Optimizing coronary artery disease management: the role of CT-derived fractional flow reserve in predicting revascularization and guiding clinical decisions.

OBJECTIVE

Coronary artery disease (CAD) is the most common heart disease, and invasive coronary angiography (ICA) has been recognized as the gold standard for diagnosing CAD. However, this method is influenced by inter-observer and intra-observer variability. The impact of machine learning (ML) algorithm-based fractional flow reserve CT (CT-FFR) software on the prediction of revascularization, the subsequent use of ICA, and the clinical decision-making for patients with suspected CAD remains to be studied. To evaluate the role of CT-FFR in the prediction of coronary revascularization and the optimization of treatment decisions.

METHODS

A retrospective study of 279 patients who underwent non-emergency CCTA and ICA within 90 days, with ≤ 0.80 indicating significant stenosis. Stenosis severity was categorized based on CCTA and ICA results. Consistency between CT-FFR, CCTA, and ICA was evaluated. Treatment decisions based on CT-FFR were compared to those based on CCTA and ICA, focusing on modifications between OMT and REV. The impact of CT-FFR on ICA utilization and its relationship with subsequent MACE incidence were also assessed.

RESULTS

CT-FFR demonstrated strong consistency with ICA (84.2%) and CCTA (78.5%). Median CT-FFR values declined with increasing stenosis severity (CCTA severe stenosis: 0.71; ICA severe stenosis: 0.70 ), confirming its diagnostic reliability. Among 188 patients with positive CT-FFR results, 75.5% underwent revascularization, reflecting high predictive accuracy. Treatment strategies were modified in 75 cases compared to ICA results and 60 cases compared to CCTA, optimizing decisions between OMT and REV. CT-FFR may reduce ICA utilization by 56.0% and the number of arteries requiring revascularization by 25.0%, improving procedural efficiency and reducing unnecessary interventions. Furthermore, CT-FFR demonstrated prognostic value as patients with positive results had a higher incidence of MACE. It also enhanced decision-making for non-obstructive arteries, significantly improving patient management and clinical outcomes.

CONCLUSIONS

(1) CT-FFR demonstrated high consistency with the diagnostic results of both CCTA and ICA, and CT-FFR values showed a decreasing trend as the degree of stenosis increased. (2) CT-FFR based on deep learning algorithms exhibited high diagnostic performance in predicting revascularization. In addition, patients with positive CT-FFR results had approximately twice the risk of major adverse cardiovascular events (MACE) compared to those with negative results, suggesting its potential superiority in guiding ICA utilization and its promising capability for predicting clinical outcomes.