Correlation Between Quantitative Angiography-Derived Translesional Pressure and Fractional Flow Reserve

Non-invasive Angiographic-based Fractional Flow Reserve: Technical Development, Clinical Implications, and Future Perspectives

New non- and less-invasive techniques have been developed to overcome the procedural and operator related burden of the fractional flow reserve (FFR) for the assessment of potentially significant stenosis in the coronary arteries. Virtual FFR-techniques can obviate the need for the additional flow or pressure wires as used for FFR measurements. This review provides an overview of the developments and validation of the virtual FFR-algorithms, states the challenges, discusses the upcoming clinical trials, and postulates the future role of virtual FFR in the clinical practice.

Fractional Flow Reserve: Patient Selection and Perspectives

The aim of this review was to discuss the current practice and patient selection for invasive FFR, new techniques to estimate invasive FFR and future of coronary physiology tests. We elaborate on the indication and application of FFR and on the contraindications and concerns in certain patient populations.

Patients with Moderate Non-Culprit Coronary Lesions of Recent Acute Coronary Syndrome

Fractional flow reserve (FFR) measurement was compared to dobutamine stress echocardiography (DSE) instable angina (SA) with stable coronary lesion (s) (SCL (s) ) in a few trials; however, similar comparisons in patients with acute coronary syndrome (ACS) with non-culprit lesion (s) (NCL (s) ) are lacking. Our objectives were to prospectively evaluate the diagnostic performance of FFR with two different cutoff values (< 0.80 and < 0.75) relative to DSE in moderate (30%-70% diameter stenosis) NCLs (ACS group) and to compare these observations with those measured in SCLs (SA group). One hundred seventy-five consecutive patients with SA (n = 86) and ACS (n = 89) with 225 coronary lesions (109 SCLs and 116 NCLs) were enrolled. In contrast to the ACS cohort in SA patients, normal DSE was associated with higher FFR values compared to those with abnormal DSE (P = 0.051 versus P = 0.006). In addition, in the SA group, a significant correlation was observed between DSE (regional wall motion score index at peak stress) and FFR (r = -0.290; P = 0.002), whereas a similar association was absent (r = -0.029; P = 0.760) among ACS patients. In the SA group, decreasing the FFR cutoff value (< 0.80 versus < 0.75) improved the concordance of FFR with DSE (70.6% versus 81.7%) without altering its discriminatory power (area under the curve; 0.68 versus 0.63; P = 0.369), whereas in the ACS group, concordance remained similar (69.0% versus 71.6%) and discriminatory power decreased (0.62 versus 0.51; P = 0.049), respectively. In conclusion, lesion-specific FFR assessment may have different relevance in patients with moderate NCLs than in patients with SCLs.

Development of Novel Artificial Intelligence to Detect the Presence of Clinically Meaningful Coronary Atherosclerotic Stenosis in Major Branch from Coronary Angiography Video

Aim: The clinically meaningful coronary stenosis is diagnosed by trained interventional cardiologists. Whether artificial intelligence (AI) could detect coronary stenosis from CAG video is unclear.

Methods: The 199 consecutive patients who underwent coronary arteriography (CAG) with chest pain between December 2018 and May 2019 was enrolled. Each patient underwent CAG with multiple view resulting in total numbers of 1,838 videos. A multi-layer 3-dimensional convolution neural network (CNN) was trained as an AI to detect clinically meaningful coronary artery stenosis diagnosed by the expert interventional cardiologist, using data from 146 patients (resulted in 1,359 videos) randomly selected from the entire dataset (training dataset). This training dataset was further split into 109 patients (989 videos) for derivation and 37 patients (370 videos) for validation. The AI developed in derivation cohort was tuned in validation cohort to make final model.

Results: The final model was selected as the model with best performance in validation dataset. Then, the predictive accuracy of final model was tested with the remaining 53 patients (479 videos) in test dataset. Our AI model showed a c-statistic of 0.61 in validation dataset and 0.61 in test dataset, respectively.

Conclusion: An artificial intelligence applied to CAG videos could detect clinically meaningful coronary atherosclerotic stenosis diagnosed by expert cardiologists with modest predictive value. Further studies with improved AI at larger sample size is necessary.

Intracoronary Optical Coherence Tomography-Derived Virtual Fractional Flow Reserve for the Assessment of Coronary Artery Disease

Fractional flow reserve (FFR) is widely used for the assessment of myocardial ischemia. Optical coherence tomography (OCT) provides accurate visualization of coronary artery morphology. The aim of this study was to investigate the relation between FFR and OCT-derived FFR. We retrospectively analyzed 31 lesions (25 left anterior descending arteries, 2 left circumflex arteries, and 4 right coronary arteries) in 31 patients with moderate-to-severe coronary stenosis, who underwent OCT and FFR measurements simultaneously. OCT-derived FFR was calculated by the original algorithm, which was calculated using the following equation based on fluid dynamics: ΔP = FV + SV², where V is the flow velocity, F is the coefficient of pressure loss because of viscous friction (Poiseuille resistance), and S is the coefficient of local pressure loss because of abrupt enhancement (flow separation). Mean values of % diameter stenosis by quantitative coronary angiography and FFR were 55.2 ± 14.0% and 0.70 ± 0.14, respectively. OCT-derived FFR showed a stronger linear correlation with FFR measurements (r = 0.89, p <0.001; root mean square error = 0.062 FFR units) than quantitative coronary angiography % diameter stenosis (r = -0.65, p <0.001), OCT measurements of minimum lumen area (r = 0.68, p <0.001), and % area stenosis (r = -0.70, p <0.001). OCT-derived FFR has the potential to become an alternative method for the assessment of functional myocardial ischemia, and may elucidate the relation between coronary morphology and FFR.