Diagnostic accuracy of intracoronary optical coherence tomography-derived fractional flow reserve for assessment of coronary stenosis severity

Predictive models for cholesterol crystals and plaque vulnerability in acute myocardial infarction: Insights from an optical coherence tomography study

BACKGROUND

Cholesterol crystals (CCs) are recognized as a risk factor for vulnerable atherosclerotic plaque rupture (PR) and major adverse cardiovascular events. However, their predictive factors and association with plaque vulnerability in patients with acute myocardial infarction (AMI) remain insufficiently explored. Therefore, This study aims to investigate the association between CCs and plaque vulnerability in culprit lesions of AMI patients, identify the factors influencing CCs formation, and develop a predictive model for CCs.

METHODS

A total of 431 culprit lesions from AMI patients who underwent pre-intervention optical coherence tomography (OCT) imaging were analyzed. Patients were divided into groups based on the presence or absence of CCs and PR. The relationship between CCs and plaque vulnerability was evaluated. A risk nomogram for predicting CCs was developed using the least absolute shrinkage and selection operator and logistic regression analysis.

RESULTS

CCs were identified in 64.5 % of patients with AMI. The presence of CCs was associated with a higher prevalence of vulnerable plaque features, such as thin-cap fibroatheroma (TCFA), PR, macrophage infiltration, neovascularization, calcification, and thrombus, compared to patients without CCs. The CCs model demonstrated an area under the curve (AUC) of 0.676 for predicting PR. Incorporating CCs into the TCFA model (AUC = 0.656) significantly enhanced predictive accuracy, with a net reclassification improvement index of 0.462 (95 % confidence interval [CI]: 0.263-0.661, p < 0.001) and an integrated discrimination improvement index of 0.031 (95 % CI: 0.013-0.048, p = 0.001). Multivariate regression analysis identified the atherogenic index of plasma (odds ratio [OR] = 2.417), TCFA (OR = 1.759), macrophage infiltration (OR = 3.863), neovascularization (OR = 2.697), calcification (OR = 1.860), and thrombus (OR = 2.430) as independent risk factors for CCs formation. The comprehensive model incorporating these factors exhibited reasonable discriminatory ability, with an AUC of 0.766 (95 % CI: 0.717-0.815) in the training set and 0.753 (95 % CI: 0.704-0.802) in the internal validation set, reflecting good calibration. Decision curve analysis suggested that the model has potential clinical utility within a threshold probability range of approximately 18 % to 85 %.

CONCLUSIONS

CCs were associated with plaque vulnerability in the culprit lesions of AMI patients. Additionally, this study identified key factors influencing CCs formation and developed a predictive model with potential clinical applicability.

Advances in the Computational Assessment of Disturbed Coronary Flow and Wall Shear Stress: A Contemporary Review

Coronary artery blood flow is influenced by various factors including vessel geometry, hemodynamic conditions, timing in the cardiac cycle, and rheological conditions. Multiple patterns of disturbed coronary flow may occur when blood flow separates from the laminar plane, associated with inefficient blood transit, and pathological processes modulated by the vascular endothelium in response to abnormal wall shear stress. Current simulation techniques, including computational fluid dynamics and fluid-structure interaction, can provide substantial detail on disturbed coronary flow and have advanced the contemporary understanding of the natural history of coronary disease. However, the clinical application of these techniques has been limited to hemodynamic assessment of coronary disease severity, with the potential to refine the assessment and management of coronary disease. Improved computational efficiency and large clinical trials are required to provide an incremental clinical benefit of these techniques beyond existing tools. This contemporary review is a clinically relevant overview of the disturbed coronary flow and its associated pathological consequences. The contemporary methods to assess disturbed flow are reviewed, including clinical applications of these techniques. Current limitations and future opportunities in the field are also discussed.

Advances in Technology and Technique in Percutaneous Coronary Intervention: A Clinical Review

Percutaneous coronary intervention (PCI) has become the standard procedure for patients with angina and acute coronary syndrome. From the perspective of technology and technique, PCI has advanced over the last four decades, resulting in considerably improved clinical outcomes in patients with coronary artery disease in the current era. In this review article, we summarize recent advances, promising technologies, and areas for research in the field of PCI.

Value of Combined Optical Coherence Tomography and Optical Flow Ratio Measurements After Percutaneous Coronary Intervention

BACKGROUND

Optical flow ratio (OFR) is a novel computational fractional flow reserve derived from optical coherence tomography (OCT). However, the impact of combining post-stenting morphology (OCT) and physiology (OFR) remains largely unknown.

METHODS

OCT and OFR were analysed at an independent core laboratory. Target lesion failure (TLF) was defined as the composite of cardiac death, target lesion myocardial infarction, and target lesion revascularisation. Suboptimal stent deployment was identified with at least 1 TLF-related OCT or OFR characteristic.

RESULTS

A total of 448 patients with acute coronary syndrome (459 vessels) were assessed. Stent expansion < 80%, minimal stent area < 4.5 mm2, stent edge lipid-rich plaque and OFR < 0.90 were independent predictors of TLF (all P < 0.001). Patients with OCT-suboptimal (adjusted hazard ratio [aHR] 7.88, 95% CI 2.73-22.72,-P < 0.001) or OFR-suboptimal (aHR 5.78, 95% CI 2.54-13.14; P < 0.001) stent deployment showed significantly higher risk of TLF compared with those with optimal stent deployment, with a significant interaction (Pinteraction < 0.001). OCT and OFR both-suboptimal stent deployment was confirmed as an independent predictor of TLF (aHR 9.39, 95% CI 4.25-20.76; P < 0.001).

CONCLUSIONS

Combined OCT and OFR conferred an optimal reclassification of stent deployment, which may aid in decision making regarding a tailored PCI strategy for optimal stent deployment.

Illusion of revascularization: does anyone achieve optimal revascularization during percutaneous coronary intervention?

This Perspective article is a form of 'pastiche', inspired by the 1993 review by Lincoff and Topol entitled 'Illusion of reperfusion', and explores how their concept continues to apply to percutaneous revascularization in patients with coronary artery disease and ischaemia. Just as Lincoff and Topol argued that reperfusion of acute myocardial infarction was facing unresolved obstacles that hampered clinical success in 1993, we propose that challenging issues are similarly jeopardizing the potential benefits of stent-based angioplasty today. By analysing the appropriateness and efficacy of percutaneous coronary intervention (PCI), we emphasize the limitations of relying solely on visual angiographic guidance, which frequently leads to inappropriate stenting and overtreatment in up to one-third of patients and the associated increased risk of periprocedural myocardial infarction. The lack of optimal revascularization observed in half of patients undergoing PCI confers risks such as suboptimal physiology after PCI, residual angina and long-term stent-related events, leaving an estimated 76% of patients with an 'illusion of revascularization'. These outcomes highlight the need to refine our diagnostic tools by integrating physiological assessments with targeted intracoronary imaging and emerging strategies, such as co-registration systems and angiography-based computational methods enhanced by artificial intelligence, to achieve optimal revascularization outcomes.

