88 resultados para ATHEROSCLEROTIC CORONARY PLAQUES
em Queensland University of Technology - ePrints Archive
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Background: Biomechanical stresses play an important role in determining plaque stability. Quantification of these simulated stresses can be potentially used to assess plaque vulnerability and differentiate different patient groups. Methods and Results: 54 asymptomatic and 45 acutely symptomatic patients underwent in vivo multicontrast magnetic resonance imaging (MRI) of the carotid arteries. Plaque geometry used for finite element analysis was derived from in vivo MRI at the sites of maximum and minimum plaque burden. In total, 198 slices were used for the computational simulations. A pre-shrink technique was used to refine the simulation. Maximum principle stress at the vulnerable plaque sites (ie, critical stress) was extracted for the selected slices and a comparison was performed between the 2 groups. Critical stress in the slice with maximum plaque burden is significantly higher in acutely symptomatic patients as compared to asymptomatic patients (median, inter quartile range: 198.0 kPa (119.8-359.0 kPa) vs 138.4 kPa (83.8-242.6 kPa), P=0.04). No significant difference was found in the slice with minimum plaque burden between the 2 groups (196.7 kPa (133.3-282.7 kPa) vs 182.4 kPa (117.2-310.6 kPa), P=0.82). Conclusions: Acutely symptomatic carotid plaques have significantly high biomechanical stresses than asymptomatic plaques. This might be potentially useful for establishing a biomechanical risk stratification criteria based on plaque burden in future studies.
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Background: High-resolution magnetic resonance (MR) imaging has been used for MR imaging-based structural stress analysis of atherosclerotic plaques. The biomechanical stress profile of stable plaques has been observed to differ from that of unstable plaques; however, the role that structural stresses play in determining plaque vulnerability remains speculative. Methods: A total of 61 patients with previous history of symptomatic carotid artery disease underwent carotid plaque MR imaging. Plaque components of the index artery such as fibrous tissue, lipid content and plaque haemorrhage (PH) were delineated and used for finite element analysis-based maximum structural stress (M-C Stress) quantification. These patients were followed up for 2 years. The clinical end point was occurrence of an ischaemic cerebrovascular event. The association of the time to the clinical end point with plaque morphology and M-C Stress was analysed. Results: During a median follow-up duration of 514 days, 20% of patients (n=12) experienced an ischaemic event in the territory of the index carotid artery. Cox regression analysis indicated that M-C Stress (hazard ratio (HR): 12.98 (95% confidence interval (CI): 1.32-26.67, pZ0.02), fibrous cap (FC) disruption (HR: 7.39 (95% CI: 1.61e33.82), p Z 0.009) and PH (HR: 5.85 (95% CI: 1.27e26.77), p Z 0.02) are associated with the development of subsequent cerebrovascular events. Plaques associated with future events had higher M-C Stress than those which had remained asymptomatic (median (interquartile range, IQR): 330 kPa (229e494) vs. 254 kPa (166-290), p Z0.04). Conclusions: High biomechanical structural stresses, in addition to FC rupture and PH, are associated with subsequent cerebrovascular events.
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The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.
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Introduction: Inflammation is a recognized risk factor for the vulnerable atherosclerotic plaque. The aim of this study was to explore whether there is a difference in the degree of Magnetic Resonance (MR) defined inflammation using Ultra Small Super-Paramagnetic Iron Oxide (USPIO) particles, within carotid atheroma in completely asymptomatic individuals and the asymptomatic carotid stenosis in a cohort of patients undergoing coronary artery bypass grafting (CABG). Methods: 10 patients awaiting CABG with asymptomatic carotid disease and 10 completely asymptomatic individuals with no documented coronary artery disease underwent multi-sequence MR imaging before and 36 hours post USPIO infusion. Images were manually segmented into quadrants and signal change in each quadrant, normalised to adjacent muscle signal, was calculated following USPIO administration. Results: The mean percentage of quadrants showing signal loss was 94% in the CABG group, compared to 24% in the completely asymptomatic individuals (p < 0.001). The carotid plaques from the CABG patients showed a significant mean signal intensity decrease of 16.4% after USPIO infusion (95% CI 10.6% to 22.2%; p < 0.001). The truly asymptomatic plaques showed a mean signal intensity increase (i.e. enhancement) after USPIO infusion of 8.4% (95% CI 2.6% to 14.2%; p = 0.007). The mean signal difference between the two groups was 24.9% (95% CI 16.7% to 33.0%; p < 0.001). Conclusions: These findings are consistent with the hypothesis that inflammatory atheroma is a systemic disease. The carotid territory is more likely to take up USPIO if another vascular territory is symptomatic.
