Novel near-infrared spectroscopy-intravascular ultrasound-based deep-learning methodology for accurate coronary computed tomography plaque quantification and characterization.

Aims: Coronary computed tomography angiography (CCTA) is inferior to intravascular imaging in detecting plaque morphology and quantifying plaque burden. We aim to, for the first time, train a deep-learning (DL) methodology for accurate plaque quantification and characterization in CCTA using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). Methods and results: Seventy patients were prospectively recruited who underwent CCTA and NIRS-IVUS imaging. Corresponding cross sections were matched using an in-house developed software, and the estimations of NIRS-IVUS for the lumen, vessel wall borders, and plaque composition were used to train a convolutional neural network in 138 vessels. The performance was evaluated in 48 vessels and compared against the estimations of NIRS-IVUS and the conventional CCTA expert analysis. Sixty-four patients (186 vessels, 22 012 matched cross sections) were included. Deep-learning methodology provided estimations that were closer to NIRS-IVUS compared with the conventional approach for the total atheroma volume (ΔDL-NIRS-IVUS: -37.8 ± 89.0 vs. ΔConv-NIRS-IVUS: 243.3 ± 183.7 mm3, variance ratio: 4.262, P < 0.001) and percentage atheroma volume (-3.34 ± 5.77 vs. 17.20 ± 7.20%, variance ratio: 1.578, P < 0.001). The DL methodology detected lesions more accurately than the conventional approach (Area under the curve (AUC): 0.77 vs. 0.67, P < 0.001) and quantified minimum lumen area (ΔDL-NIRS-IVUS: -0.35 ± 1.81 vs. ΔConv-NIRS-IVUS: 1.37 ± 2.32 mm2, variance ratio: 1.634, P < 0.001), maximum plaque burden (4.33 ± 11.83% vs. 5.77 ± 16.58%, variance ratio: 2.071, P = 0.004), and calcific burden (-51.2 ± 115.1 vs. -54.3 ± 144.4, variance ratio: 2.308, P < 0.001) more accurately than conventional approach. The DL methodology was able to segment a vessel on CCTA in 0.3 s. Conclusions: The DL methodology developed for CCTA analysis from co-registered NIRS-IVUS and CCTA data enables rapid and accurate assessment of lesion morphology and is superior to expert analysts (Clinicaltrials.gov: NCT03556644).

CARDIAN: a novel computational approach for real-time end-diastolic frame detection in intravascular ultrasound using bidirectional attention networks.

Introduction: Changes in coronary artery luminal dimensions during the cardiac cycle can impact the accurate quantification of volumetric analyses in intravascular ultrasound (IVUS) image studies. Accurate ED-frame detection is pivotal for guiding interventional decisions, optimizing therapeutic interventions, and ensuring standardized volumetric analysis in research studies. Images acquired at different phases of the cardiac cycle may also lead to inaccurate quantification of atheroma volume due to the longitudinal motion of the catheter in relation to the vessel. As IVUS images are acquired throughout the cardiac cycle, end-diastolic frames are typically identified retrospectively by human analysts to minimize motion artefacts and enable more accurate and reproducible volumetric analysis. Methods: In this paper, a novel neural network-based approach for accurate end-diastolic frame detection in IVUS sequences is proposed, trained using electrocardiogram (ECG) signals acquired synchronously during IVUS acquisition. The framework integrates dedicated motion encoders and a bidirectional attention recurrent network (BARNet) with a temporal difference encoder to extract frame-by-frame motion features corresponding to the phases of the cardiac cycle. In addition, a spatiotemporal rotation encoder is included to capture the IVUS catheter's rotational movement with respect to the coronary artery. Results: With a prediction tolerance range of 66.7 ms, the proposed approach was able to find 71.9%, 67.8%, and 69.9% of end-diastolic frames in the left anterior descending, left circumflex and right coronary arteries, respectively, when tested against ECG estimations. When the result was compared with two expert analysts' estimation, the approach achieved a superior performance. Discussion: These findings indicate that the developed methodology is accurate and fully reproducible and therefore it should be preferred over experts for end-diastolic frame detection in IVUS sequences.

POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation.