Role of physiology in the management of multivessel disease among patients with acute coronary syndrome

Multivessel coronary artery disease (CAD), defined as ≥50% stenosis in 2 or more epicardial arteries, is associated with a high burden of morbidity and mortality in acute coronary syndrome (ACS) patients. A salient challenge for managing this cohort is selecting the optimal revascularisation strategy, for which the use of coronary physiology has been increasingly recognised. Fractional flow reserve (FFR) is an invasive, pressure wire-based, physiological index measuring the functional significance of coronary lesions. Understanding this can help practitioners evaluate which lesions could induce myocardial ischaemia and, thus, decide which vessels require urgent revascularisation. Non-hyperaemic physiology-based indices, such as instantaneous wave-free ratio (iFR), provide valid alternatives to FFR. While FFR and iFR are recommended by international guidelines in stable CAD, there is ongoing discussion regarding the role of physiology in patients with ACS and multivessel disease (MVD); growing evidence supports FFR use in the latter. Compelling findings show FFR-guided complete percutaneous coronary intervention (PCI) can reduce adverse cardiovascular events, mortality, and repeat revascularisations in ACS and MVD patients compared to angiography-based PCI. However, FFR is limited in identifying non-flow-limiting vulnerable plaques, which can disadvantage high-risk patients. Here, integrating coronary physiology assessment with intracoronary imaging in decision-making can improve outcomes and quality of life. Further research into novel physiology-based tools in ACS and MVD is needed. This review aims to highlight the key evidence surrounding the role of FFR and other functional indices in guiding PCI strategy in ACS and MVD patients.

Development of machine learning models for fractional flow reserve prediction in angiographically intermediate coronary lesions

BACKGROUND

Fractional flow reserve (FFR) represents the gold standard in guiding the decision to proceed or not with coronary revascularization of angiographically intermediate coronary lesion (AICL). Optical coherence tomography (OCT) allows to carefully characterize coronary plaque morphology and lumen dimensions.

OBJECTIVES

We sought to develop machine learning (ML) models based on clinical, angiographic and OCT variables for predicting FFR.

METHODS

Data from a multicenter, international, pooled analysis of individual patient's level data from published studies assessing FFR and OCT on the same target AICL were collected through a dedicated database to train (n = 351) and validate (n = 151) six two-class supervised ML models employing 25 clinical, angiographic and OCT variables.

RESULTS

A total of 502 coronary lesions in 489 patients were included. The AUC of the six ML models ranged from 0.71 to 0.78, whereas the measured F1 score was from 0.70 to 0.75. The ML algorithms showed moderate sensitivity (range: 0.68-0.77) and specificity (range: 0.59-0.69) in detecting patients with a positive or negative FFR. In the sensitivity analysis, using 0.75 as FFR cut-off, we found a higher AUC (0.78-0.86) and a similar F1 score (range: 0.63-0.76). Specifically, the six ML models showed a higher specificity (0.71-0.84), with a similar sensitivity (0.58-0.80) with respect to 0.80 cut-off.

CONCLUSIONS

ML algorithms derived from clinical, angiographic, and OCT parameters can identify patients with a positive or negative FFR.

Simulation of coronary fractional flow reserve and whole-cycle flow based on optical coherence tomography in individual patients with coronary artery disease

Computer simulations of coronary fractional flow reserve (FFR) based on coronary imaging have emerged as an attractive alternative to invasive measurements. However, most methods are proprietary and employ non-physiological assumptions. Our aims were to develop and validate a physiologically realistic open-source simulation model for coronary flow, and to use this model to predict FFR based on intracoronary optical coherence tomography (OCT) data in individual patients. We included patients undergoing elective coronary angiography with angiographic borderline coronary stenosis. Invasive measurements of coronary hyperemic pressure and absolute flow and OCT imaging were performed. A computer model of coronary flow incorporating pulsatile flow and the effect of left ventricular contraction was developed and calibrated, and patient-specific flow simulation was performed. Forty-eight coronary arteries from 41 patients were included in the analysis. Average FFR was 0.79 ± 0.14, and 50% had FFR ≤ 0.80. Correlation between simulated and measured FFR was high (r = 0.83, p < 0.001). Average difference between simulated FFR and observed FFR in individual patients was - 0.009 ± 0.076. Overall diagnostic accuracy for simulated FFR ≤ 0.80 in predicting observed FFR ≤ 0.80 was 0.88 (0.75-0.95) with sensitivity 0.79 (0.58-0.93) and specificity 0.96 (0.79-1.00). The positive predictive value was 0.95 (0.75-1.00) and the negative predictive value was 0.82 (0.63-0.94). In conclusion, realistic simulations of whole-cycle coronary flow can be produced based on intracoronary OCT data with a new, computationally simple simulation model. Simulated FFR had moderate numerical agreement with observed FFR and a good diagnostic accuracy for predicting hemodynamic significance of coronary stenoses.

Bifurcation detection in intravascular optical coherence tomography using vision transformer based deep learning

Objective. Bifurcation detection in intravascular optical coherence tomography (IVOCT) images plays a significant role in guiding optimal revascularization strategies for percutaneous coronary intervention (PCI). We propose a bifurcation detection method using vision transformer (ViT) based deep learning in IVOCT.Approach. Instead of relying on lumen segmentation, the proposed method identifies the bifurcation image using a ViT-based classification model and then estimate bifurcation ostium points by a ViT-based landmark detection model.Main results. By processing 8640 clinical images, the Accuracy and F1-score of bifurcation identification by the proposed ViT-based model are 2.54% and 16.08% higher than that of traditional non-deep learning methods, are similar to the best performance of convolutional neural networks (CNNs) based methods, respectively. The ostium distance error of the ViT-based model is 0.305 mm, which is reduced 68.5% compared with the traditional non-deep learning method and reduced 24.81% compared with the best performance of CNNs based methods. The results also show that the proposed ViT-based method achieves the highest success detection rate are 11.3% and 29.2% higher than the non-deep learning method, and 4.6% and 2.5% higher than the best performance of CNNs based methods when the distance section is 0.1 and 0.2 mm, respectively.Significance. The proposed ViT-based method enhances the performance of bifurcation detection of IVOCT images, which maintains a high correlation and consistency between the automatic detection results and the expert manual results. It is of great significance in guiding the selection of PCI treatment strategies.

After RIPCORD 2, FAME 3, FLOWER-MI and FUTURE: Has the Pressure Wire had its Day?

Recent years have seen the publication of several high-profile, negative trials about pressure wires. This has coincided with a consistent increase in the ratio of angioplasty for acute coronary syndromes versus percutaneous coronary intervention in stable coronary artery disease, a greater use of intracoronary imaging during percutaneous coronary intervention and the continued evolution of computational fluid dynamics-derived estimations of fractional flow reserve from both CT and invasive coronary angiography. Consequently, many interventional cardiologists now wonder if the pressure wire will soon become obsolete. This head-to-head article provides a critical appraisal of recent trial data, discusses a potential evolution in how pressure wires are used and debates the motion that the device (and by extension, invasive assessment of coronary physiology) has now had its day.

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.

Renal Dysfunction Increases Risk of Adverse Cardiovascular Events in 5-Year Follow-Up Study of Intermediate Coronary Artery Lesions

BACKGROUND Progression of chronic coronary syndrome (CCS) is influenced by chronic kidney disease (CKD). This 5-year follow-up study aimed to assess 100 patients with 118 intermediate coronary artery lesions evaluated by fractional flow reserve (FFR) and intravascular imaging stratified according to renal function. MATERIAL AND METHODS This prospective study enrolled patients with intermediate coronary stenosis identified by coronary angiogram. Patients with severe renal dysfunction (estimated glomerular filtration rate (eGFR) <45 ml/min/1.73 m²) were excluded from the study. The remaining were divided into 2 groups according to eGFR: 45-60 ml/min/1.73 m² for mild-to-moderate renal dysfunction and >60 ml/min/1.73 m² for no renal dysfunction. We analyzed intermediate-grade stenoses (40-80% as assessed in coronary angiography) with the use of optical coherence tomography (OCT), FFR, and intravascular ultrasound (IVUS). RESULTS Renal dysfunction patients were older (67.7±8.1 vs 63.6±9.7 years, P=0.044). Lesion characteristics, including plaque type and minimal lumen area in OCT, showed no significant differences between the renal dysfunction and no renal dysfunction groups. Thin-cap fibroatheroma, calcific plaques, lipidic plaques, and fibrous plaques had similar prevalence. FFR values and IVUS parameters did not significantly differ between the groups. Over a 5-year follow-up, individuals with mild-to-moderate renal dysfunction had an elevated risk of all-cause mortality and major adverse cardiovascular events in multivariate analyses adjusted for age and sex. CONCLUSIONS Mild-to-moderate renal dysfunction was not associated with significant differences in OCT- and IVUS-derived plaque morphology nor with functional indices characterizing intermediate-grade coronary stenoses. Renal dysfunction was related to a higher risk of all-cause mortality and major adverse cardiovascular events prevalence in 5-year follow-up.

