Diagnostic accuracy of optical flow ratio: an individual patient-data meta-analysis

An Overview of Computational Coronary Physiology Technologies Based on Medical Imaging and Artificial Intelligence

This article reviews four new technologies for assessment of coronary hemodynamics based on medical imaging and artificial intelligence, including quantitative flow ratio (QFR), optical flow ratio (OFR), computational fractional flow reserve (CT-FFR) and artificial intelligence (AI)-based instantaneous wave-free ratio (iFR). These technologies use medical imaging such as coronary angiography, computed tomography angiography (CTA), and optical coherence tomography (OCT), to reconstruct three-dimensional vascular models through artificial intelligence algorithms, simulate and calculate hemodynamic parameters in the coronary arteries, and achieve non-invasive and rapid assessment of the functional significance of coronary stenosis. This article details the working principles, advantages such as non-invasiveness, efficiency, accuracy, limitations such as image dependency, and assumption restrictions, of each technology. It also compares and analyzes the image dependency, calculation accuracy, calculation speed, and operation simplicity, of the four technologies. The results show that these technologies are highly consistent with the traditional invasive wire method, and shows distinct advantages in terms of accuracy, reliability, convenience and cost-effectiveness, but there are also factors that affect accuracy. The results of this review demonstrates that AI-based iFR technology is currently one of the most promising technologies. The main challenges and directions for future development are also discussed. These technologies bring new ideas for the non-invasive assessment of coronary artery disease, and are expected to promote the technological progress in this field.

Prognostic value of optical flow ratio for cardiovascular outcomes in patients after percutaneous coronary stent implantation

Background

The relationship between the optical flow ratio (OFR) and clinical outcomes in patients with coronary artery disease (CAD) after percutaneous coronary stent implantation (PCI) remains unknown.

Objective

To examine the correlation between post-PCI OFR and clinical outcomes in patients with CAD following PCI.

Methods

Patients who underwent optical coherence tomography (OCT) guided PCI at Guangdong Provincial People's Hospital were retrospectively and continuously enrolled. Clinical data, post-PCI OCT characteristics, and OFR measurements were collected and analyzed to identify predictors of target vessel failure (TVF) after PCI.

Results

Among 354 enrolled patients, 26 suffered TVF during a median follow-up of 484 (IQR: 400-774) days. Post-PCI OFR was significantly lower in the TVF group than in the non-TVF group (0.89 vs. 0.93; P = 0.001). In multivariable Cox regression analysis, post-PCI OFR (HR per 0.1 increase: 0.60; 95% CI: 0.41-0.89; P = 0.011), large stent edge dissection (HR: 3.85; 95% CI: 1.51-9.84; P = 0.005) and thin-cap fibroatheroma (TCFA) (HR: 2.95; 95% CI: 1.19-7.35; P = 0.020) in the non-stented segment were independently associated with TVF. In addition, the inclusion of post-PCI OFR to baseline characteristics and post-PCI OCT findings improved the predictive power of the model to distinguish subsequent TVF after PCI (0.838 vs. 0.796; P = 0.028).

Conclusion

The post-PCI OFR serves as an independent determinant of risk for TVF in individuals with CAD after PCI. The inclusion of post-PCI OFR assessments, alongside baseline characteristics and post-PCI OCT findings, substantially enhances the capacity to differentiate the subsequent manifestation of TVF in CAD patients following PCI.

Diagnostic Performance of Intracoronary Optical Coherence Tomography-Modulated Quantitative Flow Ratio for Assessing Coronary Stenosis

Background

A novel method for fast computation of Murray law-based quantitative flow ratio (μQFR) from coregistered angiography and optical coherence tomography (OCT) was recently developed. This study aimed to evaluate the diagnostic performance of this OCT-modulated μQFR (OCT-μFR).

Methods

Patients who underwent coronary angiography, OCT, and fractional flow reserve (FFR) were retrospectively enrolled. μQFR was computed from a single angiographic projection. Subsequently, OCT image pullback was coregistered with the angiogram, and OCT-μFR was calculated based on the coregistered data. The same cut-off value of 0.80 was used for OCT-μFR, μQFR, and FFR to define ischemia.

Results

A paired comparison of OCT-μFR and μQFR was performed in 269 vessels from 218 patients. The mean FFR was 0.81 ± 0.11, and 45.0% of vessels had an FFR ≤0.80. OCT-μFR showed a better correlation with FFR than μQFR (r = 0.83 vs 0.76, P = .018) and numerically higher diagnostic performance (area under the curve [AUC] = 0.95 vs 0.92, P = .057). Sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio for OCT-μFR to identify ischemia-causing stenosis were 89.3%, 93.2%, 91.5%, 91.4%, 13.2, and 0.1, respectively. In addition, OCT-μFR showed significantly higher diagnostic performance compared with μQFR in vessels with suboptimal angiographic image quality (AUC = 0.93 vs 0.87, P = .028) and tandem lesions (AUC = 0.94 vs 0.87, P = .017).

Conclusions

Computation of OCT-μFR was feasible and accurately identified physiologically significant coronary stenosis with simultaneous morphological assessment. In vessels with suboptimal angiographic image quality or tandem lesions, OCT-μFR had a higher diagnostic performance than angiography-based μQFR.