Optimal Application of Fractional Flow Reserve to Assess Serial Coronary Artery Disease: A 3D‐Printed Experimental Study With Clinical Validation

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 (ΔFFR (app)) and compared with phantoms with the stenosis in isolation (ΔFFR (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. ΔFFR (app) underestimated ΔFFR (true) in 88% of phantoms, with underestimation proportional to total FFR. Discrepancy as a proportion of ΔFFR (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.