Co-registered optical coherence tomography and X-ray angiography for the prediction of fractional flow reserve

Cardiovascular disease (CVD) stands as the leading global cause of mortality, and coronary artery disease (CAD) has the highest prevalence, contributing to 42% of these fatalities. Recognizing the constraints inherent in the anatomical assessment of CAD, Fractional Flow Reserve (FFR) has emerged as a pivotal functional diagnostic metric. Herein, we assess the potential of employing an ensemble approach with deep neural networks (DNN) to predict invasively measured Fractional Flow Reserve (FFR) using raw anatomical data extracted from both optical coherence tomography (OCT) and X-ray coronary angiography (XA). In this study, we used a challenging dataset, with 46% of the lesions falling within the FFR range of 0.75 to 0.85. Despite this complexity, our model achieved an accuracy of 84.3%, demonstrating a sensitivity of 87.5% and a specificity of 81.4%. Our results demonstrate that incorporating both OCT and XA signals, co-registered, as inputs for the DNN model leads to an important increase in overall accuracy.

Virtual Hemodynamic Assessment of Coronary Lesions: The Advent of Functional Angiography and Coronary Imaging

The fractional flow reserve (FFR) is well recognized as a gold standard measure for the estimation of functional coronary stenosis. Technological progressions in image processing have empowered the reconstruction of three-dimensional models of the coronary arteries via both non-invasive and invasive imaging modalities. The application of computational fluid dynamics (CFD) techniques to coronary 3D anatomical models allows the virtual evaluation of the hemodynamic significance of a coronary lesion with high diagnostic accuracy.

METHODS

Search of the bibliographic database for articles published from 2011 to 2023 using the following search terms: invasive FFR and non-invasive FFR. Pooled analysis of the sensitivity and specificity, with the corresponding confidence intervals from 32% to 94%. In addition, the summary processing times were determined.

RESULTS

In total, 24 studies published between 2011 and 2023 were included, with a total of 13,591 patients and 3345 vessels. The diagnostic accuracy of the invasive and non-invasive techniques at the per-patient level was 89% (95% CI, 85-92%) and 76% (95% CI, 61-80%), respectively, while on the per-vessel basis, it was 92% (95% CI, 82-88%) and 81% (95% CI, 75-87%), respectively.

CONCLUSION

These opportunities providing hemodynamic information based on anatomy have given rise to a new era of functional angiography and coronary imaging. However, further validations are needed to overcome several scientific and computational challenges before these methods are applied in everyday clinical practice.

Optical Coherence Tomography Measures Predicting Fractional Flow Reserve: The OMEF Study

Background

Optical coherence tomography (OCT) allows to carefully characterize coronary plaque morphology and lumen dimensions. We sought to evaluate the value of OCT in predicting fractional flow reserve (FFR).

Methods

We performed a multicenter, international, pooled analysis of individual patient-level data from published studies assessing FFR and OCT on the same vessel. Data from stable or unstable patients who underwent both FFR and OCT of the same coronary artery were collected through a dedicated database. Predefined OCT parameters were minimum lumen area (MLA), percentage area stenosis (%AS), and presence of thrombus or plaque rupture. Primary end point was FFR ≤0.80. Secondary outcome was the incidence of major adverse cardiac events in patients not undergoing revascularization based on negative FFR (>0.80).

Results

A total of 502 coronary lesions in 489 patients were included. A significant correlation was observed between OCT-MLA and FFR values (R = 0.525; P < .001), and between OCT-%AS and FFR values (R = -0.482; P < .001). In Receiver operating characteristic analysis, MLA <2.0 mm2 showed a good discriminative power to predict an FFR ≤0.80 (AUC, 0.80), whereas %AS >73% showed a moderate discriminative power (AUC, 0.73). When considering proximal coronary segments, the best OCT cutoff values predicting an FFR ≤0.80 were MLA <3.1 mm2 (AUC, 0.82), and %AS >61% (AUC, 0.84). In patients with a negative FFR not revascularized, the combination of lower MLA and higher %AS had a trend toward worse outcome (which was statistically significant in the analysis restricted to proximal vessels).

Conclusions

OCT lumen measures (MLA, %AS) may predict FFR, and different cutoffs are needed for proximal vessels.

Artificial Intelligence for the Interventional Cardiologist: Powering and Enabling OCT Image Interpretation

Intravascular optical coherence tomography (IVOCT) is a form of intra-coronary imaging that uses near-infrared light to generate high-resolution, cross-sectional, and 3D volumetric images of the vessel. Given its high spatial resolution, IVOCT is well-placed to characterise coronary plaques and aid with decision-making during percutaneous coronary intervention. IVOCT requires significant interpretation skills, which themselves require extensive education and training for effective utilisation, and this would appear to be the biggest barrier to its widespread adoption. Various artificial intelligence-based tools have been utilised in the most contemporary clinical IVOCT systems to facilitate better human interaction, interpretation and decision-making. The purpose of this article is to review the existing and future technological developments in IVOCT and demonstrate how they could aid the operator.

Optimal diagnostic approach for using CT-derived quantitative flow ratio in patients with stenosis on coronary computed tomography angiography

BACKGROUND

Coronary computed tomography angiography (CCTA)-derived quantitative flow ratio (CT-QFR) is an on-site non-invasive technique estimating invasive fractional flow reserve (FFR). This study assesses the diagnostic performance of using most distal CT-QFR versus lesion-specific CT-QFR approach for identifying hemodynamically obstructive coronary artery disease (CAD).

METHODS

Prospectively enrolled de novo chest pain patients (n ​= ​445) with ≥50 ​% visual diameter stenosis on CCTA were referred for invasive evaluation. On-site CT-QFR was analyzed post-hoc blinded to angiographic data and obtained as both most distal (MD-QFR) and lesion-specific CT-QFR (LS-QFR). Abnormal CT-QFR was defined as ≤0.80. Hemodynamically obstructive CAD was defined as invasive FFR ≤0.80 or ≥70 ​% diameter stenosis by 3D-quantitative coronary angiography.

RESULTS

In total 404/445 patients had paired CT-QFR and invasive analyses of whom 149/404 (37 ​%) had hemodynamically obstructive CAD. MD-QFR and LS-QFR classified 188 (47 ​%) and 165 (41 ​%) patients as abnormal, respectively. Areas under the receiver-operating characteristic curve for MD-QFR was 0.83 vs. 0.85 for LS-QFR, p ​= ​0.01. Sensitivities for MD-QFR and LS-QFR were 80 ​% (95%CI: 73-86) vs. 77 ​% (95%CI: 69-83), p ​= ​0.03, respectively, and specificities were 73 ​% (95%CI: 67-78) vs. 80 ​% (95%CI: 75-85), p ​< ​0.01, respectively. Positive predictive values for MD-QFR and LS-QFR were 63 ​% vs. 69 ​%, p ​< ​0.01, respectively, and negative predictive values for MD-QFR and LS-QFR were 86 ​% vs. 85 ​%, p ​= ​0.39, respectively).

CONCLUSION

Using a lesion-specific CT-QFR approach has superior discrimination of hemodynamically obstructive CAD compared to a most distal CT-QFR approach. CT-QFR identified most cases of hemodynamically obstructive CAD while a normal CT-QFR excluded hemodynamically obstructive CAD in the majority of patients.

