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For a long time, it has been hoped that doctors could screen and diagnosis of coronary heart disease through non-invasive imaging techniques, so as to maximize the benefit/risk ratio of patients. This trial is to explore the screening and diagnostic value of three-dimensional speckle tracking technology for coronary heart disease, and the evaluation value of 3D speckle track image(3D-STI) technology for cardiac function improvement after coronary intervention, and to seek reliable, accurate and quantifiable non-invasive imaging examination for the diagnosis, follow-up and prognosis of coronary heart disease.
In this study, coronary angiography is taken as the "gold standard", and 3d-STI echocardiography technology is proposed to combine with clinical characteristics of subjects for joint diagnosis, so as to evaluate the value of 3d-sti technology in the diagnosis of coronary heart disease.
Patients were followed up with echocardiography after interventional treatment to explore the feasibility of 3D-STI technology in evaluating cardiac function improvement.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| coronary artery disease | Patients who were diagnosed with coronary heart disease at admission and planned to undergo coronary angiography |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 3D-speckle tracking imaging | Diagnostic Test | The strain peak value and strain peaking time of each segment (including 16 segments) of the left ventricular myocardium can be obtained by 3d-sti technology. |
| Measure | Description | Time Frame |
|---|---|---|
| Left ventricular myocardial viability | By measuring the strain and time to peak, the left ventricular myocardial viability was finally evaluated. Left ventricular local ventricular viable myocardium was quantitatively analyzed in patients .Aimed to detect the myocardial ischaemia and viability. The data of strain and time to peak were measured using the three-dimensional speckle tracking model. In apical four-chamber view, make sure the endocardium, mitral valve ring and apex imaging is clear, mark the left and right mitral valve ring and apex spot . After automatic ultrasonic echo spot tracked, software can delineate endocardial curve, and automaticaly calculate the left ventricular myocardial segments strain and time to peak. The strain peaks of each segment (including 16 segments) of the myocardium of the left ventricle included four strain peaks: RS, CS, LS and AS | half an hour |
| Measure | Description | Time Frame |
|---|---|---|
| The value of 3D-STI technology on the improvement of left ventricular myocardial function | During the 6-month follow-up, 3D-STI technique was used to evaluate the improvement of left ventricular myocardial function compared with conventional parameters like LVEF and NYHA. 3D-STI parameters are the same : strain and peak time . | half a year |
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Inclusion Criteria:
Exclusion Criteria:
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According to reference, the area under the ROC curve of 3d-STI strain peak and strain peaking time for the diagnosis of coronary heart disease is about 0.65.In this study, it is expected that the combined diagnosis model can increase the area under the ROC curve to the level of 0.85, and the standard deviation of the area under the ROC curve is estimated to be 0.5. In order to get 85% assurance, we plan to include 827 subjects.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Beijing Hospital | Recruiting | Beijing | Beijing Municipality | 100730 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41455557 | Derived | Guo Y, Song X, Xu T, Guo H, Yang C, Zhong Y, Li H, Gong J, Wang F. Utilizing machine learning in echocardiographic analysis to distinguish obstructive and non-obstructive coronary artery disease. Int J Cardiol. 2026 Mar 15;447:134121. doi: 10.1016/j.ijcard.2025.134121. Epub 2025 Dec 25. | |
| 40787200 | Derived | Guo Y, Cai YH, Xu T, Song XY, Guo HX, Dong M, Ni D, Li H, Wang F, Xue WF. Echocardiographic video-driven multi-task learning model for coronary artery disease diagnosis and severity grading. Front Bioeng Biotechnol. 2025 Jul 25;13:1556748. doi: 10.3389/fbioe.2025.1556748. eCollection 2025. |
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| ID | Term |
|---|---|
| D003324 | Coronary Artery Disease |
| ID | Term |
|---|---|
| D003327 | Coronary Disease |
| D017202 | Myocardial Ischemia |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
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| 37170232 | Derived | Guo Y, Xia C, Zhong Y, Wei Y, Zhu H, Ma J, Li G, Meng X, Yang C, Wang X, Wang F. Machine learning-enhanced echocardiography for screening coronary artery disease. Biomed Eng Online. 2023 May 11;22(1):44. doi: 10.1186/s12938-023-01106-x. |
| 36899310 | Derived | Guo Y, Wang X, Yang CG, Meng XY, Li Y, Xia CX, Xu T, Weng SX, Zhong Y, Zhang RS, Wang F. Noninvasive assessment of myocardial work during left ventricular isovolumic relaxation in patients with diastolic dysfunction. BMC Cardiovasc Disord. 2023 Mar 10;23(1):129. doi: 10.1186/s12872-023-03156-4. |
| D001161 |
| Arteriosclerosis |
| D001157 | Arterial Occlusive Diseases |
| D014652 | Vascular Diseases |