Objective The aim of this study was to determine the effect of successful coronary revascularisation on left ventricular diastolic function.Methods We consecutively studied the diastolic function by Doppler echocardio...Objective The aim of this study was to determine the effect of successful coronary revascularisation on left ventricular diastolic function.Methods We consecutively studied the diastolic function by Doppler echocardiography in 125 patients with one vessel disease before and 48 hours after selective coronary angioplasty. The following parameters of left ventricular diastolic function were evaluated: peak early (VE, m/s) and peak late diastolic (VA, m/s) flow velocity, E/A ratio, acceleration time (AT, ms), deceleration time (DT, ms) and isovolumetric relaxation time (IVRT, ms). Ejection fraction (EF; %) was determined and used to characterise systolic left ventricular function. Results All of the patients were initially successful treated with coronary angioplasty (residual stenosis <40% ). In 98 patients( 78.4% ) stents were used to improve an inadequate result after coronary angioplasty. Both patient groups (27 patients with coronary angioplasty and 98 patients with combined coronary angioplasty and stent implantation) showed no relevant differences concerning sex, age, atherosclerotic risk factors, exercise capacity and results of exercise electrocardiography. All patients who underwent stent implantation showed an early improvement of left ventricular diastolic function 48 hours after revascularisation. Surprisingly there was no significant short term improvement (48 hours) of diastolic function in patients with initially successful angioplasty.Conclusions We suppose that stent implantation might normalize coronary blood flow faster than that of coronary balloon angioplasty.展开更多
Objective To observe the value of deep learning echocardiographic intelligent model for evaluation on left ventricular(LV)regional wall motion abnormalities(RWMA).Methods Apical two-chamber,three-chamber and four-cham...Objective To observe the value of deep learning echocardiographic intelligent model for evaluation on left ventricular(LV)regional wall motion abnormalities(RWMA).Methods Apical two-chamber,three-chamber and four-chamber views two-dimensional echocardiograms were obtained prospectively in 205 patients with coronary heart disease.The model for evaluating LV regional contractile function was constructed using a five-fold cross-validation method to automatically identify the presence of RWMA or not,and the performance of this model was assessed taken manual interpretation of RWMA as standards.Results Among 205 patients,RWMA was detected in totally 650 segments in 83 cases.LV myocardial segmentation model demonstrated good efficacy for delineation of LV myocardium.The average Dice similarity coefficient for LV myocardial segmentation results in the apical two-chamber,three-chamber and four-chamber views was 0.85,0.82 and 0.88,respectively.LV myocardial segmentation model accurately segmented LV myocardium in apical two-chamber,three-chamber and four-chamber views.The mean area under the curve(AUC)of RWMA identification model was 0.843±0.071,with sensitivity of(64.19±14.85)%,specificity of(89.44±7.31)%and accuracy of(85.22±4.37)%.Conclusion Deep learning echocardiographic intelligent model could be used to automatically evaluate LV regional contractile function,hence rapidly and accurately identifying RWMA.展开更多
文摘Objective The aim of this study was to determine the effect of successful coronary revascularisation on left ventricular diastolic function.Methods We consecutively studied the diastolic function by Doppler echocardiography in 125 patients with one vessel disease before and 48 hours after selective coronary angioplasty. The following parameters of left ventricular diastolic function were evaluated: peak early (VE, m/s) and peak late diastolic (VA, m/s) flow velocity, E/A ratio, acceleration time (AT, ms), deceleration time (DT, ms) and isovolumetric relaxation time (IVRT, ms). Ejection fraction (EF; %) was determined and used to characterise systolic left ventricular function. Results All of the patients were initially successful treated with coronary angioplasty (residual stenosis <40% ). In 98 patients( 78.4% ) stents were used to improve an inadequate result after coronary angioplasty. Both patient groups (27 patients with coronary angioplasty and 98 patients with combined coronary angioplasty and stent implantation) showed no relevant differences concerning sex, age, atherosclerotic risk factors, exercise capacity and results of exercise electrocardiography. All patients who underwent stent implantation showed an early improvement of left ventricular diastolic function 48 hours after revascularisation. Surprisingly there was no significant short term improvement (48 hours) of diastolic function in patients with initially successful angioplasty.Conclusions We suppose that stent implantation might normalize coronary blood flow faster than that of coronary balloon angioplasty.
文摘Objective To observe the value of deep learning echocardiographic intelligent model for evaluation on left ventricular(LV)regional wall motion abnormalities(RWMA).Methods Apical two-chamber,three-chamber and four-chamber views two-dimensional echocardiograms were obtained prospectively in 205 patients with coronary heart disease.The model for evaluating LV regional contractile function was constructed using a five-fold cross-validation method to automatically identify the presence of RWMA or not,and the performance of this model was assessed taken manual interpretation of RWMA as standards.Results Among 205 patients,RWMA was detected in totally 650 segments in 83 cases.LV myocardial segmentation model demonstrated good efficacy for delineation of LV myocardium.The average Dice similarity coefficient for LV myocardial segmentation results in the apical two-chamber,three-chamber and four-chamber views was 0.85,0.82 and 0.88,respectively.LV myocardial segmentation model accurately segmented LV myocardium in apical two-chamber,three-chamber and four-chamber views.The mean area under the curve(AUC)of RWMA identification model was 0.843±0.071,with sensitivity of(64.19±14.85)%,specificity of(89.44±7.31)%and accuracy of(85.22±4.37)%.Conclusion Deep learning echocardiographic intelligent model could be used to automatically evaluate LV regional contractile function,hence rapidly and accurately identifying RWMA.