Intravascular ultrasound (IVUS) is recommended in guiding coronary intervention. The segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS images is a key step, but the manual process is time-consuming and error-prone, and suffers from inter-observer variability. In this paper, we propose a novel perceptual organisation-aware selective transformer framework that can achieve accurate and robust segmentation of the vessel walls in IVUS images. In this framework, temporal context-based feature encoders extract efficient motion features of vessels. Then, a perceptual organisation-aware selective transformer module is proposed to extract accurate boundary information, supervised by a dedicated boundary loss. The obtained EEM and lumen segmentation results will be fused in a temporal constraining and fusion module, to determine the most likely correct boundaries with robustness to morphology. Our proposed methods are extensively evaluated in non-selected IVUS sequences, including normal, bifurcated, and calcified vessels with shadow artifacts. The results show that the proposed methods outperform the state-of-the-art, with a Jaccard measure of 0.92 for lumen and 0.94 for EEM on the IVUS 2011 open challenge dataset. This work has been integrated into a software QCU-CMS2 to automatically segment IVUS images in a user-friendly environment.

Implications of computed tomography reconstruction algorithms on coronary atheroma quantification: Comparison with intravascular ultrasound.

BACKGROUND: Advances in coronary computed tomography angiography (CCTA) reconstruction algorithms are expected to enhance the accuracy of CCTA plaque quantification. We aim to evaluate different CCTA reconstruction approaches in assessing vessel characteristics in coronary atheroma using intravascular ultrasound (IVUS) as the reference standard. METHODS: Matched cross-sections (n â€‹= â€‹7241) from 50 vessels in 15 participants with chronic coronary syndrome who prospectively underwent CCTA and 3-vessel near-infrared spectroscopy-IVUS were included. Twelve CCTA datasets per patient were reconstructed using two different kernels, two slice thicknesses (0.75 â€‹mm and 0.50 â€‹mm) and three different strengths of advanced model-based iterative reconstruction (IR) algorithms. Lumen and vessel wall borders were manually annotated in every IVUS and CCTA cross-section which were co-registered using dedicated software. Image quality was sub-optimal in the reconstructions with a sharper kernel, so these were excluded. Intraclass correlation coefficient (ICC) and repeatability coefficient (RC) were used to compare the estimations of the 6 CT reconstruction approaches with those derived by IVUS. RESULTS: Segment-level analysis showed good agreement between CCTA and IVUS for assessing atheroma volume with approach 0.50/5 (slice thickness 0.50 â€‹mm and highest strength 5 ADMIRE IR) being the best (total atheroma volume ICC: 0.91, RC: 0.67, p â€‹< â€‹0.001 and percentage atheroma volume ICC: 0.64, RC: 14.06, p â€‹< â€‹0.001). At lesion-level, there was no difference between the CCTA reconstructions for detecting plaques (accuracy range: 0.64-0.67; p â€‹= â€‹0.23); however, approach 0.50/5 was superior in assessing IVUS-derived lesion characteristics associated with plaque vulnerability (minimum lumen area ICC: 0.64, RC: 1.31, p â€‹< â€‹0.001 and plaque burden ICC: 0.45, RC: 32.0, p â€‹< â€‹0.001). CONCLUSION: CCTA reconstruction with thinner slice thickness, smooth kernel and highest strength advanced IR enabled more accurate quantification of the lumen and plaque at a segment-, and lesion-level analysis in coronary atheroma when validated against intravascular ultrasound. CLINICALTRIALS: gov (NCT03556644).

Intravascular Imaging for Guiding In-Stent Restenosis and Stent Thrombosis Therapy.

Advances in stent technology and the design of endovascular devices with thinner struts, anti-inflammatory and antithrombotic polymers, and better drug kinetics have enhanced the safety and efficacy of the second-generation drug-eluting stents and broadened their use in the therapy of high-risk patients and complex anatomies. However, despite these developments, in-stent restenosis and stent thrombosis remain the Achilles' heel of percutaneous coronary intervention, with their cumulative incidence reaching up to 10% at 5 years following percutaneous coronary intervention. The treatment of stent failure poses challenges and is associated with a worse prognosis than conventional percutaneous coronary intervention. Several studies have recently highlighted the value of intravascular imaging in identifying causes of stent failure, underscored its role in treatment planning, and registries have shown that its use may be associated with better clinical outcomes. The present review aims to summarize the evidence in the field; it discusses the value of intravascular imaging in identifying the mechanisms of in-stent restenosis and stent thrombosis in assessing the morphological characteristics of neointima tissue that appears to determine long-term outcomes in evaluating procedural results, and presents the findings of studies supporting its value in guiding therapy in stent failure.