Diagnostic performance of angiography-derived fractional flow reserve and CT-derived fractional flow reserve: A systematic review and Bayesian network meta-analysis

OBJECTIVE

Accumulating evidence has demonstrated that fractional flow reserves (FFRs) derived from invasive coronary angiograms (CA-FFRs) and coronary computed tomography angiography-derived FFRs (CT-FFRs) are promising alternatives to wire-based FFRs. However, it remains unclear which method has better diagnostic performance. This systematic review and meta-analysis aimed to compare the diagnostic performances of the two approaches.

METHODS

The Cochrane Library, PubMed, Embase, Medline (Ovid), the Chinese China National Knowledge Infrastructure Database (CNKI), VIP, and WanFang Data databases were searched for relevant studies that included comparisons between CA-FFR and CT-FFR, from their respective database inceptions until January 1, 2023. Studies where both noninvasive FFR (including CA-FFR and CT-FFR) and invasive FFR (as a reference standard) were performed for the diagnosis of ischemic coronary artery disease and were designed as prospective, paired diagnostic studies, were pulled. The diagnostic test accuracy method and Bayesian hierarchical summary receiver operating characteristic (ROC) model for network meta-analysis (NMA) of diagnostic tests (HSROC-NMADT) were both used to perform a meta-analysis on the data.

RESULTS

Twenty-six studies were included in this NMA. The results from both the diagnostic test accuracy and HSROC-NMADT methods revealed that the diagnostic accuracy of CA-FFR was higher than that of CT-FFR, in terms of sensitivity (Se; 0.86 vs. 0.84), specificity (Sp; 0.90 vs. 0.78), positive predictive value (PPV; 0.83 vs. 0.70), and negative predictive value (NPV; 0.91 vs. 0.89) for the detection of myocardial ischemia. A cumulative ranking curve analysis indicated that CA-FFR had a higher diagnostic accuracy than CT-FFR in the context of this study, with a higher area under the ROC curve (AUC; 0.94 vs. 0.87).

CONCLUSIONS

Although both of these two commonly used virtual FFR methods showed high levels of diagnostic accuracy, we demonstrated that CA-FFR had a better Se, Sp, PPV, NPV, and AUC than CT-FFR. However, this study provided only indirect comparisions; therefore, larger studies are warranted to directly compare the diagnostic performances of these two approaches.

Intravascular Imaging-Derived Physiology-Basic Principles and Clinical Application

Intravascular imaging-derived physiology is emerging as a promising tool allowing simultaneous anatomic and functional lesion assessment. Recently, several optical coherence tomography-based and intravascular ultrasound-based fractional flow reserve (FFR) indices have been developed that compute FFR through computational fluid dynamics, fluid dynamics equations, or machine-learning methods. This review aims to provide an overview of the currently available intravascular imaging-based physiologic indices, their diagnostic performance, and clinical application.

Assessment of the functional severity of coronary lesions from optical coherence tomography based on ensembled learning

BACKGROUND

Atherosclerosis is one of the most frequent cardiovascular diseases. The dilemma faced by physicians is whether to treat or postpone the revascularization of lesions that fall within the intermediate range given by an invasive fractional flow reserve (FFR) measurement. The paper presents a monocentric study for lesions significance assessment that can potentially cause ischemia on the large coronary arteries.

METHODS

A new dataset is acquired, comprising the optical coherence tomography (OCT) images, clinical parameters, echocardiography and FFR measurements collected from 80 patients with 102 lesions, with stable multivessel coronary artery disease. Having the ground truth given by the invasive FFR measurement, the dataset is challenging because almost 40% of the lesions are in the gray zone, having an FFR value between 0.75 and 0.85. Twenty-six features are extracted from OCT images, clinical characteristics, and echocardiography and the most relevant are identified by examining the models' accuracy. An ensembled learning is performed for solving the binary classification problem of lesion significance considering the leave-one-out cross-validation approach.

RESULTS

Ensemble models are designed from the multi-features voting from 5 features models by prediction aggregation with a maximum accuracy of 81.37% and a maximum area under the curve score (AUC) of 0.856.

CONCLUSIONS

The proposed explainable supervised learning-based lesion classification is a new method that can be improved by training with a larger multicenter dataset for further designing a tool for guiding the decision making of the clinician for the cases outside the gray zone and for the other situation extra clinical information about the lesion is needed.

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 accuracy of a novel optical coherence tomography-based fractional flow reserve algorithm for assessment of coronary stenosis significance

BACKGROUND

This study aimed to introduce a novel optical coherence tomography-derived fractional flow reserve (FFR) computational approach and assess the diagnostic performance of the algorithm for assessing physiological function.

METHODS

The fusion of coronary optical coherence tomography and angiography was used to generate a novel FFR algorithm (AccuFFRoct) to evaluate functional ischemia of coronary stenosis. In the current study, a total of 34 consecutive patients were included, and AccuFFRoct was used to calculate the FFR for these patients. With the wire-measured FFR as the reference standard, we evaluated the performance of our approach by accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

Per vessel accuracy, sensitivity, specificity, PPV, and NPV for AccuFFRoct in identifying hemodynamically significant coronary stenosis were 93.8%, 94.7%, 92.3%, 94.7%, and 92.3%, respectively, were found. Good correlation (Pearson's correlation coefficient r = 0.80, p < 0.001) between AccuFFRoct and FFR was observed. The Bland-Altman analysis showed a mean difference value of -0.037 (limits of agreement: -0.189 to 0.115). The area under the receiver-operating characteristic curve (AUC) of AccuFFRoct in identifying physiologically significant stenosis was 0.94, which was higher than the minimum lumen area (MLA, AUC = 0.91) and significantly higher than the diameter stenosis (%DS, AUC = 0.78).

CONCLUSIONS

This clinical study shows the efficiency and accuracy of AccuFFRoct for clinical implementation when using invasive FFR measurement as a reference. It could provide important insights into coronary imaging superior to current methods based on the degree of coronary artery stenosis.

Clinical quantitative coronary artery stenosis and coronary atherosclerosis imaging: a Consensus Statement from the Quantitative Cardiovascular Imaging Study Group

The detection and characterization of coronary artery stenosis and atherosclerosis using imaging tools are key for clinical decision-making in patients with known or suspected coronary artery disease. In this regard, imaging-based quantification can be improved by choosing the most appropriate imaging modality for diagnosis, treatment and procedural planning. In this Consensus Statement, we provide clinical consensus recommendations on the optimal use of different imaging techniques in various patient populations and describe the advances in imaging technology. Clinical consensus recommendations on the appropriateness of each imaging technique for direct coronary artery visualization were derived through a three-step, real-time Delphi process that took place before, during and after the Second International Quantitative Cardiovascular Imaging Meeting in September 2022. According to the Delphi survey answers, CT is the method of choice to rule out obstructive stenosis in patients with an intermediate pre-test probability of coronary artery disease and enables quantitative assessment of coronary plaque with respect to dimensions, composition, location and related risk of future cardiovascular events, whereas MRI facilitates the visualization of coronary plaque and can be used in experienced centres as a radiation-free, second-line option for non-invasive coronary angiography. PET has the greatest potential for quantifying inflammation in coronary plaque but SPECT currently has a limited role in clinical coronary artery stenosis and atherosclerosis imaging. Invasive coronary angiography is the reference standard for stenosis assessment but cannot characterize coronary plaques. Finally, intravascular ultrasonography and optical coherence tomography are the most important invasive imaging modalities for the identification of plaques at high risk of rupture. The recommendations made in this Consensus Statement will help clinicians to choose the most appropriate imaging modality on the basis of the specific clinical scenario, individual patient characteristics and the availability of each imaging modality.

