Single centre experience using vessel Fractional Flow Reserve (vFFR) and diagnostic performance compared with invasive Fractional Flow Reserve (FFR)

Abstract Background Fractional flow reserve (FFR) is a diagnostic gold standard in the functional assessment of coronary artery stenoses (1). Alternative invasive and non-invasive methods have been developed in an attempt to avoid limitations associated with FFR including added procedural risk, patient discomfort, time and cost. Vessel fractional flow reserve (vFFR) is a novel software using two angiographic projections to generate a 3D vessel reconstruction which has shown high diagnostic accuracy in predicting a positive FFR (2-4). Methods All patients undergoing coronary angiography between 01/10/2022 and 08/03/2023, where FFR was performed, were assessed. Patients were included where images met requirements necessary to use vFFR software. vFFR measurements were taken retrospectively by a single investigator and not used to inform patient care. Data were expressed as mean +/- standard deviation and %. The correlation of vFFR to FFR was compared using Pearson’s correlation coefficient. Receiving operator characteristic (ROC) area under the curve (AUC) analysis was used to determine diagnostic performance. Statistical analysis was performed using SPSS v27. Results 28 of 37 patients and 32 of 44 vessels were included and analysed with vFFR software. Excluded vessels were unsuitable due to insufficient frame rate or projection angulation during angiography. Mean age was 63 +/- 3.8 years and 75% of patients were male. Of 32 vessels assessed, 20 were LAD, 5 RCA, 5 LCx and 2 LMS. 29 of 32 vFFR values agreed with positive or negative FFR. Positive vFFR values were seen in all 15 vessels with positive FFR. 3 vessels received a positive vFFR where FFR values were negative. Mean vFFR was 0.76 +/- 0.1 and mean FFR was 0.81 +/- 0.09. vFFR correlated significantly with FFR (R=0.777, p<0.001). vFFR demonstrated good diagnostic accuracy in discriminating "positive" (>0.8) and "negative" (≤0.8) FFR with an AUC of 0.758 (95% CI 0.61-0.91, p<0.01). Conclusion vFFR correlated significantly with FFR and all positive FFR values also received positive vFFR values. vFFR demonstrated good diagnostic accuracy in predicting a positive FFR result. Thus, these data support a strategy of using a negative vFFR to avoid further testing.Pearson correlation of vFFR to invFFRROC curve: vFFR predicting invFFR