Clinical implications of exercise‐induced regional wall motion abnormalities in significant aortic regurgitation

2020 ◽  
Vol 37 (10) ◽  
pp. 1583-1593
Author(s):  
Ji‐won Hwang ◽  
Sung‐Ji Park ◽  
Eun Kyoung Kim ◽  
Sung‐A Chang ◽  
Jin‐Oh Choi ◽  
...  
Keyword(s):  
Aortic Regurgitation ◽  
Wall Motion ◽  
Regional Wall Motion ◽  
Clinical Implications ◽  
Regional Wall ◽  
Regional Wall Motion Abnormalities ◽  
Wall Motion Abnormalities ◽  
Exercise Induced

Spontaneous changes in regional wall motion abnormalities in acute myocardial infarction

1986 ◽  
Vol 58 (6) ◽  
pp. 406-410 ◽  
Author(s):  
Nagara Tamaki ◽  
Tsunehiro Yasuda ◽  
Robert C. Leinbach ◽  
Herman K. Gold ◽  
Kenneth A. McKusick ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Wall Motion ◽  
Regional Wall Motion ◽  
Regional Wall ◽  
Regional Wall Motion Abnormalities ◽  
Wall Motion Abnormalities

scholarly journals Editor’s Choice- Pathophysiology, diagnosis and management of MINOCA: an update

2018 ◽  
Vol 8 (1) ◽  
pp. 54-62 ◽  
Author(s):  
Giancarla Scalone ◽  
Giampaolo Niccoli ◽  
Filippo Crea
Keyword(s):  
Myocardial Infarction ◽  
Coronary Artery ◽  
Coronary Arteries ◽  
Wall Motion ◽  
Regional Wall Motion ◽  
Left Ventricular ◽  
Regional Wall ◽  
Diagnosis And Management ◽  
Regional Wall Motion Abnormalities ◽  
Wall Motion Abnormalities

Myocardial infarction with non-obstructive coronary arteries (MINOCA) is a syndrome with different causes, characterised by clinical evidence of myocardial infarction with normal or near-normal coronary arteries on angiography. Its prevalence ranges between 5% and 25% of all myocardial infarction. The prognosis is extremely variable, depending on the cause of MINOCA. The key principle in the management of this syndrome is to clarify the underlying individual mechanisms to achieve patient-specific treatments. Clinical history, electrocardiogram, cardiac enzymes, echocardiography, coronary angiography and left ventricular angiography represent the first level diagnostic investigations to identify the causes of MINOCA. Regional wall motion abnormalities at left ventricular angiography limited to a single epicardial coronary artery territory identify an ‘epicardial pattern’whereas regional wall motion abnormalities extended beyond a single epicardial coronary artery territory identify a ‘microvascular pattern’. The most common causes of MINOCA are represented by coronary plaque disease, coronary dissection, coronary artery spasm, coronary microvascular spasm, Takotsubo cardiomyopathy, myocarditis, coronary thromboembolism, other forms of type 2 myocardial infarction and MINOCA of uncertain aetiology. This review aims at summarising the diagnosis and management of MINOCA, according to the underlying physiopathology.

Predictors of Left Ventricular Regional Wall Motion Abnormalities After Subarachnoid Hemorrhage

2006 ◽  
Vol 4 (3) ◽  
pp. 199-205 ◽  
Author(s):  
Avinash Kothavale ◽  
Nader M. Banki ◽  
Alexander Kopelnik ◽  
Sirisha Yarlagadda ◽  
Michael T. Lawton ◽  
...  
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Wall Motion ◽  
Regional Wall Motion ◽  
Left Ventricular ◽  
Regional Wall ◽  
Regional Wall Motion Abnormalities ◽  
Wall Motion Abnormalities

Real-time myocardial perfusion contrast echocardiography and regional wall motion abnormalities after aneurysmal subarachnoid hemorrhage

2009 ◽  
Vol 111 (5) ◽  
pp. 1023-1028 ◽  
Author(s):  
Sahar S. Abdelmoneim ◽  
Eelco F. M. Wijdicks ◽  
Vivien H. Lee ◽  
Wilson P. Daugherty ◽  
Mathieu Bernier ◽  
...  
Keyword(s):  
Blood Flow ◽  
Cardiac Dysfunction ◽  
Wall Motion ◽  
Myocardial Dysfunction ◽  
Contrast Echocardiography ◽  
Regional Wall Motion ◽  
Regional Wall ◽  
Regional Wall Motion Abnormalities ◽  
Wall Motion Abnormalities