Practical Application of Coronary Physiologic Assessment: Asia-Pacific Expert Consensus Document: Part 1

Coronary physiologic assessment is performed to measure coronary pressure, flow, and resistance or their surrogates to enable the selection of appropriate management strategy and its optimization for patients with coronary artery disease. The value of physiologic assessment is supported by a large body of evidence that has led to major recommendations in clinical practice guidelines. This expert consensus document aims to convey practical and balanced recommendations and future perspectives for coronary physiologic assessment for physicians and patients in the Asia-Pacific region based on updated information in the field that including both wire- and image-based physiologic assessment. This is Part 1 of the whole consensus document, which describes the general concept of coronary physiology, as well as practical information on the clinical application of physiologic indices and novel image-based physiologic assessment.

Fast Computational Approaches to Derive Fractional Pressure Ratio in Patients with Extracranial or Intracranial Symptomatic Stenosis

OBJECTIVE

We aimed to evaluate the performance of fast and straightforward Murray law-based quantitative flow ratio (μQFR) computation in cerebrovascular stenosis.

METHODS

A total of 30 patients with symptomatic stenosis of 50%-70% luminal stenosis and underwent fractional pressure ratio (FPR) assessment at our hospital were included in the present study. μQFR was applied to the interrogated vessel. An artificial intelligence algorithm was proposed for automatic delineation of lumen contours of cerebrovascular stenosis. We used invasive FPRs as a reference standard. Pearson's correlation coefficient (r) was used to assess the correlation strength between the μQFR and FPR, and Bland-Altman plots were used to evaluate the agreement between the μQFR and FPR. An analysis of the receiver operating characteristic was used to evaluate the performance of μQFR.

RESULTS

Our results displayed a strong positive correlations (r = 0.92; P < 0.001) between the μQFR and pressure wire FPR. Excellent agreement was observed between the μQFR and FPR with a mean difference of 0.01 ± 0.08 (range, -0.16 to 0.14; P = 0.263). The overall accuracy for identifying an FPR of ≤0.7 was 92% (95% confidence interval [CI], 85%-100%). The area under the receiver operating characteristic curve was higher for the μQFR (0.92; 95% CI, 0.81-0.98) than for diameter stenosis (0.88; 95% CI, 0.75-0.95). The positive likelihood ratio was 3.9 for the μQFR with a negative likelihood ratio of 0.

CONCLUSIONS

The μQFR computation has a strong correlation and agrees with the FPR calculated from the pressure wire. Therefore, the μQFR might provide an essential therapeutic aid for patients with symptomatic stenosis.

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.

Intravascular Imaging versus Physiological Assessment versus Biomechanics-Which Is a Better Guide for Coronary Revascularization

Today, coronary artery disease (CAD) continues to be a prominent cause of death worldwide. A reliable assessment of coronary stenosis represents a prerequisite for the appropriate management of CAD. Nevertheless, there are still major challenges pertaining to some limitations of current imaging and functional diagnostic modalities. The present review summarizes the current data on invasive functional and intracoronary imaging assessment using optical coherence tomography (OCT), and intravascular ultrasound (IVUS). Amongst the functional parameters-on top of fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR)-we point to novel angiography-based measures such as quantitative flow ratio (QFR), vessel fractional flow reserve (vFFR), angiography-derived fractional flow reserve (FFRangio), and computed tomography-derived flow fractional reserve (FFR-CT), as well as hybrid approaches focusing on optical flow ratio (OFR), computational fluid dynamics and attempts to quantify the forces exaggerated by blood on the coronary plaque and vessel wall.

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

BACKGROUND

Optical flow ratio (OFR) is a novel method for the fast computation of fractional flow reserve (FFR) from optical coherence tomography.

AIMS

We aimed to evaluate the diagnostic accuracy of OFR in assessing intermediate coronary stenosis using wire-based FFR as the reference.

METHODS

We performed an individual patient-level meta-analysis of all available studies with paired OFR and FFR assessments. The primary outcome was vessel-level diagnostic concordance of the OFR and FFR, using a cut-off of ≤0.80 to define ischaemia and ≤0.90 to define suboptimal post-percutaneous coronary intervention (PCI) physiology. This meta-analysis was registered in PROSPERO (CRD42021287726).

RESULTS

Five studies were finally included, providing 574 patients and 626 vessels (404 pre-PCI and 222 post-PCI) with paired OFR and FFR from 9 international centres. Vessel-level diagnostic concordance of the OFR and FFR was 91% (95% confidence interval [CI]: 88%-94%), 87% (95% CI: 82%-91%), and 90% (95% CI: 87%-92%) in pre-PCI, post-PCI, and overall, respectively. The overall sensitivity, specificity, and positive and negative predictive values were 84% (95% CI: 79%-88%), 94% (95% CI: 92%-96%), 90% (95% CI: 86%-93%), and 89% (95% CI: 86%-92%), respectively. Multivariate logistic regression indicated that a low pullback speed (odds ratio [OR] 7.02, 95% CI: 1.68-29.43; p=0.008) was associated with a higher risk of obtaining OFR values at least 0.10 higher than FFR. Increasing the minimal lumen area was associated with a lower risk of obtaining an OFR at least 0.10 lower than FFR (OR 0.39, 95% CI: 0.18-0.82; p=0.013).

CONCLUSIONS

This individual patient data meta-analysis demonstrated a high diagnostic accuracy of OFR. OFR has the potential to provide an improved integration of intracoronary imaging and physiological assessment for the accurate evaluation of coronary artery disease.

Quantitative flow ratio modulated by intracoronary optical coherence tomography for predicting physiological efficacy of percutaneous coronary intervention

BACKGROUND

The combination of coronary imaging assessment and blood flow perturbation estimation has the potential to improve percutaneous coronary intervention (PCI) guidance.

OBJECTIVES

We aimed to evaluate a novel method for fast computation of Murray law-based quantitative flow ratio (μQFR) from coregistered optical coherence tomography (OCT) and angiography (OCT-modulated μQFR, OCT-μQFR) in predicting physiological efficacy of PCI.

METHODS

Patients treated by OCT-guided PCI in the OCT-arm of the Fractional Flow Reserve versus Optical Coherence Tomography to Guide RevasculariZAtion of Intermediate Coronary Stenoses trial (FORZA, NCT01824030) were included. Based on angiography and OCT before PCI, simulated residual OCT-μQFR was computed by assuming full stent expansion to the intended-to-treat segment. Plaque composition was automatically characterized using a validated artificial intelligence algorithm. Actual post-PCI OCT-μQFR pullback was computed based on coregistration of angiography and OCT acquired immediately after PCI. Suboptimal functional stenting result was defined as OCT-μQFR ≤ 0.90.

RESULTS

Paired simulated residual OCT-μQFR and actual post-PCI OCT-μQFR were obtained in 76 vessels from 74 patients. Simulated residual OCT-μQFR showed good correlation (r = 0.80, p < 0.001), agreement (mean difference = -0.02 ± 0.02, p < 0.001), and diagnostic concordance (79%, 95% confidence interval: 70%-88%) with actual post-PCI OCT-μQFR. Actual post-PCI in-stent OCT-μQFR had a median value of 0.02 and was associated with left anterior descending artery lesion location (β = 0.38, p < 0.001), higher baseline total plaque burden (β = 0.25, p = 0.031), and fibrous plaque volume (β = 0.24, p = 0.026).

CONCLUSIONS

This study based on patients enrolled in a prospective OCT-guidance PCI trial shows that simulated residual OCT-μQFR had good correlation, agreement, and diagnostic concordance with actual post-PCI OCT-μQFR. In OCT-guided procedures, OCT-μQFR in-stent pressure drop was low and was significantly predicted by pre-PCI vessel/plaque characteristics.

