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The coronary artery system is composed of three different types of blood vessels, namely epicardial arteries, arterioles and capillaries. Compared with epicardial arteries, arterioles and capillaries are lower than the resolution of current angiography systems, so angiography cannot be used for visualization. Existing studies have shown that coronary microcirculation plays an extremely important role in maintaining full myocardial perfusion. Coronary microvascular disorders can lead to myocardial hypoperfusion and ischemia, and are related to the poor prognosis of patients with coronary heart disease. At present, there is no technology that can directly detect the state of the coronary microcirculation in the human body, but the coronary microcirculation function can be indirectly assessed through two invasive and non-invasive methods. Among them, the index of microcirculation resistance (IMR) is widely used to evaluate coronary microcirculation function [3]. However, in the case of epicardial stenosis, accurate determination of IMR requires knowledge of coronary artery contraction pressure (Pw). However, measuring IMR is an invasive examination technique, and measuring IMR requires high technical requirements for the operator. Therefore, the CaIMR value obtained by AI technology can well overcome this limitation, and the existing data show that the CaIMR value has a good correlation with the IMR value. However, CaIMR has a clinical prognosis for patients with acute ST-segment elevation myocardial infarction. The predictive value of CaIMR has not yet been explored. This project aims to evaluate the application value of CaIMR in predicting the occurrence of adverse cardiovascular events in patients with acute ST-segment elevation myocardial infarction after percutaneous coronary intervention.
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Zhibing Lu, MD, PhD
Data sourced from clinicaltrials.gov
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