coronary artery stent
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2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
C. Huscher ◽  
G. Perri ◽  
G. Lazzarin ◽  
G. Marchegiani ◽  
G. Malleo ◽  
...  

2021 ◽  
Vol 8 ◽  
Author(s):  
Jue Yang ◽  
Xin Li ◽  
Zerui Chen ◽  
Tucheng Sun ◽  
Ruixin Fan ◽  
...  

For patients with acute type A aortic dissection, strongly suspected of having concomitant severe coronary artery disease (CAD), preoperative or intraoperative coronary angiography has been recommended. However, conventional selective coronary angiography in this setting may extend the dissection or aortic rupture. We present the use of intraoperative open-heart coronary angiography in a patient with acute type A aortic dissection. A 50-year-old man presented with chest pain and dyspnea and was admitted to our department with acute type A aortic dissection. The patient underwent coronary artery stent implantation in the left anterior descending coronary artery (LAD) 3 years previously due to an acute myocardial infarction. This time we failed to evaluate the patency of the LAD using multidetector computed tomography. An aortic rupture occurred due to conventional coronary angiography, and open-heart coronary angiography was performed. The examination revealed no significant stenosis. A Bentall procedure and total aortic arch replacement were performed, with an intraoperative stent inserted into the descending aorta, and the patient had an uneventful postoperative course. From this case, we learn that intraoperative open-heart coronary angiography is safe and effective in patients with acute type A aortic dissection.


2021 ◽  
Author(s):  
Anne Cornelissen ◽  
Atsushi Sakamoto ◽  
Yu Sato ◽  
Rika Kawakami ◽  
Masayuki Mori ◽  
...  

Major advances have been made in coronary artery stent technology over the last decades. Drug-eluting stents reduced in-stent restenosis and have shown better outcomes compared with bare metal stents, yet some limitations still exist to their use. Because they delay healing of the vessel wall, longer dual antiplatelet therapy is mandatory to mitigate against stent thrombosis and this limitation is most concerning in subjects at high risk for bleeding. The COBRA PzF nanocoated coronary stent has been associated with accelerated endothelialization relative to drug-eluting stents, reduced inflammation and thromboresistance in preclinical studies, suggesting more flexible dual antiplatelet therapy requirement with potential benefits especially in those at high bleeding risk. Here, we discuss the significance of COBRA PzF in light of recent experimental and clinical studies.


Author(s):  
Pérez-Asensio Ana ◽  
Maneiro Melón Nicolás Manuel ◽  
Nuche Berenguer Jorge ◽  
Huertas Nieto Sergio ◽  
Escribano Subías Pilar ◽  
...  

2021 ◽  
Vol 11 (15) ◽  
pp. 6986
Author(s):  
Elisabeth Pachl ◽  
Alireza Zamanian ◽  
Myriam Stieler ◽  
Calvin Bahr ◽  
Narges Ahmidi

The main intervention for coronary artery disease is stent implantation. We aim to predict post-intervention target lesion failure (TLF) months before its onset, an extremely challenging task in clinics. This post-intervention decision support tool helps physicians to identify at-risk patients much earlier and to inform their follow-up care. We developed a novel machine-learning model with three components: a TLF predictor at discharge via a combination of nine conventional models and a super-learner, a risk score predictor for time-to-TLF, and an update function to manage the size of the at-risk cohort. We collected data in a prospective study from 120 medical centers in over 25 countries. All 1975 patients were enrolled during Phase I (2016–2020) and were followed up for five years post-intervention. During Phase I, 151 patients (7.6%) developed TLF, which we used for training. Additionally, 12 patients developed TLF after Phase I (right-censored). Our algorithm successfully classifies 1635 patients as not at risk (TNR = 90.23%) and predicts TLF for 86 patients (TPR = 52.76%), outperforming its training by identifying 33% of the right-censored patients. We also compare our model against five state of the art models, outperforming them all. Our prediction tool is able to optimize for both achieving higher sensitivity and maintaining a reasonable size for the at-risk cohort over time.


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