Key result
A risk prediction model incorporating age, sex, timing of surgery, and plaque morphology is considered to optimize patient selection for carotid endarterectomy in symptomatic carotid stenosis.
Why the study?
Does a risk prediction model help identify patients with symptomatic carotid stenosis most at risk of stroke?
Does a risk prediction model help identify patients with symptomatic carotid stenosis most at risk of stroke?
A risk prediction model incorporating multiple clinical and morphological factors may help optimize patient selection for carotid endarterectomy.
May refine CEA selection in symptomatic stenosis; leaves open prospective validation of integrated models.
We have reliable data on the degree of stenosis above which endarterectomy for symptomatic carotid stenosis is beneficial, but benefit is also influenced by other factors, particularly age, sex, the timing of surgery, plaque surface morphology and the nature of the presenting symptomatic event(s). This review will consider the selection of patients for carotid surgery based on the factors that influence the likely risk of stroke on medical treatment. In order to take into account all of the relevant factors, a risk prediction model is considered.
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Peter M. Rothwell (2005) conducted a review in Symptomatic carotid stenosis. Risk prediction model was evaluated on Risk of stroke. A risk prediction model incorporating age, sex, timing of surgery, and plaque morphology is considered to optimize patient selection for carotid endarterectomy in symptomatic carotid stenosis.
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