Stent Angioplasty in Coronary Artery Anomalies With Intramural Course: When, Why, How, With What Results?

Although coronary artery anomalies include multiple disorders, few are likely to require intervention, given that the risk for critical sequelae (ie, sudden cardiac arrest and sudden cardiac death) is generally low. This article addresses which coronary artery anomaly carriers may need intervention and which interventions may be required. The recent introduction of stent angioplasty is discussed in particular, along with general reviews of nomenclature, various anatomical and functional presentations, quantitative diagnosis methods, and indications for surgical versus percutaneous intervention. Novel criteria for defining severe stenosis also are proposed. Optimal risk quantification depends on precise imaging that only intravascular ultrasonography or optical coherence tomography can reliably obtain. Accordingly, the technique of intravascular ultrasonography-monitored stent angioplasty is described in detail. Initial results from our group's study of 100 patients with right or left anomalous origin of a coronary artery from an opposite sinus of Valsalva with intramural course are reported. Future efforts should prospectively evaluate stent angioplasty in multicenter studies based on precise, consistent techniques and follow-up protocols, such as those initiated by our group. Comparisons with surgical results should be part of the program, with the understanding that detailed and complete results from those techniques will require long-term (5- to 10-year) studies.

Intravascular Imaging-Based Physiologic Assessment

Intravascular imaging (IVI), including intravascular ultrasound (IVUS) and optical coherence tomography (OCT), is clinically useful for assessing the luminal size, lesion length, and plaque characteristics, as well as for evaluating stent deployment; however, it is not designed to estimate myocardial ischemia accurately. Thus, several types of IVI-derived fractional flow reserve (FFR) (IVI-derived FFR) have been developed and reported. In general, the algorithms of virtual FFR are based on basic fluid dynamics equations (mainly Poiseuille and Borda-Carnot equations) and original microvascular models (fixed velocity or calculating coronary flow reserve). Although the models and assumptions used in the past reports were mostly based on the standard population (not independent patient data), the developed software calculated FFR with high accuracy (88% to 94%) with strong correlations between IVI-derived FFR and wire-based FFR (0.69 to 0.89). Given several other less invasive virtual FFR methods currently available for clinical use, IVI-derived FFR would be limited for the sole use of pre-percutaneous coronary intervention (PCI) physiological evaluation; however, it may play a unique role at PCI guidance and optimization, potentially allowing comprehensive and time/cost-saving assessment of both anatomical and physiological lesion properties using a single diagnostic device.

Coronary Physiologic Assessment Based on Angiography and Intracoronary Imaging

Intracoronary physiology testing has evolved as a promising diagnostic approach in the management of patients with coronary artery disease. The value of hyperemic translesional pressure ratios to estimate the functional relevance of coronary stenoses is supported by a wealth of outcomes data. The continuing drive to further simplify this approach led to the development of non-hyperemic pressure-based indices. Recent attention has focused on estimating functional significance without invasively measuring coronary pressure through the measurement of virtual indices derived from the coronary angiogram. By offering a routine assessment of the physiology of all the major epicardial coronary vessels, angiogram-derived physiology has the potential to modify current practice by facilitating more accurate patient-level, vessel-level, and even lesion-level decision making. This article reviews the current state of angiogram-derived physiology and speculates on its potential impact on clinical practice, in continuation to the previously published article on coronary physiology in this journal.

Advances in Diagnosis, Therapy, and Prognosis of Coronary Artery Disease Powered by Deep Learning Algorithms

Percutaneous coronary intervention has been a standard treatment strategy for patients with coronary artery disease with continuous ebullient progress in technology and techniques. The application of artificial intelligence and deep learning in particular is currently boosting the development of interventional solutions, improving the efficiency and objectivity of diagnosis and treatment. The ever-growing amount of data and computing power together with cutting-edge algorithms pave the way for the integration of deep learning into clinical practice, which has revolutionized the interventional workflow in imaging processing, interpretation, and navigation. This review discusses the development of deep learning algorithms and their corresponding evaluation metrics together with their clinical applications. Advanced deep learning algorithms create new opportunities for precise diagnosis and tailored treatment with a high degree of automation, reduced radiation, and enhanced risk stratification. Generalization, interpretability, and regulatory issues are remaining challenges that need to be addressed through joint efforts from multidisciplinary community.

Intracoronary Diagnostics in Patients with Acute Coronary Syndrome

Despite the increasing number of coronary interventions in China, long-term adverse cardiovascular events remain high, especially in patients with acute coronary syndromes (ACS). The advent of intracoronary imaging and coronary physiological diagnostic techniques, such as optical coherence tomography (OCT), intravascular ultrasound (IVUS), near infrared spectroscopy (NIRS), and flow reserve fraction (FFR), has optimized the diagnosis and risk classification of patients with ACS. Intracoronary diagnostics compensate for the deficiencies of conventional coronary angiography in identifying and incriminating lesions and high-risk lesions. The combination of intracoronary imaging and physiological techniques is expected to achieve a comprehensive evaluation of the structural features and physiology of the coronary arteries, thus further tailoring and improving the prognosis of patients.

Optical coherence tomography and coronary revascularization: from indication to procedural optimization

Angiography alone is the most commonly used imaging modality for guidance of percutaneous coronary interventions. Angiography is limited, however, by several factors, including that it only portrays a low resolution, two-dimensional outline of the lumen and does not inform on plaque composition and functional stenosis severity. Optical coherence tomography (OCT) is an intracoronary imaging technique that has superior spatial resolution compared to all other imaging modalities. High-resolution imaging of the vascular wall enables precise measurement of vessel wall and luminal dimensions, more accurately informing about the anatomic severity of epicardial stenoses, and also provides input for computational models to assess functional severity. The very high-resolution images also permit plaque characterization that may be informative for prognostication. Moreover, periprocedural imaging provides valuable information to guide lesion preparation, stent implantation and to evaluate acute stent complications for which iterative treatment might reduce the occurrence of major adverse stent events. As such, OCT represent a potential future all-in-one tool that provides the data necessary to establish the indications, procedural planning and optimization, and final evaluation of percutaneous coronary revascularization.

In-Hospital Heart Failure in Patients With Takotsubo Cardiomyopathy Due to Coronary Artery Disease: An Artificial Intelligence and Optical Coherence Tomography Study

BACKGROUND

Takotsubo syndrome (TTS) is often associated with symptoms of heart failure (HF) during the acute phase of the disease. 3-dimensional optical coherence tomography (OCT) may be used to assess the extent of angiographically silent underlying coronary artery disease (CAD). This study aims to use an artificial intelligence algorithm to analyze OCT findings and to determine whether the presence of pre-existing CAD predisposes TTS patients to present HF at admission.

METHODS

This is an observational and retrospective study that enrolled TTS patients who underwent coronary angiography and OCT examination of left anterior descending (LAD) coronary artery. Plaque characterization was automatically analyzed via an artificial intelligence model from OCT images. An angiography-derived index of microcirculatory resistance (IMRangio) using the optic flow ratio (OFR) was calculated to assess its correlation with plaque volumes.

RESULTS

Thirty-seven patients were included (94.6 % women) with a median age of 82.0 years. Ten patients (27 %) showed some degree of HF at admission. Sixty-seven coronary non-obstructive plaques were analyzed. Tissue compositional analysis showed that patients with HF had an increased overall plaque volume (79.0 mm³ vs 28.6 mm³; p = 0.011) and longer plaque lesion length (12.8 mm vs 7.2 mm; p = 0.006). Patients with HF also showed an increased percentage of lipidic and calcified plaque tissue (26.4 % vs 13.4 %; p = 0.019 and 4.5 % vs 0.0 %; p = 0.001, respectively). A moderate positive correlation was found between global overall plaque volume and IMRangio.

