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Prediction of the location of culprit lesions responsible for ST-segment elevation myocardial infarctions may allow for prevention of these events. A retrospective analysis of coronary artery motion (CAM) was performed on coronary angiograms of 20 patients who subsequently had ST-segment elevation myocardial infarction treated by primary or rescue...
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Introduction:
Electrocardiogram a widely available tool may predict infarct related artery in acute inferior wall myocardial infarction. Severity of ST segment elevation may correlate with proximity of lesion in right coronary artery.
Methods:
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Citations
... Coronary artery motion has been proposed to have an effect on the location of coronary artery disease by causing both low shear wall stress and increased wall strain [12]. We and other authors have previously shown that the compression pattern of coronary artery motion correlates with the location of coronary artery disease [13] and is predictive of the locations of culprit lesions in STEMIs [14][15][16]. ...
... The results in this study extend this finding to a quantitative method using MSCT. The compression pattern of coronary artery motion has also been shown to be predictive of the locations of culprit lesions in ST-segment elevation myocardial infarctions [14][15][16]. Whether QCAM is predictive of the location of culprit lesions in STEMIs is a clinically interesting hypothesis that remains to be tested. ...
Background: We describe a novel quantitative index of coronary artery motion (QCAM) from multislice computed tomography (MSCT) and test its association with the location of coronary artery disease. Methods: 25 patients with known or suspected coronary artery disease underwent ECG-gated MSCT. The coronary artery images were divided into 150 sections using landmarks that could be identified at time points at end-diastole and end-systole. QCAM was derived from the change in centerline length of the coronary sections between these time points. Plaques were identified and classified by type and severity of stenosis. Results: The mean QCAM was significantly less in the coronary sections with plaque (94.3%+/-8.1%) than those without (99.0%+/-10.2%) (p=0.023). There was a significant correlation between QCAM and plaque stenosis (Spearman's rank correlation coefficient, ρ= -0.192, p=0.018). The correlation between QCAM and plaque type approached statistical significance (Spearman's rank correlation coefficient, ρ= -0.156, p=0.057). Sensitivity, specificity, positive and negative predictive values for the identification of coronary plaque within a section for QCAM <100% were 80%, 46%, 27% and 90% respectively. Conclusions: QCAM is a novel quantitative measurement of coronary artery motion that correlates with the location of coronary artery disease. Quantitative evaluation of coronary artery motion provides a new approach to understanding the biomechanics of coronary artery disease.
Aims
Cardiac cycle morphological changes can accelerate plaque growth proximal to myocardial bridging (MB) in the left anterior descending artery (LAD). To assess coronary computed tomography angiography (CCTA)-based vascular radiomics for predicting proximal plaque development in LAD MB.
Methods and results
Patients with repeated CCTA scans showing LAD MB without proximal plaque in index CCTA were included from Jinling Hospital as a development set. They were divided into training and internal testing in an 8:2 ratio. Patients from four other tertiary hospitals were set as external validation set. The endpoint was proximal plaque development of LAD MB in follow-up CCTA. Four vascular radiomics models were built: MB centreline (MB CL), proximal MB CL (pMB CL), MB cross-section (MB CS), and proximal MB CS (pMB CS), whose performances were evaluated using area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), and net reclassification improvement (NRI). In total, 295 patients were included in the development (n = 192; median age, 54 ± 11 years; 137 men) and external validation sets (n = 103; median age, 57 ± 9 years; 57 men). The pMB CS vascular radiomics model exhibited higher AUCs in training, internal test, and external sets (AUC = 0.78, 0.75, 0.75) than the clinical and anatomical model (all P < 0.05). Integration of the pMB CS vascular radiomics model significantly raised the AUC of the clinical and anatomical model from 0.56 to 0.75 (P = 0.002), along with enhanced NRI [0.76 (0.37–1.14), P < 0.001] and IDI [0.17 (0.07–0.26), P < 0.001] in the external validation set.
Conclusion
The CCTA-based pMB CS vascular radiomics model can predict plaque development in LAD MB.
Temporal variations of the coronary arteries during a cardiac cycle are defined as the superposition of the changes in the position, curvature, and torsion of the coronary artery axis markers and the variations in the lumen cross-sectional shape due to the distensible wall motion induced by the pulse pressure and contraction of the myocardium in a cardiac cycle. This review discusses whether the modelling the temporal variations of the coronary arteries is needed for the investigation of the hemodynamics specifically in time critical applications such as a clinical environment. The numerical modellings in the literature which model or disregard the temporal variations of the coronary arteries on the hemodynamic parameters are discussed. The results in the literature show that neglecting the effects of temporal geometric variations is expected to result in about 5\% deviation of the time-averaged pressure drop and wall shear stress values and also about 20\% deviation of the temporal variations of hemodynamic parameters, such as time-dependent wall shear stress and oscillatory shear index. This review study can be considered as a guide for the future studies to outline the conditions in which temporal variations of the coronary arteries can be neglected, while providing a reliable estimation of hemodynamic parameters.
