医学
罪魁祸首
血管内超声
心脏病学
病变
不利影响
内科学
靶病变
心肌梗塞
急性冠脉综合征
动脉
放射科
经皮冠状动脉介入治疗
病理
作者
Brian C. Case,Corey Shea,Rebecca Torguson,Cheng Zhang,Charan Yerasi,Giorgio A. Medranda,Kayode O. Kuku,Héctor M. García‐García,Gary S. Mintz,Ron Waksman
标识
DOI:10.1016/j.carrev.2021.12.012
摘要
Intravascular ultrasound (IVUS) and near-infrared spectroscopy (NIRS) can identify vulnerable coronary atherosclerotic plaques. We aimed to compare the presence or absence of baseline intravascular imaging of non-culprit lesions and their subsequent adverse events.We identified patients from the Lipid Rich Plaque (LRP) study who had a non-culprit-lesion adverse event and divided them into 2 cohorts: those with lesions detected with NIRS-IVUS imaging at baseline and those with lesions not imaged at baseline.Overall, 73 patients had an adverse event (99 coronary segments) during the 24-month follow-up period. Among them, 41 patients (56.2%) had a non-culprit-lesion adverse event related to a coronary segment imaged at baseline, and 32 patients (43.8%) had a non-culprit-lesion adverse event adjudicated to a segment that was not scanned at baseline. Angiographic core laboratory analysis suggested that unscanned lesions were more often in the right coronary artery (~50%); branches of the left coronary artery, i.e., diagonal or left obtuse marginal arteries (~20%); smaller vessels; or more tortuous vessels; and less often in the left anterior descending or distal locations. There was a weak trend for acute severe events (adjudicated myocardial infarction and acute coronary syndrome) in patients with lesions not scanned at baseline (50.0% versus 36.6%, p = 0.250).In patients with follow-up non-culprit-lesion adverse events, nearly half were not imaged with NIRS-IVUS at baseline. Because events related to non-imaged lesions were at least as severe as events related to imaged lesions, future clinical trials and clinical protocols should be designed to minimize this issue.The Lipid-Rich Plaque Study (LRP), https://clinicaltrials.gov/ct2/show/NCT02033694, NCT02033694.
科研通智能强力驱动
Strongly Powered by AbleSci AI