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quantitative coronary angiography (QCA) systems that have traditionally been used to assess lesion severity have been replaced by 3D-QCA systems, enabling more reliable evaluation of vessel geometry and lesion dimensions.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.

Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images.

PURPOSE: The assessment of vulnerable plaque characteristics and distribution is important to stratify cardiovascular risk in a patient. Computed tomography angiography (CTA) offers a promising alternative to invasive imaging but is limited by the fact that the range of Hounsfield units (HU) in lipid-rich areas overlaps with the HU range in fibrotic tissue and that the HU range of calcified plaques overlaps with the contrast within the contrast-filled lumen. This paper is to investigate whether lipid-rich and calcified plaques can be detected more accurately on cross-sectional CTA images using deep learning methodology. METHODS: Two deep learning (DL) approaches are proposed, a 2.5D Dense U-Net and 2.5D Mask-RCNN, which separately perform the cross-sectional plaque detection in the Cartesian and polar domain. The spread-out view is used to evaluate and show the prediction result of the plaque regions. The accuracy and F1-score are calculated on a lesion level for the DL and conventional plaque detection methods. RESULTS: For the lipid-rich plaques, the median and mean values of the F1-score calculated by the two proposed DL methods on 91 lesions were approximately 6 and 3 times higher than those of the conventional method. For the calcified plaques, the F1-score of the proposed methods was comparable to those of the conventional method. The median F1-score of the Dense U-Net-based method was 3% higher than that of the conventional method. CONCLUSION: The two methods proposed in this paper contribute to finer cross-sectional predictions of lipid-rich and calcified plaques compared to studies focusing only on longitudinal prediction. The angular prediction performance of the proposed methods outperforms the convincing conventional method for lipid-rich plaque and is comparable for calcified plaque.

Chronic total occlusion in non-ST elevation myocardial infarction - A multi-centre observational study.

OBJECTIVES: To evaluate the characteristics and outcomes of patients with a chronic total occlusion (CTO) in a Non-ST Elevation Myocardial Infarction (NSTEMI) cohort. BACKGROUND: There is limited data on the clinical characteristics, revascularisation strategies and outcomes of patients presenting with a NSTEMI and a CTO. METHODS: Retrospective analysis of a six-centre percutaneous coronary intervention (PCI) registry in the UK between January 2015 and December 2020 was performed. Patients with a NSTEMI with and without a CTO were compared for baseline characteristics and outcomes. RESULTS: There were 17,355 NSTEMI patients in total of whom 1813 patients had a CTO (10.4 %). Patients with a CTO were more likely to be older (CTO: 67.8 (±11.5) years vs. no CTO: 67.2 (±12) years, p = 0.04), male (CTO: 81.1 % vs.71.9 %, p < 0.0001) with a greater prevalence of cardiovascular risk factors. All-cause mortality at 30 days: HR 2.63, 95 % CI 1.42-4.84, p = 0.002 and at 1 year: HR: 1.87, 95 % CI 1.25-2.81, p = 0.003 was higher in the CTO cohort. CTO patients who underwent revascularisation were younger (Revascularisation 66.4 [±11.7] years vs. no revascularisation 68.4 [±11.4] years, p = 0.001). Patients with failed CTO revascularisation had lower survival (HR 0.21, 95 % CI 0.10-0.42, p < 0.0001). The mean time to revascularisation was 13.4 days. There was variation in attempt at CTO revascularisation between the 6 centres for (16 % to 100 %) with success rates ranging from 65 to 100 %. CONCLUSIONS: In conclusion, the presence of a CTO in NSTEMI patients undergoing PCI was associated with worse in-hospital and long-term outcomes.

Efficacy of human experts and an automated segmentation algorithm in quantifying disease pathology in coronary computed tomography angiography: A head-to-head comparison with intravascular ultrasound imaging.