Object The pathophysiology of myocardial dysfunction after subarachnoid hemorrhage (SAH) remains unclear. Using myocardial real-time perfusion contrast echocardiography (RTP-CE), the authors evaluated microvascular function in patients with acute SAH. Methods Over a 15-month period, 10 patients with acute SAH and evidence of cardiac dysfunction were prospectively enrolled. The authors performed RTP-CE within 48 hours of SAH diagnosis. Wall motion and myocardial perfusion were evaluated in 16 left ventricle segments. Qualitative and quantitative RTP-CE analyses were conducted to compare patients with and without regional wall motion abnormalities (RWMAs). Follow-up RTP-CE at a mean of 53.7 ± 43 days was undertaken in patients with baseline RWMAs. Results Ten patients with SAH and evidence of cardiac dysfunction were prospectively enrolled. There were 3 men and 7 women whose mean age was 63.5 ± 10.1 years. The authors documented evidence of RWMAs in 6 patients. Normal perfusion was demonstrated by RTP-CE in all patients at baseline and follow-up, despite the presence of RWMAs. Compared with patients presenting with normal wall motion, in patients with RWMAs there was a trend for higher quantitative RTP-CE parameters, suggesting hyperemia with mean myocardial blood flow velocity (β, s−1) of 1.08 ± 0.61 (95% CI 0–2.61) compared with 1.62 ± 0.64 (95% CI 0.94–2.29) and myocardial blood flow (A × β, dB/s) of 0.99 ± 0.41 (95% CI 0–2.0) versus 1.63 ± 0.86 (95% CI 0.72–2.53). Follow-up RTP-CE was feasible in 3 patients with RWMAs. Regional systolic function was restored in those who completed follow-up. Conclusions The authors found that RTP-CE readily evaluates microvascular function in patients with SAH. Wall motion and perfusion dissociation were observed. Quantitative RTP-CE showed a trend for microvascular hyperemia in patients with RWMAs, suggesting that post-SAH myocardial dysfunction could occur in the absence of microvascular dysfunction.

4942Automated recognition of regional wall motion abnormalities by deep neural network interpretation of echocardiography

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
M S Huang ◽  
M R Tsai
Keyword(s):  
Neural Network ◽  
Left Ventricle ◽  
Deep Neural Network ◽  
Wall Motion ◽  
Three Dimensional ◽  
Regional Wall Motion ◽  
Regional Wall ◽  
Relationship Analysis ◽  
Regional Wall Motion Abnormalities ◽  
Wall Motion Abnormalities

Abstract Background The deep neural network assisted in automated echocardiography interpretation joint to cardiologist final confirmation has now been gradually emerging. There were applications applied in echocardiography views classification, chamber size and myocardium mass evaluation, and certain disease detections already published. Our aim, instead of frame-by-frame “image-level” interpretation in previous studies, is to apply deep neural network in echocardiography temporal relationship analysis – “video-level” – and applied in automated left ventricle myocardium regional wall motion abnormalities recognition. Methods We collected all echocardiography performed in 2017, and preprocessed them into numeric arrays for matrix computations. Regional wall motion abnormalities were approved by authorized cardiologists, and processed into labels whether regional wall motion abnormalities presented in anterior, inferior, septal, or lateral walls of the left ventricle, as the ground truth. We then first developed a convolutional neural network (CNN) model to do view selection, and gathered parasternal long/short views, and apical four/two chamber views from each exam, as well as developing view prediction confidence for strict image quality control. Within these images, we annotated part of images to develop the second CNN model, known as U-net, for image segmentation and mark each regional wall. Finally, we developed the major three-dimensional CNN model with the inputs composed of four views of echocardiography videos and then output the final label for motion abnormalities in each wall. Results In total we collected 13,984 series of echocardiography, and gathered four main views with quality confidence level above 90%, which resulted in 9,323 series for training. Within these images, we annotated 2,736 frames for U-net model and resulted in dice score of segmentation 73%. With the join of segmentation model, the final three-dimensional CNN model predict regional wall motion with accuracy of 83%. Conclusions Deep neural network application in regional wall motion recognition is feasible and should mandate further investigation for promoting performance. Acknowledgement/Funding None

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