Prognostic implication of CT-FFR based functional SYNTAX score in patients with de novo three-vessel disease

This study was aimed at investigating whether a machine learning (ML)-based coronary computed tomographic angiography (CCTA) derived fractional flow reserve (CT-FFR) SYNTAX score (SS), ‘Functional SYNTAX score’ (FSS_CTA), would predict clinical outcome in patients with three-vessel coronary artery disease (CAD).

The SS based on CCTA (SS_CTA) and ICA (SS_ICA) were retrospectively collected in 227 consecutive patients with three-vessel CAD. FSS_CTA was calculated by combining the anatomical data with functional data derived from a ML-based CT-FFR assessment. The ability of each score system to predict major adverse cardiac events (MACE) was compared. The difference between revascularization strategies directed by the anatomical SS and FSS_CTA was also assessed. Two hundred and twenty-seven patients were divided into two groups according to the SS_CTA cut-off value of 22. After determining FSS_CTA for each patient, 22.9% of patients (52/227) were reclassified to a low-risk group (FSS_CTA ≤ 22). In the low- vs. intermediate-to-high (>22) FSS_CTA group, MACE occurred in 3.2% (4/125) vs. 34.3% (35/102), respectively (P < 0.001). The independent predictors of MACE were FSS_CTA (OR = 1.21, P = 0.001) and diabetes (OR = 2.35, P = 0.048). FSS_CTA demonstrated a better predictive accuracy for MACE compared with SS_CTA (AUC: 0.81 vs. 0.75, P = 0.01) and SS_ICA (0.81 vs. 0.75, P < 0.001). After FSS_CTA was revealed, 52 patients initially referred for CABG based on SS_CTA would have been changed to PCI.

Recalculating SS by incorporating lesion-specific ischaemia as determined by ML-based CT-FFR is a better predictor of MACE in patients with three-vessel CAD. Additionally, the use of FSS_CTA may alter selected revascularization strategies in these patients.