BACKGROUND: Coronary computed tomography angiography (CCTA) analysis is currently performed by experts and is a laborious process. Fully automated edge-detection methods have been developed to expedite CCTA segmentation however their use is limited as there are concerns about their accuracy. This study aims to compare the performance of an automated CCTA analysis software and the experts using near-infrared spectroscopy-intravascular ultrasound imaging (NIRS-IVUS) as a reference standard. METHODS: Fifty-one participants (150 vessels) with chronic coronary syndrome who underwent CCTA and 3-vessel NIRS-IVUS were included. CCTA analysis was performed by an expert and an automated edge detection method and their estimations were compared to NIRS-IVUS at a segment-, lesion-, and frame-level. RESULTS: Segment-level analysis demonstrated a similar performance of the two CCTA analyses (conventional and automatic) with large biases and limits of agreement compared to NIRS-IVUS estimations for the total atheroma (ICC: 0.55 vs 0.25, mean difference:192 (-102-487) vs 243 (-132-617) and percent atheroma volume (ICC: 0.30 vs 0.12, mean difference: 12.8 (-5.91-31.6) vs 20.0 (0.79-39.2). Lesion-level analysis showed that the experts were able to detect more accurately lesions than the automated method (68.2 â€‹% and 60.7 â€‹%) however both analyses had poor reliability in assessing the minimal lumen area (ICC 0.44 vs 0.36) and the maximum plaque burden (ICC 0.33 vs 0.33) when NIRS-IVUS was used as the reference standard. CONCLUSIONS: Conventional and automated CCTA analyses had similar performance in assessing coronary artery pathology using NIRS-IVUS as a reference standard. Therefore, automated segmentation can be used to expedite CCTA analysis and enhance its applications in clinical practice.

Early vascular healing after neXt-generation drug-eluting stent implantation in Patients with non-ST Elevation acute Coronary syndrome based on optical coherence Tomography guidance and evaluation (EXPECT): study protocol for a randomized controlled trial.

Background: There is limited evidence about vessel wall healing response following implantation of next-generation drug-eluting stents (DES) in patients admitted with a non-ST elevation acute coronary syndrome (NSTE-ACS). Cumulative data indicate that optical coherence tomography (OCT) imaging can optimize percutaneous coronary intervention results and expedite stent endothelialization in the general population but there is lack of data in NSTE-ACS patients. Methods: The EXPECT study is an investigator-initiated, prospective, randomized trial to assess early vascular healing response following next-generation DES implantation in patients admitted with NSTE-ACS based on OCT guidance and evaluation. Sixty patients are randomized at 1:1:1 ratio to OCT-guided percutaneous coronary intervention (PCI) with 3-month follow-up OCT imaging (O3 group, n = 20), to angiography-guided PCI with 3-month follow-up OCT imaging (A3 group, n = 20) and to angiography-guided PCI with 6-month follow-up OCT imaging (A6 group, n = 20). The primary endpoint of the study is stent strut coverage rate at 3- or 6- month follow-up in the studied groups. The secondary endpoints of the study include OCT imaging endpoints, clinical endpoints, and molecular biology endpoints at the different time points. The clinical endpoints comprised of major cardiovascular adverse events and individual components. The molecular biology endpoints comprised of lipid levels and the levels of inflammatory indicators. Discussion: The findings of the EXPECT study are anticipated to provide novel insights into vessel wall healing in NSTE-ACS population following implantation of next-generation DES, underscore the value of OCT imaging in expediting strut coverage in this setting, and explore the potential of an early discontinuation of dual antiplatelet therapy (DAPT) in this population. Clinical Trial Registration: ClinicalTrials.gov, NCT04375319.

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.

Mediastinal monophasic synovial sarcoma with pericardial extension causing hemodynamic instability.

A 46-year-old man presented with mass on chest x-ray along with a 6-month history of weight loss, dyspnea and cough. He was hypotensive and an echocardiogram showed large extra-cardiac mass compressing the right ventricular outflow tract resulting in features of cardiac tamponade. Chest computed tomography revealed a mediastinal mass invading the pericardium adjacent to right ventricular outflow tract. Biopsy of the mass confirmed primary monophasic synovial sarcoma. Chemotherapy and radiotherapy along with anti-inflammatories were given as surgery was too high risk due to the location of the tumour and pericardial involvement. Patient responded briefly to the treatment with improvement in hemodynamic parameters but over next weeks he became less responsive to treatment with increasing size. He died 2 months after treatment commenced.