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Objectives: There is considerable evidence that patients with carotid artery stenosis treated immediately after the ischaemic cerebrovascular event have a better clinical outcome than those who have delayed treatment. Biomechanical assessment of carotid plaques using high-resolution MRI can help examine the relationship between the timing of carotid plaque symptomology and maximum simulated plaque stress concentration. Methods: Fifty patients underwent high-resolution multisequence in vivo MRI of their carotid arteries. Patients with acute symptoms (n=25) underwent MRI within 72 h of the onset of ischaemic cerebrovascular symptoms, whereas recently symptomatic patients (n=25) underwent MRI from 2 to 6 weeks after the onset of symptoms. Stress analysis was performed based on the geometry derived from in vivo MRI of the symptomatic carotid artery at the point of maximum stenosis. The peak stresses within the plaques of the two groups were compared. Results: Patient demographics were comparable for both groups. All the patients in the recently symptomatic group had severe carotid stenosis in contrast to patients with acute symptoms who had predominantly mild to moderate carotid stenosis. The simulated maximum stresses in patients with acute symptoms was significantly higher than in recently symptomatic patients (median (IQR): 313310 4 dynes/cm 2 (295 to 382) vs 2523104 dynes/cm 2 (236 to 311), p=0.02). Conclusions: Patients have extremely unstable, high-risk plaques, with high stresses, immediately after an acute cerebrovascular event, even at lower degrees of carotid stenoses. Biomechanical stress analysis may help us refine our risk-stratification criteria for the management of patients with carotid artery disease in future.
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Introduction Two symposia on “cardiovascular diseases and vulnerable plaques” Cardiovascular disease (CVD) is the leading cause of death worldwide. Huge effort has been made in many disciplines including medical imaging, computational modeling, bio- mechanics, bioengineering, medical devices, animal and clinical studies, population studies as well as genomic, molecular, cellular and organ-level studies seeking improved methods for early detection, diagnosis, prevention and treatment of these diseases [1-14]. However, the mechanisms governing the initiation, progression and the occurrence of final acute clinical CVD events are still poorly understood. A large number of victims of these dis- eases who are apparently healthy die suddenly without prior symptoms. Available screening and diagnostic methods are insufficient to identify the victims before the event occurs [8,9]. Most cardiovascular diseases are associated with vulnerable plaques. A grand challenge here is to develop new imaging techniques, predictive methods and patient screening tools to identify vulnerable plaques and patients who are more vulnerable to plaque rupture and associated clinical events such as stroke and heart attack, and recommend proper treatment plans to prevent those clinical events from happening. Articles in this special issue came from two symposia held recently focusing on “Cardio-vascular Diseases and Vulnerable Plaques: Data, Modeling, Predictions and Clinical Applications.” One was held at Worcester Polytechnic Institute (WPI), Worcester, MA, USA, July 13-14, 2014, right after the 7th World Congress of Biomechanics. This symposium was endorsed by the World Council of Biomechanics, and partially supported by a grant from NIH-National Institute of Biomedical Image and Bioengineering. The other was held at Southeast University (SEU), Nanjing, China, April 18-20, 2014.
