CT-Derived Fractional Flow Reserve for Risk Stratification and Prediction of Major Adverse Cardiovascular Events

Medicine/Surgery

Coronary artery disease (CAD) remains the leading cause of mortality and morbidity globally, emphasizing the critical need for accurate risk stratification and prediction of major adverse cardiovascular events (MACE). Fractional flow reserve (FFR) is the gold standard for assessing the functional significance of coronary stenosis, but its invasive nature limits widespread application. Computed tomography-derived FFR (CT-FFR) has emerged as a non-invasive alternative, integrating anatomical information from coronary computed tomography angiography (CCTA) with computational fluid dynamics to estimate FFR. This review aims to synthesize the current evidence on the role of CT-FFR in risk stratification of CAD patients and its predictive value for MACE, while discussing technical considerations, clinical validation, limitations, and future directions. Methods: A comprehensive literature search was performed in PubMed, Embase, and Cochrane Library databases up to December 2025, using keywords such as “CT-derived fractional flow reserve”, “CT-FFR”, “risk stratification”, “major adverse cardiovascular events”, and “coronary artery disease”. Studies focusing on the diagnostic performance, risk stratification ability, and MACE prediction of CT-FFR were included, with priority given to randomized controlled trials (RCTs), meta-analyses, and large-scale prospective cohort studies. Results: CT-FFR exhibits excellent correlation with invasive FFR (r > 0.8 in most studies) and superior diagnostic accuracy for functionally significant stenosis compared to CCTA alone. In terms of risk stratification, CT-FFR-negative patients have a very low short- and long-term MACE rate (≤1% per year), supporting their safe management with medical therapy alone. Conversely, CT-FFR-positive patients have a significantly higher MACE risk, which can be further stratified by the degree of FFR reduction, presence of multivessel disease, and concurrent clinical risk factors. Meta-analyses have confirmed that CT-FFR provides incremental prognostic value beyond traditional risk factors, CCTA findings, and even invasive FFR in some subsets. Technical advancements, such as machine learning-enhanced CT-FFR algorithms and low-radiation dose protocols, have further expanded its clinical applicability. Limitations: CT-FFR is limited by the need for specialized software and expertise, potential inaccuracies in calcified or tortuous vessels, and lack of data in certain high-risk populations (e.g., acute coronary syndrome, severe heart failure)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.