Drug-eluting coronary artery stents

Scott and White Healthcare, 2401 South 31 St., Temple, TX 76508, USA.
American family physician (Impact Factor: 2.18). 12/2009; 80(11):1245-51.
Source: PubMed


Many advances have been made in the percutaneous treatment of coronary artery disease during the past 30 years. Although balloon angioplasty alone is still performed, the use of coronary artery stents is much more common. Approximately 40 percent of patients treated with balloon angioplasty developed restenosis, and this was reduced to roughly 30 percent with the use of bare-metal stents. However, restenosis within the stent can occur and is difficult to treat. Drug-eluting stents were developed to lower the rate of restenosis, which now occurs in less than 10 percent of patients treated with these stents. There have been concerns about abrupt thrombosis within drug-eluting stents occurring late after their implantation, leading to acute myocardial infarction and death. Recent studies have alleviated, but not completely dispelled, these concerns. Strict adherence to dual antiplatelet therapy with aspirin and a thienopyridine is required after stent placement, and the premature discontinuation of therapy is the most important risk factor for acute stent thrombosis. Adequate communication between cardiologists and primary care physicians is essential not only to avoid the premature discontinuation of therapy, but also to identify, before stent placement, those patients in whom prolonged antiplatelet therapy may be ill-advised. Elective surgery following stent placement should be delayed until the recommended course of dual antiplatelet therapy has been completed.

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    • "Restenosis usually occurs within three to nine months after PCI [3]. PCI restenosis rate without stenting range between 20% and 65%, depending on the method of follow-up and the criteria used to define restenosis [3-9]. Successfully dilated total coronary occlusions have a higher rate of angiographic restenosis at 6 months than dilated stenoses [13, 14]. "
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    ABSTRACT: Coronary artery disease (CAD) is the most prevalent form of cardiovascular disease affecting about 13 million Americans, while more than one million percutaneous transluminal intervention (PCI) procedures are performed annually in the USA. The relative high occurrence of restenosis, despite stent implementation, seems to be the primary limitation of PCI. Over the last decades, single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI), has proven an invaluable tool for the diagnosis of CAD and patients’ risk stratification, providing useful information regarding the decision about revascularization and is well suited to assess patients after intervention. Information gained from post-intervention MPI is crucial to differentiate patients with angina from those with exo-cardiac chest pain syndromes, to assess peri-intervention myocardial damage, to predict-detect restenosis after PCI, to detect CAD progression in non-revascularized vessels, to evaluate the effects of intervention if required for occupational reasons and to evaluate patients’ long-term prognosis. On the other hand, chest pain and exercise electrocardiography are largely unhelpful in identifying patients at risk after PCI. Although there are enough published data demonstrating the value of myocardial perfusion SPECT imaging in patients after PCI, there is still debate on whether or not these tests should be performed routinely.
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    ABSTRACT: Computational modelling of physical and biochemical processes has emerged as a means of evaluating medical devices, offering new insights that explain current performance, inform future designs and even enable personalized use. Yet resource limitations force one to compromise with reduced order computational models and idealized assumptions that yield either qualitative descriptions or approximate, quantitative solutions to problems of interest. Considering endovascular drug delivery as an exemplary scenario, we used a supervised machine learning framework to process data generated from low fidelity coarse meshes and predict high fidelity solutions on refined mesh configurations. We considered two models simulating drug delivery to the arterial wall: (i) two-dimensional drug-coated balloons and (ii) three-dimensional drug-eluting stents. Simulations were performed on compu-tational mesh configurations of increasing density. Supervised learners based on Gaussian process modelling were constructed from combinations of coarse mesh setting solutions of drug concentrations and nearest neigh-bourhood distance information as inputs, and higher fidelity mesh solutions as outputs. These learners were then used as computationally inexpensive sur-rogates to extend predictions using low fidelity information to higher levels of mesh refinement. The cross-validated, supervised learner-based predictions improved fidelity as compared with computational simulations performed at coarse level meshes—a result consistent across all outputs and compu-tational models considered. Supervised learning on coarse mesh solutions can augment traditional physics-based modelling of complex physiologic phenomena. By obtaining efficient solutions at a fraction of the computational cost, this framework has the potential to transform how modelling approaches can be applied in the evaluation of medical technologies and their real-time administration in an increasingly personalized fashion.
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