Abstract
Background
The change in fractional flow reserve derived from CT (FFRCT) value across a coronary stenosis (ΔFFRCT) improves the physiological characterization of coronary artery disease (CAD). The role of ΔFFRCT in guiding risk-stratification and downstream testing in patients with stable CAD is unknown.
Purpose
To investigate the incremental value of ΔFFRCT at predicting early revascularization and improving efficacy of resource utilization.
Methods
Patients with CAD on CT coronary angiography (CTCA) were enrolled in an international multicenter registry. Patients with non-evaluable FFRCT analysis were excluded. The CTCA was assessed for: stenosis severity as per CAD-Reporting and Data System (CAD-RADS), lesion length and lesion-specific FFRCT measured 2 cm distal to stenosis. Risk factors and actual treatment (revascularization vs medical therapy) at 90-day follow-up were recorded. Multivariable logistic regression analysis for early revascularization was conducted. The incremental discrimination for revascularization prediction was compared among 3 models (model 1: risk factors + lesion length and location + CAD-RADS; model 2: model 1 + lesion-specific FFRCT; model 3: model 2 + ΔFFRCT). Simulating ICA referral for patients with CAD-RADS ≥3 and lesion-specific FFRCT ≤0.8, the potential impact of ΔFFRCT at reducing ICA referral and improving the ratio of subsequent revascularization was assessed.
Results
Of 4730 patients (66±10 years; 34% female), 2092 (42.7%) underwent ICA and 1168 (24.7%) underwent early revascularization. With increasing ΔFFRCT, a higher incidence of revascularization (Figure 1A) and an increase in the revascularization to ICA ratio was observed (Figure 1B). ΔFFRCT >0.13 was the optimal cut-off for predicting revascularization as determined by the Youden index. ΔFFRCT remained an independent predictor for early revascularization (odds ratio per 0.05 increase with 95% CI, 1.31 [1.26–1.35]; p<0.0001) after adjusting for risk factors, CAD-RADS, lesion length and location, and FFRCT. Among the 3 models, model 3, which included ΔFFRCT showed the highest AUC and improved discrimination power compared to model 2 (0.87 [0.86–0.88] vs 0.85 [0.84–0.86]; p<0.0001] (Figure 2), with the greatest incremental value for ΔFFRCT observed in patients with lesion-specific FFRCT between 0.71–0.80. In patients with CAD-RADS ≥3 and lesion-specific FFRCT ≤0.8, a diagnostic strategy incorporating ΔFFRCT >0.13 would potentially reduce ICA referral by 32.2% (1638 to 1110) and improve the revascularization to ICA ratio from 65.2% [1068/1638] to 73.1% [811/1110].
Conclusions
The characterization of CAD with ΔFFRCT improves the identification of patients requiring early revascularization as compared to a standard diagnostic strategy of CTCA with FFRCT, particularly for those with lesion-specific FFRCT of 0.71–0.80. ΔFFRCT has the potential to aid decision making for ICA referral and improve the efficiency of resource utilization.
FUNDunding Acknowledgement
Type of funding sources: Private company. Main funding source(s): HeartFlow, Inc., Redwood City, CA, USA ΔFFRCT and actual treatment ROC curve for early revascularization