3 Optimising physiology guided management of serial coronary artery disease

Abstract

BackgroundPhysiology-guided coronary revascularisation is associated with better outcomes however it is unclear if pressure-derived indices, such as FFR, can reliably assess individual lesions in serial disease.Methods3D printed serial disease phantoms were assessed in vitro. FFR of a lesion was predicted from the size of step up on pressure-wire pullback in the presence of serial lesions (FFR app ) and compared to phantoms with no accompanying lesion (FFR true ). Mathematical models to minimise error in predicting FFR true were developed in 32 phantoms and validated in 15 patients and another 20 phantoms. In 43 serial disease patients we compared how different indices (FFR, iFR, Pd/Pa) predict true values, after isolation by PCI or disease-free sidebranch. In a subset of 21 we measured flow to calculate Hyperaemic Stenosis Resistance (hSR).ResultsFFR app underestimated FFR true in 85% of phantoms, with discrepancy proportional to total FFR. 4.5% of lesions were misclassified (FFR <0.8 threshold); with mathematical models this fell to 1.5% (figure 1). Clinically, FFR, iFR and Pd/Pa significantly underestimated true values; hSR is least affected (p<0.01).Abstract 3 Figure 1Scatterplots of difference between FFRapp of a stenosis in presence of accompanying lesion and FFRtrue, when stenosis present in isolation. Within the validation cohort, discrepancy was 5% (0.036±0.048), which improved to 0.6% (0.005±0.037) and 0.8% (0.006±0.023) using empirical and theoretical solutions respectively (P<0.01). In the clinical cohort, discrepancy was 6.6% (0.041±0.03), which improved to 2.2% (0.019±0.026) and 2.0% (0.016±0.023) (P<0.01)ConclusionPressure-derived indices underestimate stenosis significance in serial disease. Pressure pullback data can be mathematically analysed to minimise this. Pressure-flow based resistance indices are less prone to this error.