Treatment Strategies and Outcomes of Emergency Left Main Percutaneous Coronary Intervention.

Emergency percutaneous coronary intervention of the left main (LM ePCI) coronary artery necessitated by acute coronary syndrome is associated with a high risk of mortality. However, optimal treatment strategies and related outcomes remain undefined in this group. We undertook a multi-center, retrospective, observational cohort study of consecutive patients requiring LM ePCI between 2011 and 2018 and reported the coronary anatomy, treatment strategies, outcomes, and predictors of mortality. A total of 116 consecutive cases were included. Patients were predominantly male (85%) with a median age of 68.0 years; 12 patients (10%) had previous coronary artery bypass grafting. ST-elevation was noted in 76 (66%); 30 (26%) presented with an out-of-hospital cardiac arrest (OOHCA) and 47 (41%) with cardiogenic shock. The most frequent pattern of disease was Medina 1,1,1, seen in 59 patients (51%). The commonest revascularization strategy was provisional stenting (95 cases, 82%) with improved or thrombolysis in myocardial infarction 3 flow seen in 85 cases (73%). All-cause mortality was 35% at 30 days, rising to 58% at 5 years. Adverse predictors of 30-day mortality included presentation with cardiogenic shock (p = 0.018) and OOHCA (p = 0.020), whereas improved flow and/or thrombolysis in myocardial infarction 3 flow in both circumflex and left anterior descending artery afforded a better prognosis (p = 0.028). In conclusion, patients who underwent LM ePCI are a high-risk subgroup and commonly present with cardiogenic shock and OOHCA. Provisional stenting appears to be the preferred option with the successful restoration of coronary flow in most cases despite complex anatomy. High 30-day mortality is driven by the presence of cardiogenic shock, OOHCA, and failure to restore or improve coronary flow.

Machine learning for atherosclerotic tissue component classification in combined near-infrared spectroscopy intravascular ultrasound imaging: Validation against histology.

BACKGROUND AND AIMS: Accurate classification of plaque composition is essential for treatment planning. Intravascular ultrasound (IVUS) has limited efficacy in assessing tissue types, while near-infrared spectroscopy (NIRS) provides complementary information to IVUS but lacks depth information. The aim of this study is to train and assess the efficacy of a machine learning classifier for plaque component classification that relies on IVUS echogenicity and NIRS-signal, using histology as reference standard. METHODS: Matched NIRS-IVUS and histology images from 15 cadaveric human coronary arteries were analyzed (10 vessels were used for training and 5 for testing). Fibrous/pathological intimal thickening (F-PIT), early necrotic core (ENC), late necrotic core (LNC), and calcific tissue regions-of-interest were detected on histology and superimposed onto IVUS frames. The pixel intensities of these tissue types from the training set were used to train a J48 classifier for plaque characterization (ECHO-classification). To aid differentiation of F-PIT from necrotic cores, the NIRS-signal was used to classify non-calcific pixels outside yellow-spot regions as F-PIT (ECHO-NIRS classification). The performance of ECHO and ECHO-NIRS classifications were validated against histology. RESULTS: 262 matched frames were included in the analysis (162 constituted the training set and 100 the test set). The pixel intensities of F-PIT and ENC were similar and thus these two tissues could not be differentiated by echogenicity. With ENC and LNC as a single class, ECHO-classification showed good agreement with histology for detecting calcific and F-PIT tissues but had poor efficacy for necrotic cores (recall 0.59 and precision 0.29). Similar results were found when F-PIT and ENC were treated as a single class (recall and precision for LNC 0.78 and 0.33, respectively). ECHO-NIRS classification improved necrotic core and LNC detection, resulting in an increase of the overall accuracy of both models, from 81.4% to 91.8%, and from 87.9% to 94.7%, respectively. Comparable performance of the two models was seen in the test set where the overall accuracy of ECHO-NIRS classification was 95.0% and 95.5%, respectively. CONCLUSIONS: The combination of echogenicity with NIRS-signal appears capable of overcoming limitations of echogenicity, enabling more accurate characterization of plaque components.

End-diastolic segmentation of intravascular ultrasound images enables more reproducible volumetric analysis of atheroma burden.

