Coronary heart disease is a common cause of death despite being preventable. To treat the underlying plaque deposits in the arterial walls, intravascular optical coherence tomography can be used by experts to detect and characterize the lesions. In clinical routine, hundreds of images are acquired for each patient which requires automatic plaque detection for fast and accurate decision support. So far, automatic approaches rely on classic machine learning methods and deep learning solutions have rarely been studied. Given the success of deep learning methods with other imaging modalities, a thorough understanding of deep learning-based plaque detection for future clinical decision support systems is required. We address this issue with a new dataset consisting of in-vivo patient images labeled by three trained experts. Using this dataset, we employ state-of-the-art deep learning models that directly learn plaque classification from the images. For improved performance, we study different transfer learning approaches. Furthermore, we investigate the use of cartesian and polar image representations and employ data augmentation techniques tailored to each representation. We fuse both representations in a multi-path architecture for more effective feature exploitation. Last, we address the challenge of plaque differentiation in addition to detection. Overall, we find that our combined model performs best with an accuracy of 91.7%, a sensitivity of 90.9% and a specificity of 92.4%. Our results indicate that building a deep learning-based clinical decision support system for plaque detection is feasible.
Aims Acute coronary syndromes with intact fibrous cap (IFC-ACS), i.e. caused by coronary plaque erosion, account for approximately one-third of ACS. However, the underlying pathophysiological mechanisms as compared with ACS caused by plaque rupture (RFC-ACS) remain largely undefined. The prospective translational OPTICO-ACS study programme investigates for the first time the microenvironment of ACS-causing culprit lesions (CL) with intact fibrous cap by molecular high-resolution intracoronary imaging and simultaneous local immunological phenotyping. Methods and results The CL of 170 consecutive ACS patients were investigated by optical coherence tomography (OCT) and simultaneous immunophenotyping by flow cytometric analysis as well as by effector molecule concentration measurements across the culprit lesion gradient (ratio local/systemic levels). Within the study cohort, IFC caused 24.6% of ACS while RFC-ACS caused 75.4% as determined and validated by two independent OCT core laboratories. The IFC-CL were characterized by lower lipid content, less calcification, a thicker overlying fibrous cap, and largely localized near a coronary bifurcation as compared with RFC-CL. The microenvironment of IFC-ACS lesions demonstrated selective enrichment in both CD4+ and CD8+ T-lymphocytes (+8.1% and +11.2%, respectively, both P < 0.05) as compared with RFC-ACS lesions. T-cell-associated extracellular circulating microvesicles (MV) were more pronounced in IFC-ACS lesions and a significantly higher amount of CD8+ T-lymphocytes was detectable in thrombi aspirated from IFC-culprit sites. Furthermore, IFC-ACS lesions showed increased levels of the T-cell effector molecules granzyme A (+22.4%), perforin (+58.8%), and granulysin (+75.4%) as compared with RFC plaques (P < 0.005). Endothelial cells subjected to culture in disturbed laminar flow conditions, i.e. to simulate coronary flow near a bifurcation, demonstrated an enhanced adhesion of CD8+T cells. Finally, both CD8+T cells and their cytotoxic effector molecules caused endothelial cell death, a key potential pathophysiological mechanism in IFC-ACS. Conclusions The OPTICO-ACS study emphasizes a novel mechanism in the pathogenesis of IFC-ACS, favouring participation of the adaptive immune system, particularly CD4+ and CD8+ T-cells and their effector molecules. The different immune signatures identified in this study advance the understanding of coronary plaque progression and may provide a basis for future development of personalized therapeutic approaches to ACS with IFC. Trial registration The study was registered at clinicalTrials.gov (NCT03129503).
Background: Quantitative flow ratio (QFR) has been introduced as a novel angiography-based modality for fast hemodynamic assessment of coronary artery lesions and validated against fractional flow reserve. This study sought to define the prognostic role of pancoronary QFR assessment in patients with acute coronary syndrome (ACS) including postinterventional culprit and nonculprit vessels. Methods: In a total of 792 patients with ACS (48.6% ST-segment–elevation ACS and 51.4% non–ST-segment–elevation ACS), QFR analyses of postinterventional culprit (n=792 vessels) and nonculprit vessels (n=1231 vessels) were post hoc performed by investigators blinded to clinical outcomes. The follow-up comprised of major adverse cardiovascular events, including all-cause mortality, nonfatal myocardial infarction, and ischemia-driven coronary revascularization within 2 years after the index ACS event. Results: Major adverse cardiovascular events as composite end point occurred in 99 patients (12.5%). QFR with an optimal cutoff value of 0.89 for postinterventional culprit vessels and 0.85 for nonculprit vessels emerged as independent predictor of major adverse cardiovascular events after ACS (nonculprit arteries: adjusted odds ratio, 3.78 [95% CI, 2.21–6.45], P <0.001 and postpercutaneous coronary intervention culprit arteries: adjusted odds ratio, 3.60 [95% CI, 2.09–6.20], P <0.001). Conclusions: The present study for the first time demonstrates the prognostic implications of a pancoronary angiography-based functional lesion assessment in patients with ACS. Hence, QFR offers a novel tool to advance risk stratification and guide therapeutic management after ACS.
Several studies have demonstrated the feasibility and safety of hemodynamic assessment of non-culprit coronary arteries in setting of acute coronary syndromes (ACS) using fractional flow reserve (FFR) measurements. Quantitative flow ratio (QFR), recently introduced as angiography-based fast FFR computation, has been validated with good agreement and diagnostic performance with FFR in chronic coronary syndromes. The aim of this study was to assess the feasibility and diagnostic reliability of QFR assessment during primary PCI. A total of 321 patients with ACS and multivessel disease, who underwent primary PCI and were planned for staged PCI of at least one non-culprit lesion were enrolled in the analysis. Within this patient cohort, serial post-hoc QFR analyses of 513 non-culprit vessels were performed. The median time interval between primary and staged PCI was 49 [42–58] days. QFR in non-culprit coronary arteries did not change between acute and staged measurements (0.86 vs 0.87, p = 0.114), with strong correlation (r = 0.94, p ≤ 0.001) and good agreement (mean difference -0.008, 95%CI -0.013–0.003) between measurements. Importantly, QFR as assessed at index procedure had sensitivity of 95.02%, specificity of 93.59% and diagnostic accuracy of 94.15% in prediction of QFR ≤ 0.80 at the time of staged PCI. The present study for the first time confirmed the feasibility and diagnostic accuracy of non-culprit coronary artery QFR during index procedure for ACS. These results support QFR as valuable tool in patients with ACS to detect further hemodynamic relevant lesions with excellent diagnostic performance and therefore to guide further revascularisation therapy.
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