A novel balloon-expandable transcatheter aortic valve bioprosthesis: Myval and Myval Octacor.

INTRODUCTION: Over the past two decades, transcatheter aortic valve replacement (TAVR) has expanded its application across all surgical risk levels, including low-risk patients, where, due to longer life expectancy, reducing common pitfalls of TAVR is essential. To address these needs, many technological advancements have been developed. Myval and the new generation Myval Octacor (Meril Life Sciences Pvt. Ltd) are novel balloon-expandable (BE) transcatheter heart valve (THV) systems designed for the treatment of severe aortic stenosis. AREAS COVERED: This review aims to illustrate the design features of these novel THVs and the main evidence from available studies. Furthermore, we provide evidence of these THVs' performance in challenging scenarios such as extra-large aortic annuli, bicuspid aortic valves, and valve-in-valve/valve-in-ring procedures. EXPERT OPINION: Myval and Myval Octacor have demonstrated comparable early safety and clinical efficacy to the leading contemporary THVs, exhibiting remarkably low rates of moderate to severe paravalvular leak (PVL) and permanent pacemaker implantation (PPI). The wide range of sizes offered by the Myval family may minimize the risk of under-/oversizing, potentially explaining the lower rates of the aforementioned phenomena. Moreover, the presence of both internal skirt and external reinforced cuff may also explain the low rate of moderate to severe PVL.

OCT-Guided versus Angiography-Guided Coronary Stent Implantation in Complex Lesions: An ILUMIEN IV Substudy.

BACKGROUND: ILUMIEN IV was the first large-scale, multicenter, randomized trial comparing optical coherence tomography (OCT)-guided versus angiography-guided stent implantation in patients with high-risk clinical characteristics and/or complex angiographic lesions. OBJECTIVE: Here, we aimed to specifically examine outcomes in the complex angiographic lesions subgroup. METHODS: From the original trial population (n=2487), high-risk patients without complex angiographic lesions were excluded (n=514). Complex angiographic lesion characteristics included 1) long or multiple lesions with intended total stent length ≥28 mm; 2) bifurcation lesion with intended two-stent strategy; 3) severely calcified lesion; 4) chronic total occlusion; or 5) in-stent restenosis. The study endpoints were 1) final minimal stent area (MSA); 2) 2-year composite of serious major adverse cardiovascular events (MACE; cardiac death, target-vessel myocardial infarction (MI), or stent thrombosis); and 3) 2-year effectiveness, defined as target-vessel failure (TVF), a composite of cardiac death, target-vessel MI, or ischemia-driven target-vessel revascularization. RESULTS: The post-PCI MSA was larger in the OCT- (n=992) versus angiography-guided (n=981) group (5.56±1.95 versus 5.26±1.81mm2; difference, 0.30; 95% confidence interval [CI], 0.14-0.47; P<0.001). Compared with angiography-guided PCI, OCT-guided PCI resulted in a lower risk of serious MACE (3.1% versus 4.9%; hazard ratio [HR], 0.63; 95% CI, 0.40-0.99; P=0.04). TVF was not significantly different between groups (7.3% versus 8.8%; HR, 0.82; 95% CI, 0.59-1.12; P=0.20). CONCLUSIONS: In complex angiographic lesions, OCT-guided PCI led to a larger MSA and reduced the serious MACE composite of cardiac death, target-vessel MI, or stent thrombosis compared with angiography-guided PCI at 2 years, but did not significantly improve TVF.

Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images.

Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.

Comparing two-year outcomes of balloon-expandable Myval and self-expanding Evolut R in severe aortic valve stenosis.

BACKGROUND: The new balloon-expandable (BE) Myval transcatheter heart valves (THV) has shown promising early results with low paravalvular leak (PVL) and permanent pacemaker implantation (PPI) rates. Limited data are available regarding its long-term performance. We aimed to compare the 2-year clinical and echocardiographic outcomes of transcatheter aortic valve replacement (TAVR) using the self-expanding (SE) Evolut R and the BE Myval THVs. METHODS: The EVAL study included 166 patients with severe aortic valve stenosis who underwent TAVR either with SE Evolut R (n = 108) or BE Myval (n = 58) THV. Primary objectives include comparison on clinical efficacy (freedom from all-cause mortality, stroke, and cardiovascular hospitalization), echocardiographic performance and PPI rates between the two THVs. RESULTS: At 2-year the BE Myval group showed higher clinical efficacy (86% vs. 66%,HR:2.62, 95%CI 2.2-5.1;p = 0.006), with fewer cardiac hospitalizations (3.4% vs. 13.9%,p = 0.03). No significant differences in all-cause mortality, cardiovascular mortality, or stroke rates were observed. The proportion of patients with ≥moderate PVL was significantly lower in the BE Myval compared to the SE Evolut R group (4%vs. 22%,p = 0.008). The mean transvalvular gradient was significantly higher in the SE group compared to the BE group (9.5 ± 4.3 vs. 6.9 ± 2.2 mmHg,p < 0.001), however there was no difference in the percentage of patients with a mean gradient ≥20 mmHg between the two groups. CONCLUSIONS: Both THVs offer similar 2-year clinical outcomes. The BE Myval THV demonstrated advantages with higher clinical efficacy and lower PVL incidence. Longer follow-up and randomized trials are needed to validate these results and assess Myval's sustained performance and durability.

Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries.

Artificial intelligence (AI) is likely to revolutionize the way medical images are analysed and has the potential to improve the identification and analysis of vulnerable or high-risk atherosclerotic plaques in coronary arteries, leading to advances in the treatment of coronary artery disease. However, coronary plaque analysis is challenging owing to cardiac and respiratory motion, as well as the small size of cardiovascular structures. Moreover, the analysis of coronary imaging data is time-consuming, can be performed only by clinicians with dedicated cardiovascular imaging training, and is subject to considerable interreader and intrareader variability. AI has the potential to improve the assessment of images of vulnerable plaque in coronary arteries, but requires robust development, testing and validation. Combining human expertise with AI might facilitate the reliable and valid interpretation of images obtained using CT, MRI, PET, intravascular ultrasonography and optical coherence tomography. In this Roadmap, we review existing evidence on the application of AI to the imaging of vulnerable plaque in coronary arteries and provide consensus recommendations developed by an interdisciplinary group of experts on AI and non-invasive and invasive coronary imaging. We also outline future requirements of AI technology to address bias, uncertainty, explainability and generalizability, which are all essential for the acceptance of AI and its clinical utility in handling the anticipated growing volume of coronary imaging procedures.

Optical Coherence Tomography-Guided versus Angiography-Guided PCI.

BACKGROUND: Data regarding clinical outcomes after optical coherence tomography (OCT)-guided percutaneous coronary intervention (PCI) as compared with angiography-guided PCI are limited. METHODS: In this prospective, randomized, single-blind trial, we randomly assigned patients with medication-treated diabetes or complex coronary-artery lesions to undergo OCT-guided PCI or angiography-guided PCI. A final blinded OCT procedure was performed in patients in the angiography group. The two primary efficacy end points were the minimum stent area after PCI as assessed with OCT and target-vessel failure at 2 years, defined as a composite of death from cardiac causes, target-vessel myocardial infarction, or ischemia-driven target-vessel revascularization. Safety was also assessed. RESULTS: The trial was conducted at 80 sites in 18 countries. A total of 2487 patients underwent randomization: 1233 patients were assigned to undergo OCT-guided PCI, and 1254 to undergo angiography-guided PCI. The minimum stent area after PCI was 5.72±2.04 mm2 in the OCT group and 5.36±1.87 mm2 in the angiography group (mean difference, 0.36 mm2; 95% confidence interval [CI], 0.21 to 0.51; P<0.001). Target-vessel failure within 2 years occurred in 88 patients in the OCT group and in 99 patients in the angiography group (Kaplan-Meier estimates, 7.4% and 8.2%, respectively; hazard ratio, 0.90; 95% CI, 0.67 to 1.19; P = 0.45). OCT-related adverse events occurred in 1 patient in the OCT group and in 2 patients in the angiography group. Stent thrombosis within 2 years occurred in 6 patients (0.5%) in the OCT group and in 17 patients (1.4%) in the angiography group. CONCLUSIONS: Among patients undergoing PCI, OCT guidance resulted in a larger minimum stent area than angiography guidance, but there was no apparent between-group difference in the percentage of patients with target-vessel failure at 2 years. (Funded by Abbott; ILUMIEN IV: OPTIMAL PCI ClinicalTrials.gov number, NCT03507777.).

Percutaneous Treatment of Left Main Disease: A Review of Current Status.

Percutaneous treatment of the left main coronary artery is one of the most challenging scenarios in interventional cardiology, due to the large portion of myocardium at risk the technical complexity of treating a complex bifurcation with large branches. Our aim is to provide un updated overview of the current indications for percutaneous treatment of the left main, the different techniques and the rationale underlying the choice for provisional versus upfront two-stent strategies, intravascular imaging and physiology guidance in the management of left main disease, and the role of mechanical support devices in complex high-risk PCI.