BACKGROUND: Volumetric intravascular ultrasound (IVUS) analysis is currently performed at a fixed frame interval, neglecting the cyclic changes in vessel dimensions occurring during the cardiac cycle that can affect the reproducibility of the results. Analysis of end-diastolic (ED) IVUS frames has been proposed to overcome this limitation. However, at present, there is lack of data to support its superiority over conventional IVUS. OBJECTIVES: The present study aims to compare the reproducibility of IVUS volumetric analysis performed at a fixed frame interval and at the ED frames, identified retrospectively using a novel deep-learning methodology. METHODS: IVUS data acquired from 97 vessels were included in the present study; each vessel was segmented at 1 mm interval (conventional approach) and at ED frame twice by an expert analyst. Reproducibility was tested for the following metrics; normalized lumen, vessel and total atheroma volume (TAV), and percent atheroma volume (PAV). RESULTS: The mean length of the analyzed segments was 50.0 ± 24.1 mm. ED analysis was more reproducible than the conventional analysis for the normalized lumen (mean difference: 0.76 ± 4.03 mm3 vs. 1.72 ± 11.37 mm3 ; p for the variance of differences ratio < 0.001), vessel (0.30 ± 1.79 mm3 vs. -0.47 ± 10.26 mm3 ; p < 0.001), TAV (-0.46 ± 4.03 mm3 vs. -2.19 ± 14.39 mm3 ; p < 0.001) and PAV (-0.12 ± 0.59% vs. -0.34 ± 1.34%; p < 0.001). Results were similar when the analysis focused on the 10 mm most diseased segment. The superiority of the ED approach was due to a more reproducible detection of the segment of interest and to the fact that it was not susceptible to the longitudinal motion of the IVUS probe and the cyclic changes in vessel dimensions during the cardiac cycle. CONCLUSIONS: ED IVUS segmentation enables more reproducible volumetric analysis and quantification of TAV and PAV that are established end points in longitudinal studies assessing the efficacy of novel pharmacotherapies. Therefore, it should be preferred over conventional IVUS analysis as its higher reproducibility is expected to have an impact on the sample size calculation for the primary end point.

Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images.

AIMS: The aim of this study is to develop and validate a deep learning (DL) methodology capable of automated and accurate segmentation of intravascular ultrasound (IVUS) image sequences in real-time. METHODS AND RESULTS: IVUS segmentation was performed by two experts who manually annotated the external elastic membrane (EEM) and lumen borders in the end-diastolic frames of 197 IVUS sequences portraying the native coronary arteries of 65 patients. The IVUS sequences of 177 randomly-selected vessels were used to train and optimise a novel DL model for the segmentation of IVUS images. Validation of the developed methodology was performed in 20 vessels using the estimations of two expert analysts as the reference standard. The mean difference for the EEM, lumen and plaque area between the DL-methodology and the analysts was ≤0.23mm2 (standard deviation ≤0.85mm2), while the Hausdorff and mean distance differences for the EEM and lumen borders was ≤0.19 mm (standard deviation≤0.17 mm). The agreement between DL and experts was similar to experts' agreement (Williams Index ranges: 0.754-1.061) with similar results in frames portraying calcific plaques or side branches. CONCLUSIONS: The developed DL-methodology appears accurate and capable of segmenting high-resolution real-world IVUS datasets. These features are expected to facilitate its broad adoption and enhance the applications of IVUS in clinical practice and research.

Computerized technologies informing cardiac catheterization and guiding coronary intervention.

Advances in image processing and computer hardware have enabled the development of user-friendly software which operate in real-time and can be used in the catheterization laboratory to facilitate percutaneous coronary intervention (PCI). The two dimensional-(2D) quantitative coronary angiography (QCA) systems that have traditionally been used to assess lesion severity have been replaced by 3D-QCA systems, enabling more reliable evaluation of vessel geometry and lesion dimensions. This also allows 3D reconstruction of coronary bifurcation anatomy and generation of models that can be processed by computational fluid dynamic techniques to reliably detect flow-limiting lesions. More recently, software has been introduced that has the capability of generating a digital silhouette of the coronary arteries superimposed onto X-ray angiography to facilitate wire crossing and stent placement, and potentially reduce contrast use. In parallel, methodologies have been developed that operate with an accessible interface and can process intravascular imaging data, reliably quantify lesion severity and co-register intravascular and X-ray angiographic data to comprehensively assess plaque distribution and guide PCI. The above advances are used in daily practice to improve procedural results and outcomes. This review aims to provide an overview of the developments in the field - it presents the computer-based technologies that have been designed to accurately assess lesion severity, summarizes the advantages and limitations of the systems introduced to co-register imaging data and discusses the potential value of the existing and emerging software in the catheterization laboratory.