CONCLUSION

Increased overall plaque volume was associated with the development of HF during the acute phase of TTS, suggesting that the presence of angiographically silent underlying CAD may play a prognostic role in these patients.

Diagnostic efficacy of the optical flow ratio in patients with coronary heart disease: A meta-analysis

BACKGROUND

Coronary atherosclerotic heart disease (CAD) remains one of the most serious diseases threatening human health and life. PCI (Percutaneous Coronary Intervention) is the most common treatment for patients with CAD. A rigorous and comprehensive assessment of coronary artery lesions is now needed before PCI, however, there is no consensus on how best evaluate the combination of various intracavitary imaging techniques. By merging the benefits of physiological assessment and high-definition imaging, the optical flow ratio (OFR) has emerged as a novel technology with promising prospects for application.

METHODS

A systematic review of the literature was conducted. Studies that met the criteria of the meta-analysis were considered to assess OFR and FFR (fractional flow reserve). And the summary values of sensitivity and specificity of diagnostic tests and summary receiver operating curves (SROC) were calculated.

RESULTS

A total of 5 studies were included. The sensitivity and specificity of OFR in the diagnosis of coronary artery lesions were 0.83 (95% CI: 0.75-0.88) and 0.94 (95% CI: 0.91-0.96), respectively; the positive likelihood ratio and the negative likelihood ratio were 14 (95% CI: 9.3, 21.3) and 0.18 (95% CI:0.13, 0.27), respectively. OFR showed good correlation and consistency with FFR.

CONCLUSION

The new OFR technique achieve an encouraging diagnostic performance, which also showed good correlation and consistency with FFR.

Intravascular Imaging-Derived Physiology-Basic Principles and Clinical Application

Intravascular imaging-derived physiology is emerging as a promising tool allowing simultaneous anatomic and functional lesion assessment. Recently, several optical coherence tomography-based and intravascular ultrasound-based fractional flow reserve (FFR) indices have been developed that compute FFR through computational fluid dynamics, fluid dynamics equations, or machine-learning methods. This review aims to provide an overview of the currently available intravascular imaging-based physiologic indices, their diagnostic performance, and clinical application.

Angiographic quantitative flow ratio in acute coronary syndrome: beyond a tool to define ischemia-causing stenosis-a literature review

Background and Objective

Numerous studies have demonstrated the safety and effectiveness of physiology-guided coronary revascularization in chronic coronary syndrome, resulting in a high level of guideline recommendation for these patients. However, the application of coronary physiology in acute coronary syndrome (ACS), especially in the acute phase of myocardial infarction, remains challenging. Over the last decade, the number of novel physiological indices derived from the computation of angiography have been developed as alternatives to pressure wire-based fractional flow reserve. Among these angiography-based indices, the quantitative flow ratio (QFR) is undoubtedly the one with the largest amount of data cumulated so far. In this article, we aim to review the related studies that describe efforts to investigate the diagnostic role of QFR and discuss perspectives for its current and future applications in the setting of the ACS.

Methods

A literature search was performed on the electronic databases, including PubMed, Google Scholar and Web of Science covering publications in English up to May 2022.

Key Content and Findings

An emerging body of evidence has validated the diagnostic accuracy of angiography-derived QFR for the assessment of functional severity of coronary stenosis in both acute and chronic coronary syndromes. In parallel, multiple technologies, i.e., QFR-based pullback pressure gradient index, angiography-derived index of microcirculatory resistance and intravascular imaging-based morphofunctional evaluation methods, have been proposed, allowing operators to easily obtained physiological data of micro and macro-circulation, together with atherosclerotic lesion characteristics in catheterization laboratories. More recently, promising results supporting the clinical value of QFR in guiding revascularization and predicting outcomes for ACS patients have been published.

Conclusions

Angiography-based QFR bears the potential of a wider adoption of coronary physiology assessment in the ACS setting due to its quicker and less-invasive nature. However, the current evidence mainly derived from retrospective studies or post-hoc analyses of prospective trials. Future studies are needed to further explore the benefits of QFR-guided revascularization on outcomes in ACS.

Numerical vs analytical comparison with experimental fractional flow reserve values of right coronary artery stenosis

BACKGROUND: The fractional flow reserve (FFR) index has been widely accepted as a standard diagnostic method for identifying functional relevance of coronary stenosis. Since the invasive techniques used for its determination are associated with a certain risk of vascular injury, as well as with an increased cost, several non-invasive procedures have been developed. OBJECTIVE: The aim of this study was to compare FFR values for the coronary artery obtained by computational fluid dynamics (CFD) and coronary computed tomography angiography (CCTA). METHODS: Computation of FFR has been performed using both numerical and the analytical method. The numerical method employs CFD to solve the governing equations which relate to mass and momentum conservation (the continuity equation and the Navier-Stokes equations) as well as CCTA to generate the three-dimensional computational domain. After imposing the appropriate boundary conditions, the values of the pressure change are calculated and the FFR index is determined. Based on Bernoulli’s law, the analytical method calculates the overall pressure drop across the stenosis in the coronary artery, enabling FFR determination. RESULTS: The clinical data for twenty patients who underwent invasive coronary angiography are used to validate the results obtained by using CFD (together with CCTA) simulation and analytical solution. The medically measured FFR compared to the analytical one differs by about 4%, while, the difference is about 2.6% when compared to the numerical FFR. For FFR values below 0.8 (which are considered to be associated with myocardial ischemia) the standard error has a value of 0.01201, while the standard deviation is 0.02081. For FFR values above 0.80, these values are slightly higher. Bland-Altman analysis showed that medical measurement and numerical FFR were in good agreement (SD = 0.0292, p< 0.0001). CONCLUSIONS: The analytically calculated FFR has a slightly lower coefficient of determination than the numerically computed FFR when compared with experimental one. However, it can still give a reliable answer to the question of whether patients need a stent, bypass surgery or only drug treatment and it requires a significantly lower computation time.

Coronary physiology in the catheterisation laboratory: an A to Z practical guide

Coronary revascularisation, either percutaneous or surgical, aims to improve coronary flow and relieve myocardial ischaemia. The decision-making process in patients with coronary artery disease (CAD) remains largely based on invasive coronary angiography (ICA), even though until recently ICA could not assess the functional significance of coronary artery stenoses. Invasive wire-based approaches for physiological evaluations were developed to properly assess the ischaemic relevance of epicardial CAD. Fractional flow reserve (FFR) and later, instantaneous wave-free ratio (iFR), were shown to improve clinical outcomes in several patient subsets when used for coronary revascularisation guidance or deferral and for procedural optimisation of percutaneous coronary intervention (PCI) results. Despite accumulating evidence and positive guideline recommendations, the adoption of invasive physiology has remained quite low, mainly due to technical and economic issues as well as to operator-resistance to change. Coronary image-based computational physiology has been recently developed, with promising results in terms of accuracy and a reduction in computational time, costs, radiation exposure and risks for the patient. Lastly, the integration of intracoronary imaging and physiology allows for individualised PCI treatment, aiming at complete relief of ischaemia through optimised morpho-functional immediate procedural results. Instead of a conventional state-of-the-art review, this A to Z dictionary attempts to provide a practical guide for the application of coronary physiology in the catheterisation laboratory, exploring several methods, their pitfalls, and useful tips and tricks.

Radial wall strain: a novel angiographic measure of plaque composition and vulnerability

BACKGROUND

The lipid-to-cap ratio (LCR) and thin-cap fibroatheroma (TCFA) derived from optical coherence tomography (OCT) are indicative of plaque vulnerability.

AIMS

We aimed to explore the association of a novel method to estimate radial wall strain (RWS) from angiography with plaque composition and features of vulnerability assessed by OCT.