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.

Drug-coated balloon combined with drug-eluting stent for the treatment of coronary bifurcation lesions: insights from the HYPER study.

True coronary bifurcation lesions (CBL) represent a challenging scenario for percutaneous coronary interventions (PCI), and are associated with a higher risk of target lesion failure (TLF), particularly when two stents are implanted. A hybrid strategy combining a drug-eluting stent (DES) in the main branch, and a drug-coated balloon in the side branch may improve outcomes by reducing the total stent length while maintaining an effective anti-prolipherative action. In this sub-study of the HYPER trial, 50 patients with true CBL were treated with a hybrid strategy: procedural success was 96%, one case of peri-procedural myocardial infarction and one case of TLF (in a DES-treated segment) at 1 year were reported. This study suggests that such a hybrid strategy may be a safe and effective option for true CBL PCI, and warrants additional investigations to compare outcomes with standard of care strategies.

Current Role of Intracoronary Imaging for Implementing Risk Stratification and Tailoring Culprit Lesion Treatment: A Narrative Review.

Our understanding of the pathophysiology of acute coronary syndrome and of the vascular biology of coronary atherosclerosis has made enormous progress with the implementation of intravascular imaging. Intravascular imaging contributes to overcoming the known limitations of coronary angiography and allows for the in vivo discrimination of plaque morphology giving insight into the underlying pathology of the disease process. The possibility of using intracoronary imaging to characterize lesion morphologies and correlate them with clinical presentations may influence the treatment of patients and improve risk stratification, offering the opportunity for tailored management. This review examines the current role of intravascular imaging and describes how intracoronary imaging represents a valuable tool for modern interventional cardiology in order to improve diagnostic accuracy and offer a tailored approach to the treatment of patients with coronary artery disease, especially in the acute setting.

Management strategies for heavily calcified coronary stenoses: an EAPCI clinical consensus statement in collaboration with the EURO4C-PCR group.

Since the publication of the 2015 EAPCI consensus on rotational atherectomy, the number of percutaneous coronary interventions (PCI) performed in patients with severely calcified coronary artery disease has grown substantially. This has been prompted on one side by the clinical demand for the continuous increase in life expectancy, the sustained expansion of the primary PCI networks worldwide, and the routine performance of revascularization procedures in elderly patients; on the other side, the availability of new and dedicated technologies such as orbital atherectomy and intravascular lithotripsy, as well as the optimization of the rotational atherectomy system, has increased operators' confidence in attempting more challenging PCI. This current EAPCI clinical consensus statement prepared in collaboration with the EURO4C-PCR group describes the comprehensive management of patients with heavily calcified coronary stenoses, starting with how to use non-invasive and invasive imaging to assess calcium burden and inform procedural planning. Objective and practical guidance is provided on the selection of the optimal interventional tool and technique based on the specific calcium morphology and anatomic location. Finally, the specific clinical implications of treating these patients are considered, including the prevention and management of complications and the importance of adequate training and education.

Clinical Characteristics and Prognosis of MINOCA Caused by Atherosclerotic and Nonatherosclerotic Mechanisms Assessed by OCT.

BACKGROUND: Myocardial infarction with nonobstructive coronary artery (MINOCA) is a heterogeneous syndrome caused by different pathophysiologic mechanisms. There is limited evidence regarding prognosis of patients with MINOCA caused by different mechanisms. OBJECTIVES: The present study aimed to assess the underlying mechanisms of MINOCA by optical coherence tomography (OCT) and to correlate with clinical outcomes. METHODS: Patients with MINOCA were divided into 2 groups based on OCT findings: atherosclerotic MINOCA (Ath-MINOCA) and nonatherosclerotic MINOCA (non-Ath-MINOCA). Major adverse cardiac events (MACE) were defined as cardiac death, nonfatal MI, target lesion revascularization, stroke, and rehospitalization for unstable or progressive angina. RESULTS: Among 7,423 patients with a clinical diagnosis of MI who underwent angiography, 190 of 294 MINOCA were studied using OCT. The causes of Ath-MINOCA (n = 99, 52.1%) were plaque erosion (n = 64, 33.7%), plaque rupture (n = 33, 17.4%), and calcified nodule (n = 2, 1.1%) whereas the causes of non-Ath-MINOCA (n = 91, 47.9%) were spontaneous coronary artery dissection (n = 8, 4.2%), coronary spasm (n = 9, 4.7%), and unclassified cause (n = 74, 38.9%). The 1-year MACE was 15.3% for Ath-MINOCA vs 4.5% for non-Ath-MINOCA (P = 0.015). An atherosclerotic cause was an independent predictor of MACE (HR: 5.36 [95% CI: 1.08-26.55]; P = 0.040), mainly driven by target lesion revascularization and rehospitalization, despite the composite endpoint including cardiac death and MI showing no difference. CONCLUSIONS: OCT identified a cause in 61.1% of MINOCA, in which Ath-MINOCA represents an important and distinct MINOCA subset. Ath-MINOCA were more common and associated with worse outcomes. (Incidence Rate of Heart Failure After Acute Myocardial Infarction With Optimal Treatment; NCT03297164; Paradigm Shift in the Treatment of Patients With ACS; NCT02041650).