Multi-modality intravascular imaging for guiding coronary intervention and assessing coronary atheroma: the Novasight Hybrid IVUS-OCT system.

Intravascular imaging has evolved alongside interventional cardiology as an adjunctive tool for assessing plaque pathology and for guiding and optimizing percutaneous coronary intervention (PCI) in challenging lesions. The two modalities which have dominated the field are intravascular ultrasound (IVUS), which relies on sound waves and optical coherence tomography (OCT), relying on light waves. These approaches however have limited efficacy in assessing plaque morphology and vulnerability that are essential for guiding PCI in complex lesions and identifying patient at risk that will benefit from emerging therapies targeting plaque evolution. These limitations are complementary and, in this context, it has been recognized and demonstrated in multi-modality studies that the concurrent use of IVUS and OCT can help overcome these deficits enabling a more complete and accurate plaque assessment. The Conavi Novasight Hybrid IVUS-OCT catheter is the first commercially available device that is capable of invasive clinical coronary assessment with simultaneously acquired and co-registered IVUS and OCT imaging. It represents a significant evolution in the field and is expected to have broad application in clinical practice and research. In this review article we present the limitations of standalone intravascular imaging techniques, summarize the data supporting the value of multimodality imaging in clinical practice and research, describe the Novasight Hybrid IVUS-OCT system and highlight the potential utility of this technology in coronary intervention and in the study of atherosclerosis.

Wall shear stress estimated by 3D-QCA can predict cardiovascular events in lesions with borderline negative fractional flow reserve.

BACKGROUND AND AIMS: There is some evidence of the implications of wall shear stress (WSS) derived from three-dimensional quantitative coronary angiography (3D-QCA) models in predicting adverse cardiovascular events. This study investigates the efficacy of 3D-QCA-derived WSS in detecting lesions with a borderline negative fractional flow reserve (FFR: 0.81-0.85) that progressed and caused events. METHODS: In this retrospective cohort study, we identified 548 patients who had at least one lesion with an FFR 0.81-0.85 and complete follow-up data; 293 lesions (286 patients) with suitable angiographic characteristics were reconstructed using a dedicated 3D-QCA software and included in the analysis. In the reconstructed models blood flow simulation was performed and the value of 3D-QCA variables and WSS distribution in predicting events was examined. The primary endpoint of the study was the composite of cardiac death, target lesion related myocardial infarction or clinically indicated target lesion revascularization. RESULTS: During a median follow-up of 49.4 months, 37 events were reported. Culprit lesions had a greater area stenosis [(AS), 66.1% (59.5-72.3) vs 54.8% (46.5-63.2), p<0.001], smaller minimum lumen area [(MLA), 1.66 mm2 (1.45-2.30) vs 2.10 mm2 (1.69-2.70), p=0.011] and higher maximum WSS [9.0 Pa (5.10-12.46) vs 5.0 Pa (3.37-7.54), p < 0.001] than those that remained quiescent. In multivariable analysis, AS [hazard ratio (HR): 1.06, 95% confidence interval (CI): 1.03-1.10, p=0.001] and maximum WSS (HR: 1.08, 95% CI: 1.02-1.14, p=0.012) were the only independent predictors of the primary endpoint. Lesions with an increased AS (≥58.6%) that were exposed to high WSS (≥7.69Pa) were more likely to progress and cause events (27.8%) than those with a low AS exposed to high WSS (7.4%) or those exposed to low WSS that had increased (12.8%) or low AS (2.7%, p<0.001). CONCLUSIONS: This study for the first time highlights the potential value of 3D-QCA-derived WSS in detecting, among lesions with a borderline negative FFR, those that cause cardiovascular events.