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Rupture of atheromatous plaque is the major cause of stroke or heart attack. Considering that the cardiovascular system is a classic fatigue environment, plaque rupture was treated as a chronic fatigue crack growth process in this study. Fracture mechanics theory was introduced to describe the stress status at the crack tip and Paris' law was used to calculate the crack growth rate. The effect of anatomical variation of an idealized plaque cross-section model was investigated. The crack initiation was considered to be either at the maximum circumferential stress location or at any other possible locations around the lumen. Although the crack automatically initialized at the maximum circumferential stress location usually propagated faster than others, it was not necessarily the most critical location where the fatigue life reached its minimum. We found that the fatigue life was minimum for cracks initialized in the following three regions: the midcap zone, the shoulder zone, and the backside zone. The anatomical variation has a significant influence on the fatigue life. Either a decrease in cap thickness or an increase in lipid pool size resulted in a significant decrease in fatigue life. Comparing to the previously used stress analysis, this fatigue model provides some possible explanations of plaque rupture at a low stress level in a pulsatile cardiovascular environment, and the method proposed here may be useful for further investigation of the mechanism of plaque rupture based on in vivo patient data.
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High mechanical stress in atherosclerotic plaques at vulnerable sites, called critical stress, contributes to plaque rupture. The site of minimum fibrous cap (FC) thickness (FCMIN) and plaque shoulder are well-documented vulnerable sites. The inherent weakness of the FC material at the thinnest point increases the stress, making it vulnerable, and it is the big curvature of the lumen contour over FC which may result in increased plaque stress. We aimed to assess critical stresses at FCMIN and the maximum lumen curvature over FC (LCMAX) and quantify the difference to see which vulnerable site had the highest critical stress and was, therefore, at highest risk of rupture. One hundred patients underwent high resolution carotid magnetic resonance (MR) imaging. We used 352 MR slices with delineated atherosclerotic components for the simulation study. Stresses at all the integral nodes along the lumen surface were calculated using the finite-element method. FCMIN and LCMAX were identified, and critical stresses at these sites were assessed and compared. Critical stress at FC MIN was significantly lower than that at LCMAX (median: 121.55 kPa; inter quartile range (IQR) = [60.70-180.32] kPa vs. 150.80 kPa; IQR = [91.39-235.75] kPa, p < 0.0001). If critical stress at FCMIN was only used, then the stress condition of 238 of 352 MR slices would be underestimated, while if the critical stress at LCMAX only was used, then 112 out of 352 would be underestimated. Stress analysis at FCMIN and LCMAX should be used for a refined mechanical risk assessment of atherosclerotic plaques, since material failure at either site may result in rupture.
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Introduction: Ultrasmall superparamagnetic iron oxide (USPIO)-enhanced MRI has been shown to be a useful modality to image activated macrophages in vivo, which are principally responsible for plaque inflammation. This study determined the optimum imaging time-window to detect maximal signal change post-USPIO infusion using T1-weighted (T1w), T2*- weighted (T2*w) and quantitative T2*(qT 2*) imaging. Methods: Six patients with an asymptomatic carotid stenosis underwent high resolution T1w, T2*w and qT2*MR imaging of their carotid arteries at 1.5 T. Imaging was performed before and at 24, 36, 48, 72 and 96 h after USPIO (Sinerem™, Guerbet, France) infusion. Each slice showing atherosclerotic plaque was manually segmented into quadrants and signal changes in each quadrant were fitted to an exponential power function to model the optimum time for post-infusion imaging. Results: The power function determining the mean time to convergence for all patients was 46, 41 and 39 h for the T1w, T 2*w and qT2*sequences, respectively. When modelling each patient individually, 90% of the maximum signal intensity change was observed at 36 h for three, four and six patients on T1w, T 2*w and qT2*, respectively. The rates of signal change decrease after this period but signal change was still evident up to 96 h. Conclusion: This study showed that a suitable imaging window for T 1w, T2*w and qT2*signal changes post-USPIO infusion was between 36 and 48 h. Logistically, this would be convenient in bringing patients back for one post-contrast MRI, but validation is required in a larger cohort of patients.
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Atherothrombosis is a systemic disease of the arterial wall that affects the carotid, coronary, and peripheral vascular beds, and the aorta. This condition is associated with complications such as stroke, myocardial infarction, and peripheral vascular disease, which usually result from unstable atheromatous plaques. The study of atheromatous plaques can provide useful information about the natural history and progression of the disease, and aid in the selection of appropriate treatment. Plaque imaging can be crucial in achieving this goal. In this Review, we focus on the various noninvasive imaging techniques that are being used for morphological and functional assessment of carotid atheromatous plaques in the clinical setting.
