) with acute chest pain who underwent CPCT and who had at least a mild (≥25% diameter) coronary artery stenosis. CPCT was evaluated for the presence of acute plaque rupture and vulnerable plaque features. FFR_CT measurements were performed using a machine learning-based software. We assessed the agreement between the results from FFR_CT and patient outcome (including results from invasive catheter angiography and from any non-invasive cardiac imaging test, final clinical diagnosis and revascularization) for a follow-up of 3 months.

Results: FFR_CT was technically feasible in 38/56 patients (68%). Eleven of the 38 patients (29%) showed acute plaque rupture in CPCT; all of them underwent immediate coronary revascularization. Of the remaining 27 patients (71%), 16 patients showed vulnerable plaque features (59%), of whom 11 (69%) were diagnosed with acute coronary syndrome (ACS) and 10 (63%) underwent coronary revascularization. In patients with vulnerable plaque features in CPCT, FFRCT had an agreement with outcome in 12/16 patients (75%). In patients without vulnerable plaque features (n=11), one patient showed myocardial ischemia (9%). In these patients, FFR_CT and patient outcome showed an agreement in 10/11 patients (91%).

Conclusions: Our preliminary data show that FFR_CT is feasible in patients with acute chest pain who undergo CPCT provided that image quality is sufficient. FFR_CT has the potential to improve patient triage by reducing further downstream testing but appears of limited value in patients with CT signs of acute plaque rupture.

Keywords: Acute coronary syndrome (ACS); computed tomography angiography; fractional flow reserve; machine learning; myocardial.