Key result
Machine learning-based FFRCT was feasible in 68% of patients with acute chest pain and showed 75% agreement with clinical outcomes in those with vulnerable plaque and 91% in those without.
Why the study?
The feasibility and potential clinical role of machine learning-based CT-derived fractional flow reserve in patients presenting to the emergency department with acute chest pain had not been evaluated.
Does machine learning-based FFRCT assessment agree with clinical outcomes in patients presenting to the emergency department with acute chest pain?
Observational (n=56)
Does machine learning-based FFRCT assessment agree with clinical outcomes in patients presenting to the emergency department with acute chest pain?
Machine learning-based FFRCT is feasible in acute chest pain patients with sufficient image quality and shows good agreement with clinical outcomes, particularly in those without acute plaque rupture.
Answers are grounded in this paper and cite their sources.
FFRCT feasible in ED acute chest pain; hypothesis-generating, requires prospective validation before clinical adoption.
Background: Computed tomography (CT)-derived fractional flow reserve (FFRCT) enables the non-invasive functional assessment of coronary artery stenosis. We evaluated the feasibility and potential clinical role of FFRCT 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. FFRCT measurements were performed using a machine learning-based software. We assessed the agreement between the results from FFRCT 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: FFRCT 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, FFRCT and patient outcome showed an agreement in 10/11 patients (91%). Conclusions: Our preliminary data show that FFRCT is feasible in patients with acute chest pain who undergo CPCT provided that image quality is sufficient. FFRCT 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.
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Eberhard et al. (2020) conducted an observational in Acute chest pain (n=56). Machine learning-based CT fractional flow reserve (FFRCT) vs. Patient outcome (invasive angiography, non-invasive tests, clinical diagnosis) was evaluated on Agreement between FFRCT results and patient outcome. Machine learning-based FFRCT was feasible in 68% of patients with acute chest pain and showed 75% agreement with clinical outcomes in those with vulnerable plaque and 91% in those without.
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