Optimal Application of Fractional Flow Reserve to Assess Serial Coronary Artery Disease: A 3D-Printed Experimental...

Background Assessing the physiological significance of stenoses with coexistent serial disease is prone to error. We aimed to use 3-dimensional-printing to characterize serial stenosis interplay and to derive and validate a mathematical solution to predict true stenosis significance in serial disease. Methods and Results Fifty-two 3-dimensional-printed serial disease phantoms were physiologically assessed by pressure-wire pullback (Δ FFR _app) and compared with phantoms with the stenosis in isolation (Δ FFR _true). Mathematical models to minimize error in predicting FFR _true, the FFR in the vessel where the stenosis is present in isolation, were subsequently developed using 32 phantoms and validated in another 20 and also a clinical cohort of 30 patients with serial disease. Δ FFR _app underestimated Δ FFR _true in 88% of phantoms, with underestimation proportional to total FFR . Discrepancy as a proportion of Δ FFR _true was 17.1% (absolute difference 0.036±0.048), which improved to 2.9% (0.006±0.023) using our model. In the clinical cohort, discrepancy was 38.5% (0.05±0.04) with 13.3% of stenoses misclassified (using FFR <0.8 threshold). Using mathematical correction, this improved to 15.4% (0.02±0.03), with the proportion of misclassified stenoses falling to 6.7%. Conclusions Individual stenoses are considerably underestimated in serial disease, proportional to total FFR . We have shown within in vitro and clinical cohorts that this error is significantly improved using a mathematical correction model, incorporating routinely available pressure-wire pullback data.