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Atherosclerotic plaque rupture has been extensively considered as the leading cause of death in the world. It is believed that high stress within plaque can be an important factor which can trigger the rupture of the plaque. High resolution multi-spectral magnetic resonance imaging (MRI) has allowed the plaque components (arterial wall, lipids, and fibrous cap) to be visualized in vivo [1]. The patient specific finite element model can be generated from the image data to perform stress analysis and provide critical information on understanding plaque rupture mechanisms [2]. The present work is to apply the procedure to a total of 14 patients (S1 ∼ S14), to study the stress distributions on carotid artery plaque reconstructed from multi-spectral magnetic resonance images, and the possible relationships between stress and plaque burdens.
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Rupture of vulnerable atheromatous plaque in the carotid and coronary arteries often leads to stroke and heart attack respectively. The mechanism of blood flow and plaque rupture in stenotic arteries is still not fully understood. A three dimensional rigid wall model was solved under steady state conditions and unsteady conditions by assuming a time-varying inlet velocity profile to investigate the relative importance of axial forces and pressure drops in arteries with asymmetric stenosis. Flow-structure interactions were investigated for the same geometry and the results were compared with those retrieved with the corresponding 2D cross-section structural models. The Navier-Stokes equations were used as the governing equations for the fluid. The tube wall was assumed hyperelastic, homogeneous, isotropic and incompressible. The analysis showed that the three dimensional behavior of velocity, pressure and wall shear stress is in general very different from that predicted by cross-section models. Pressure drop across the stenosis was found to be much higher than shear stress. Therefore, pressure may be the more important mechanical trigger for plaque rupture other than shear stress, although shear stress is closely related to plaque formation and progression.
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Ticagrelor is an orally active ADP P2Y12 receptor antagonist in development by AstraZeneca plc for the reduction of recurrent ischemic events in patients with acute coronary syndromes (ACS). Prior to the development of ticagrelor, thienopyridine compounds, such as clopidogrel, were the focus of research into therapies for ACS. Although the thienopyridines are effective platelet aggregation inhibitors, they are prodrugs and, consequently, exert a slow onset of action. In addition, the variability in inter-individual metabolism of thienopyridine prodrugs has been associated with reduced efficacy in some patients. Ticagrelor is not a prodrug and exhibits a more rapid onset of action than the thienopyridine prodrugs. In clinical trials conducted to date, ticagrelor was a potent inhibitor of ADP-induced platelet aggregation and demonstrated effects that were comparable to clopidogrel. In a phase II, short-term trial, the bleeding profile of participants treated with ticagrelor was similar to that obtained with clopidogrel; however, an increased incidence of dyspnea was observed - an effect that has not been reported with the thienopyridines. Considering the occurrence of dyspnea, and the apparent non-superiority of ticagrelor to clopidogrel, it is difficult to justify a clear benefit to the continued development of ticagrelor. Outcomes from an ongoing phase III trial comparing ticagrelor with clopidogrel in 18,000 patients with ACS are likely to impact on the future development of ticagrelor.
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Patients with chest discomfort or other symptoms suggestive of acute coronary syndrome (ACS) are one of the most common categories seen in many Emergency Departments (EDs). While the recognition of patients at high-risk of ACS has improved steadily, identifying the majority of chest pain presentations who fall into the low-risk group remains a challenge. Research in this area needs to be transparent, robust, applicable to all hospitals from large tertiary centres to rural and remote sites, and to allow direct comparison between different studies with minimum patient spectrum bias. A standardised approach to the research framework using a common language for data definitions must be adopted to achieve this. The aim was to create a common framework for a standardised data definitions set that would allow maximum value when extrapolating research findings both within Australasian ED practice, and across similar populations worldwide. Therefore a comprehensive data definitions set for the investigation of non-traumatic chest pain patients with possible ACS was developed, specifically for use in the ED setting. This standardised data definitions set will facilitate ‘knowledge translation’ by allowing extrapolation of useful findings into the real-life practice of emergency medicine.