Automated Algorithm Using Pre-Intervention Fractional Flow Reserve Pullback Curve to Predict Post-Intervention Physiological Results

Abstract Objectives This study sought to develop an automated algorithm using pre-percutaneous coronary intervention (PCI) fractional flow reserve (FFR) pullback recordings to predict post-PCI physiological results in the pre-PCI phase. Background Both FFR and percent FFR increase measured after PCI showed incremental prognostic implications. However, there is no current method to predict post-PCI physiological results using physiological assessment in the pre-PCI phase. Methods An automated algorithm that analyzes instantaneous FFR gradient per unit time (dFFR(t)/dt) was developed from the derivation cohort (n = 30). Using dFFR(t)/dt, the pattern of atherosclerotic disease in each patient was classified into 3 groups (major, mixed, and minor FFR gradient groups) in both the internal validation cohort with constant pullback method (n = 234) and the external validation cohort with nonstandardized pullback methods (n = 252). All patients in the validation cohorts underwent PCI on the basis of pre-PCI FFR ≤0.80. Suboptimal post-PCI physiological results were defined as both post-PCI FFR Results In validation cohorts, dFFR(t)/dt showed significant correlations with percent FFR increase (R = 0.801; p  Conclusions The automated algorithm analyzing pre-PCI pullback curve was able to predict post-PCI physiological results. The incidence of suboptimal post-PCI physiological results was significantly different according to algorithm-based classifications in the pre-PCI physiological assessment. (Automated Algorithm Detecting Physiologic Major Stenosis and Its Relationship with Post-PCI Clinical Outcomes [Algorithm-PCI]; NCT04304677 )