Automatic microchannel detection using deep learning in intravascular optical coherence tomography images.

Microchannel formation is known to be a significant marker of plaque vulnerability, plaque rupture, and intraplaque hemorrhage, which are responsible for plaque progression. We developed a fully-automated method for detecting microchannels in intravascular optical coherence tomography (IVOCT) images using deep learning. A total of 3,075 IVOCT image frames across 41 patients having 62 microchannel segments were analyzed. Microchannel was manually annotated by expert cardiologists, according to previously established criteria. In order to improve segmentation performance, pre-processing including guidewire detection/removal, lumen segmentation, pixel-shifting, and noise filtering was applied to the raw (r,θ) IVOCT image. We used the DeepLab-v3 plus deep learning model with the Xception backbone network for identifying microchannel candidates. After microchannel candidate detection, each candidate was classified as either microchannel or no-microchannel using a convolutional neural network (CNN) classification model. Our method provided excellent segmentation of microchannel with a Dice coefficient of 0.811, sensitivity of 92.4%, and specificity of 99.9%. We found that pre-processing and data augmentation were very important to improve results. In addition, a CNN classification step was also helpful to rule out false positives. Furthermore, automated analysis missed only 3% of frames having microchannels and showed no false positives. Our method has great potential to enable highly automated, objective, repeatable, and comprehensive evaluations of vulnerable plaques and treatments. We believe that this method is promising for both research and clinical applications.

Automated analysis of fibrous cap in intravascular optical coherence tomography images of coronary arteries.

Thin-cap fibroatheroma (TCFA) and plaque rupture have been recognized as the most frequent risk factor for thrombosis and acute coronary syndrome. Intravascular optical coherence tomography (IVOCT) can identify TCFA and assess cap thickness, which provides an opportunity to assess plaque vulnerability. We developed an automated method that can detect lipidous plaque and assess fibrous cap thickness in IVOCT images. This study analyzed a total of 4360 IVOCT image frames of 77 lesions among 41 patients. Expert cardiologists manually labeled lipidous plaque based on established criteria. To improve segmentation performance, preprocessing included lumen segmentation, pixel-shifting, and noise filtering on the raw polar (r, θ) IVOCT images. We used the DeepLab-v3 plus deep learning model to classify lipidous plaque pixels. After lipid detection, we automatically detected the outer border of the fibrous cap using a special dynamic programming algorithm and assessed the cap thickness. Our method provided excellent discriminability of lipid plaque with a sensitivity of 85.8% and A-line Dice coefficient of 0.837. By comparing lipid angle measurements between two analysts following editing of our automated software, we found good agreement by Bland-Altman analysis (difference 6.7° ± 17°; mean ~ 196°). Our method accurately detected the fibrous cap from the detected lipid plaque. Automated analysis required a significant modification for only 5.5% frames. Furthermore, our method showed a good agreement of fibrous cap thickness between two analysts with Bland-Altman analysis (4.2 ± 14.6 µm; mean ~ 175 µm), indicating little bias between users and good reproducibility of the measurement. We developed a fully automated method for fibrous cap quantification in IVOCT images, resulting in good agreement with determinations by analysts. The method has great potential to enable highly automated, repeatable, and comprehensive evaluations of TCFAs.

Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning.

Microvessels in vascular plaque are associated with plaque progression and are found in plaque rupture and intra-plaque hemorrhage. To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was performed using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,θ) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71 ± 0.10 and pixel-wise sensitivity/specificity of 87.7 ± 6.6%/99.8 ± 0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5 ± 0.3%, specificity of 98.8 ± 1.0%, and accuracy of 99.1 ± 0.5%. The classification step eliminated the majority of residual false positives and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared with 730 from manual analysis, representing a 4.4% difference. When compared with the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.