Appropriate Patient Selection for Carotid Revascularization Procedures is Urgently Needed

The identification/selection of appropriate patient subgroups with asymptomatic carotid artery stenosis and the performance of prophylactic carotid endarterectomy (CEA)/carotid artery stenting (CAS) exclusively on these asymptomatic patient subgroups is currently one of the "hottest" topics in vascular surgery. It is now clear that offering CEA/CAS to asymptomatic carotid patients based only on the degree of carotid stenosis is unjustified and scientifically flawed. On the other hand, offering only best medical therapy to every asymptomatic patient, irrespective of certain high-risk criteria (such as the detection of microemboli by transcranial Doppler, intraplaque hemorrhage, silent embolic infarcts on brain computed tomography/magnetic resonance imaging, elevated biomarkers, family history), is equally wrong. The validation of specific measures to identify those asymptomatic patients at high risk for developing symptoms is crucial to achieve optimal use of carotid interventions and avoid wasting stroke prevention resources.

Risk analysis of carotid stent from a population-based database in Taiwan

Because stroke is the third leading disease that causes mortality in the world, the prevention of stroke from advanced carotid stenosis is an important issue. The carotid stent (CAS) is a less invasive to treat advanced carotid stenosis, but for high-risk patients it may cause some events after the procedure that reduces the benefit of stroke prevention. Because patients and their families have less information about risk of events after CAS and are easy concerned, this study calculates the individual probability of major adverse cardiovascular events including any stroke, myocardial infarction, or death after procedure.The analyzed dataset was composed of patients undergoing CAS from the longitudinal National Health Insurance claim database in Taiwan. The validation dataset was composed of patients undergoing CAS from the Tri-Service General Hospital. We excluded patients under 18 years of age. The prediction model was constructed with a multivariable Cox proportional hazard regression and performed with forward stepwise selection. The nomogram construction was based on the multivariable Cox model.The risk factors were determined as follows: age with a hazard ratio (HR) of 1.027 (95% confidence interval [CI]: 1.002-1.053) for every 1 year older, congestive heart failure with a HR of 2.196 (95% CI: 1.368-3.524), malignant disease with a HR of 1.724 (95% CI: 1.009-2.944), diabetes mellitus with a HR of 1.722 (95% CI: 1.109-2.674), and symptomatic status with a HR of 1.604 (95% CI: 1.027-2.507). The model showed good discrimination with a P < 0.001 (concordance index, 0.681; bootstrap corrected, 0.661) in the derivation data. The concordance index of external validation was 0.66 (P = 0.048), which indicates acceptable performance.We developed a nomogram with a visual scale method and prognostic information, and it is easy to use in clinical practice. The integer-base method may support communication between clinicians and patients before CAS to reduce the anxiety about making a treatment decision. However, insofar as older patients with multiple comorbidities are at high risk, the option of an alternative treatment strategy with medical therapy should be suggested. In the future, prospective tests should be performed to validate whether this model helps patients to prevent events.