AI-Powered Imaging Predicts Future Heart Events in Stable Coronary Artery Disease
A new analysis reveals that artificial intelligence-based imaging can predict future cardiovascular events in patients with suspected stable coronary artery disease, offering a potential leap forward in personalized heart health management. The findings, presented today at EACVI 2025, the annual congress of the European Association of Cardiovascular Imaging (EACVI), a branch of the European Society of Cardiology (ESC), suggest a broader role for this technology beyond its current diagnostic applications.
Stable Coronary Artery Disease and the Limitations of Current Diagnostics
Stable coronary artery disease (CAD), a common condition characterized by recurring chest pain or discomfort, often presents a diagnostic challenge.While coronary computed tomography angiography (CCTA) is routinely used as a first-line investigation to identify blockages in the arteries, it struggles to accurately assess the degree of reduced blood flow, leaving clinicians uncertain about the need for intervention.
To address this gap, researchers have been investigating the use of fractional flow reserve computed tomography (FFR-CT), a non-invasive imaging technique that uses artificial intelligence to simulate blood flow through the coronary arteries. The recent study, leveraging data from the ISH&CHIPS population, which was large enough to determine whether FFR-CT adds incremental value to traditional cardiovascular risk factors in predicting cardiovascular outcomes and death.”
Large-Scale Study Reveals Prognostic Power of FFR-CT
The analysis, conducted as part of the FISH&CHIPS observational study, included data from 7,836 patients who underwent FFR-CT analysis (using the HeartFlow system) at 27 sites across England. The patients,with a median age of 63 and 37.4% female, were followed for an average of 3.1 years. During this period, researchers tracked the incidence of myocardial infarction (MI), cardiovascular mortality, all-cause mortality, and the need for revascularization – procedures like coronary artery bypass grafting or stenting.
FFR-CT measurements were categorized into four groups: normal (FFR-CT >0.8),borderline (0.71-0.8),reduced (0.51-0.7),and severely reduced (≤0.5). the results demonstrated a clear correlation between lower FFR-CT values and increased risk of adverse cardiovascular events.
specifically, the data revealed:
- MI occurred in 1.0% of patients with normal FFR-CT, rising to 2.0% with borderline FFR-CT, 3.9% with reduced FFR-CT, and 5.2% with severely reduced FFR-CT.
- Patients with the lowest FFR-CT values faced a four-fold increased risk of heart attack and a three-fold increased risk of dying from a heart attack.
- 191 MIs (2.4%), 1,573 revascularizations (20.1%), 74 cardiovascular deaths (0.9%), and 261 all-cause deaths (3.3%) were recorded during the follow-up period.
Autonomous Risk Stratification and Personalized Treatment
Importantly, the increased risk associated with lower FFR-CT values remained significant even after accounting for traditional cardiovascular risk factors such as age, sex, hypertension, diabetes, and dyslipidemia. This suggests that FFR-CT provides independent and valuable prognostic information.
“Adding to its diagnostic abilities, this study is the first to provide conclusive evidence of FFR-CT’s prognostic power, independent of other risk factors,” stated Professor Timothy Fairbairn, Senior Author from Liverpool Heart and Chest Hospital. “We observed that even so-called ‘borderline’ FFR-CT was associated with worse outcomes compared with normal values, but the individuals with the lowest values have the highest risk. FFR-CT could be used to inform personalized risk assessment, allowing us to provide more intensive bespoke treatment to those at high risk.”
Researchers are also investigating the cost-effectiveness of incorporating FFR-CT into the diagnostic pathway for stable CAD, with results expected to be presented at EACVI 2025. This further underscores the potential for FFR-CT to not only improve patient outcomes but also optimize healthcare resource allocation.
Source: European Society of Cardiology