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have failed to dominate in research. We developed a novel deep learning (DL)-methodology for end-diastolic frame detection in IVUS and compared its efficacy against expert analysts and a previously established methodology using electrocardiographic (ECG)-estimations as reference standard. Near-infrared spectroscopy-IVUS (NIRS-IVUS) data were prospectively acquired from 20 coronary arteries and co-registered with the concurrent ECG-signal to identify end-diastolic frames. A DL-methodology which takes advantage of changes in intensity of corresponding pixels in consecutive NIRS-IVUS frames and consists of a network model designed in a bidirectional gated-recurrent-unit (Bi-GRU) structure was trained to detect end-diastolic frames. The efficacy of the DL-methodology in identifying end-diastolic frames was compared with two expert analysts and a conventional image-based (CIB)-methodology that relies on detecting vessel movement to estimate phases of the cardiac cycle. A window of ± 100 ms from the ECG estimations was used to define accurate end-diastolic frames detection. The ECG-signal identified 3,167 end-diastolic frames. The mean difference between DL and ECG estimations was 3 ± 112 ms while the mean differences between the 1st-analyst and ECG, 2nd-analyst and ECG and CIB-methodology and ECG were 86 ± 192 ms, 78 ± 183 ms and 59 ± 207 ms, respectively. The DL-methodology was able to accurately detect 80.4%, while the two analysts and the CIB-methodology detected 39.0%, 43.4% and 42.8% of end-diastolic frames, respectively (P < 0.05). The DL-methodology can identify NIRS-IVUS end-diastolic frames accurately and should be preferred over expert analysts and CIB-methodologies, which have limited efficacy.

Diagnostic accuracy of quantitative flow ratio (QFR) and vessel fractional flow reserve (vFFR) estimated retrospectively by conventional radiation saving X-ray angiography.

BACKGROUND: Angiography derived FFR reveals good performance in assessing intermediate coronary stenosis. However, its performance under contemporary low X-ray frame and pulse rate settings is unknown. We aim to validate the feasibility and performance of quantitative flow ratio (QFR) and vessel fractional flow reserve (vFFR) under such angiograms. METHODS: This was an observational, retrospective, single center cohort study. 134 vessels in 102 patients, with angiograms acquired under 7.5fps and 7pps mode, were enrolled. QFR (fQFR and cQFR) and vFFR were validated with FFR as the gold standard. A conventional manual and a newly developed algorithmic exclusion method (M and A group) were both evaluated for identification of poor-quality angiograms. RESULTS: Good agreement between QFR/vFFR and FFR were observed in both M and A group, except for vFFR in the M group. The correlation coefficients between fQFR/cQFR/vFFR and FFR were 0.6242, 0.5888, 0.4089 in the M group, with rvFFR significantly lower than rfQFR (p = 0.0303), and 0.7055, 0.6793, 0.5664 in the A group, respectively. AUCs of detecting lesions with FFR ≤ 0.80 were 0.852 (95% CI 0.722-0.913), 0.858 (95% CI 0.778-0.917), 0.682 (95% CI 0.586-0.768), for fQFR/cQFR/vFFR in the M group, while vFFR performed poorer than fQFR (p = 0.0063) and cQFR (p = 0.0054). AUCs were 0.898 (95% CI 0.811-0.945), 0.892 (95% CI 0.803-0.949), 0.843 (95% CI 0.746-0.914) for fQFR/cQFR/vFFR in the A group. AUCvFFR was significantly higher in the A group than that in the M group (p = 0.0399). CONCLUSIONS: QFR/vFFR assessment is feasible under 7.5fps and 7pps angiography, where cQFR showed no advantage compared to fQFR. Our newly developed algorithmic exclusion method could be a better method of selecting angiograms with adequate quality for angiography derived FFR assessment.

Iatrogenic catheter-induced ostial coronary artery dissections: Prevalence, management, and mortality from a cohort of 55,968 patients over 10 years.

OBJECTIVE: We sought to describe the prevalence, management strategies and evaluate the prognosis of patients with iatrogenic catheter-induced ostial coronary artery dissection (ICOCAD). BACKGROUND: ICOCAD is a rare but potentially devastating complication of cardiac catheterisation. The clinical manifestations of ICOCAD vary from asymptomatic angiographic findings to abrupt vessel closure leading to myocardial infarction and death. METHODS: 55,968 patients who underwent coronary angiography over a 10-year period were screened for ICOCAD as defined by the National Heart, Lung, and Blood Institute. The management and all-cause mortality were retrieved from local and national databases. RESULTS: The overall prevalence of ICOCAD was 0.09% (51/55,968 patients). Guide catheters accounted for 75% (n = 37) of cases. Half of the ICOCAD cases involved the right coronary artery while the remaining were related to left main stem (23/51; 45%) and left internal mammary artery (2/51; 4%). Two-thirds of ICOCAD were high grade (type D, E, and F). The majority of cases were type F dissections (n = 18; 66%), of which two third occurred in females in their 60s. The majority of ICOCAD patients (42/51; 82%) were treated with percutaneous coronary intervention while the remaining underwent coronary artery bypass grafting (3/51; 6%) or managed conservatively (6/51; 12%). Three deaths occurred during the index admission while 48/51 patients (94.1%) were safely discharged without further mortality over a median follow-up of 3.6 years. CONCLUSIONS: ICOCAD is a rare but life-threatening complication of coronary angiography. Timely recognition and prompt bailout PCI is a safe option for majority of patients with good clinical outcomes.