METHODS

Anonymised data from patients with intermediate stenosis who underwent coronary angiography (CAG) and OCT were analysed in a core laboratory. Angiography-derived RWSmax was computed as the maximum deformation of lumen diameter throughout the cardiac cycle, expressed as a percentage of the largest lumen diameter. The LCR and TCFA were automatically determined on OCT images by a recently validated algorithm based on artificial intelligence.

RESULTS

OCT and CAG images from 114 patients (124 vessels) were analysed. The average time for the analysis of RWSmax was 57 (39-82) seconds. The RWSmax in the interrogated plaques was 12% (10-15%) and correlated positively with the LCR (r=0.584; p<0.001) and lipidic plaque burden (r=0.411; p<0.001), and negatively with fibrous cap thickness (r= -0.439; p<0.001). An RWSmax >12% was an angiographic predictor for an LCR >0.33 (area under the curve [AUC]=0.86, 95% confidence interval [CI]: 0.78-0.91; p<0.001) and TCFA (AUC=0.72, 95% CI: 0.63-0.80; p<0.001). Lesions with RWSmax >12% had a higher prevalence of TCFA (22.0% versus 1.5%; p<0.001), thinner fibrous cap thickness (71 μm versus 101 μm; p<0.001), larger lipidic plaque burden (23.3% versus 15.4%; p<0.001), and higher maximum LCR (0.41 versus 0.18; p<0.001) compared to lesions with RWSmax ≤12%.

CONCLUSIONS

Angiography-derived RWS was significantly correlated with plaque composition and known OCT features of plaque vulnerability in patients with intermediate coronary stenosis.

Morphometric Assessment for Functional Evaluation of Coronary Stenosis with Optical Coherence Tomography and the Optical Flow Ratio in a Vessel with Single Stenosis

Objectives: The study aimed to evaluate the diagnostic performance of optical coherence tomography (OCT) in identifying functionally significant coronary stenosis in a vessel with single stenosis. Background: The OCT-based morphofunctional computational method for deriving the optical flow ratio (OFR) has diagnostic value, as it can identify the functional severity of coronary stenosis, but the ability of the OFR to aid the OCT in determining coronary stenosis hemodynamics in single-stenosis lesion remains unclear. Methods: 74 vessels with single stenosis were studied in 69 patients; all cases were performed through OCT and quantitative flow ratio (QFR), and OCT images were used to perform OFR. Results: Among vessels with single stenosis, OFR showed a good correlation with QFR (r = 0.86; p < 0.001). Taking QFR as the standard, the vessel-level diagnosis accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of OFR were 90% (95% CI: 81 to 96), 94% (95% CI: 77 to 99), 88% (95% CI: 74 to 96), 85% (95% CI: 68 to 94) and 95% (95% CI: 82 to 99), respectively. Among vessels with OFR/QFR concordance, both the minimum lumen area (MLA) and minimum lumen diameter (MLD) showed excellent diagnostic efficiency (MLA: area under the curve (AUC) = 0.92, 95% CI: 0.85 to 0.98, p < 0.001; MLD: AUC = 0.93, 95% CI: 0.86 to 0.98, p < 0.001) in determining the functional significance of coronary stenosis in a single stenosis lesion, and the best cutoff values were 1.55 mm2 and 1.40 mm. Conclusions: OFR has a good correlation with QFR. OCT-measured MLA and MLD have excellent diagnostic efficiency in identifying the hemodynamic significance of coronary stenosis in a vessel with single stenosis.

Coronary functional assessment in non-obstructive coronary artery disease: Present situation and future direction

Non-obstructive coronary artery disease (CAD), which is defined as coronary stenosis <50%, has been increasingly recognized as an emerging entity in clinical practice. Vasomotion abnormality and coronary microvascular dysfunction are two major mechanisms contributing to the occur of angina with non-obstructive CAD. Although routine coronary functional assessment is limited due to several disadvantages, functional evaluation can help to understand the pathophysiological mechanism and/or to exclude specific etiologies. In this review, we summarized the potential mechanisms involved in ischemia with non-obstructive coronary arteries (INOCA) and myocardial infarction with non-obstructive coronary arteries (MINOCA), the two major form of non-obstructive CAD. Additionally, we reviewed currently available functional assessment indices and their use in non-obstructive CAD. Furthermore, we speculated that novel technique combined anatomic and physiologic parameters might provide more individualized therapeutic choice for patients with non-obstructive CAD.

Risk Stratification in Acute Coronary Syndrome by Comprehensive Morphofunctional Assessment With Optical Coherence Tomography

Background

Artificial intelligence enables simultaneous evaluation of plaque morphology and computational physiology from optical coherence tomography (OCT).

Objectives

This study sought to appraise the predictive value of major adverse cardiovascular events (MACE) by combined plaque morphology and computational physiology.

Methods

A total of 604 patients with acute coronary syndrome who underwent OCT imaging in ≥1 nonculprit vessel during index coronary angiography were retrospectively enrolled. A novel morphologic index, named the lipid-to-cap ratio (LCR), and a functional parameter to evaluate the physiologic significance of coronary stenosis from OCT, namely, the optical flow ratio (OFR), were calculated from OCT, together with classical morphologic parameters, like thin-cap fibroatheroma (TCFA) and minimal lumen area.

Results

The 2-year cumulative incidence of a composite of nonculprit vessel–related cardiac death, cardiac arrest, acute myocardial infarction, and ischemia-driven revascularization (NCV-MACE) at 2 years was 4.3%. Both LCR (area under the curve [AUC]: 0.826; 95% CI: 0.793-0.855) and OFR (AUC: 0.838; 95% CI: 0.806-0.866) were superior to minimal lumen area (AUC: 0.618; 95% CI: 0.578-0.657) in predicting NCV-MACE at 2 years. Patients with both an LCR of >0.33 and an OFR of ≤0.84 had significantly higher risk of NCV-MACE at 2 years than patients in whom at least 1 of these 2 parameters was normal (HR: 42.73; 95% CI: 12.80-142.60; P < 0.001). The combination of thin-cap fibroatheroma and OFR also identified patients at higher risk of future events (HR: 6.58; 95% CI: 2.83-15.33; P < 0.001).

Conclusions

The combination of LCR with OFR permits the identification of a subgroup of patients with 43-fold higher risk of recurrent cardiovascular events in the nonculprit vessels after acute coronary syndrome.

Towards a Deep-Learning Approach for Prediction of Fractional Flow Reserve from Optical Coherence Tomography

Cardiovascular disease (CVD) is the number one cause of death worldwide, and coronary artery disease (CAD) is the most prevalent CVD, accounting for 42% of these deaths. In view of the limitations of the anatomical evaluation of CAD, Fractional Flow Reserve (FFR) has been introduced as a functional diagnostic index. Herein, we evaluate the feasibility of using deep neural networks (DNN) in an ensemble approach to predict the invasively measured FFR from raw anatomical information that is extracted from optical coherence tomography (OCT). We evaluate the performance of various DNN architectures under different formulations: regression, classification—standard, and few-shot learning (FSL) on a dataset containing 102 intermediate lesions from 80 patients. The FSL approach that is based on a convolutional neural network leads to slightly better results compared to the standard classification: the per-lesion accuracy, sensitivity, and specificity were 77.5%, 72.9%, and 81.5%, respectively. However, since the 95% confidence intervals overlap, the differences are statistically not significant. The main findings of this study can be summarized as follows: (1) Deep-learning (DL)-based FFR prediction from reduced-order raw anatomical data is feasible in intermediate coronary artery lesions; (2) DL-based FFR prediction provides superior diagnostic performance compared to baseline approaches that are based on minimal lumen diameter and percentage diameter stenosis; and (3) the FFR prediction performance increases quasi-linearly with the dataset size, indicating that a larger train dataset will likely lead to superior diagnostic performance.