Computerised Methodologies for Non-Invasive Angiography-Derived Fractional Flow Reserve Assessment: A Critical Review.

Fractional flow reserve is the gold standard for assessing the haemodynamic significance of intermediate coronary artery stenoses. Cumulative evidence has shown that FFR-guided revascularisation reduces stent implantations and improves patient outcomes. However, despite the wealth of evidence and guideline recommendations, its use in clinical practice remains minimal. Patient and technical limitations of FFR as well as the need for intracoronary instrumentation, use of adenosine, and increased costs have limited FFR's applicability in clinical practice. Over the last decade, several angiography-derived FFR software packages have been developed which do not require intracoronary pressure assessment with a guidewire or need for administration of hyperaemic agents. At present, there are 3 commercially available software packages and several other non-commercial technologies that have been described in the literature. These technologies have been validated against invasive FFR showing good accuracy and correlation. However, the methodology behind these solutions is different-some algorithms are based on solving the governing equations of fluid dynamics such as the Navier-Stokes equation while others have opted for a more simplified mathematical formula approach. The aim of this review is to critically appraise the methodology behind all the known angiography-derived FFR technologies highlighting the key differences and limitations.

The Evolution of Data Fusion Methodologies Developed to Reconstruct Coronary Artery Geometry From Intravascular Imaging and Coronary Angiography Data: A Comprehensive Review.

Understanding the mechanisms that regulate atherosclerotic plaque formation and evolution is a crucial step for developing treatment strategies that will prevent plaque progression and reduce cardiovascular events. Advances in signal processing and the miniaturization of medical devices have enabled the design of multimodality intravascular imaging catheters that allow complete and detailed assessment of plaque morphology and biology. However, a significant limitation of these novel imaging catheters is that they provide two-dimensional (2D) visualization of the lumen and vessel wall and thus they cannot portray vessel geometry and 3D lesion architecture. To address this limitation computer-based methodologies and user-friendly software have been developed. These are able to off-line process and fuse intravascular imaging data with X-ray or computed tomography coronary angiography (CTCA) to reconstruct coronary artery anatomy. The aim of this review article is to summarize the evolution in the field of coronary artery modeling; we thus present the first methodologies that were developed to model vessel geometry, highlight the modifications introduced in revised methods to overcome the limitations of the first approaches and discuss the challenges that need to be addressed, so these techniques can have broad application in clinical practice and research.

Evaluation of the Efficacy of Computed Tomographic Coronary Angiography in Assessing Coronary Artery Morphology and Physiology: Rationale and Study Design.

Computed tomographic coronary angiography (CTCA) is a non-invasive imaging modality, which allows plaque burden and composition assessment and detection of plaque characteristics associated with increased vulnerability. In addition, CTCA-based coronary artery reconstruction enables local haemodynamic forces assessment, which regulate plaque formation and vascular inflammation and prediction of lesions that are prone to progress and cause events. However, the use of CTCA for vulnerable plaque detection in the clinical arena remains limited. To unlock the full potential of CTCA and enable its broad use, further work is needed to develop user-friendly processing tools that will allow fast and accurate analysis of CTCA, computational fluid dynamic modelling, and evaluation of the local haemodynamic forces. The present study aims to develop a seamless platform that will overcome the limitations of CTCA and enable fast and accurate evaluation of plaque morphology and physiology. We will analyse imaging data from 70 patients with coronary artery disease who will undergo state-of-the-art CTCA and near-infrared spectroscopy-intravascular ultrasound imaging and develop and train algorithms that will take advantage of the intravascular imaging data to optimise vessel segmentation and plaque characterisation. Furthermore, we will design an advanced module that will enable reconstruction of coronary artery anatomy from CTCA, blood flow simulation, shear stress estimation, and comprehensive visualisation of vessel pathophysiology. These advances are expected to facilitate the broad use of CTCA, not only for risk stratification but also for the evaluation of the effect of emerging therapies on plaque evolution.