Structured AbstractObjectiveCarotid endarterectomy (CEA) and carotid artery stenting (CAS) are recommended for high stroke-risk patients with carotid artery stenosis to reduce ischemic events. However, we often face difficulty in determining the best treatment method. Therefore, it is necessary to develop a useful decision support tool to identify an appropriate patient-specific treatment for carotid artery stenosis. Our objective is to develop an accurate post-CEA/CAS outcome prediction model using machine learning (ML) algorithms that will serve as a basis for a new decision support tool for patient-specific treatment planning.MethodsRetrospectively collected data from 165 consecutive patients with carotid artery stenosis underwent CEA or CAS at a single institution were divided into training and test samples. The following six ML algorithms were tuned, and their predictive performance evaluated by comparison with surgeon predictions: an artificial neural network, logistic regression, support vector machine, Gaussian naïve Bayes, random forest, and extreme gradient boosting (XGBoost). A total of 17 clinical parameters were used for the ML model development. These parameters consisted of age, pretreatment modified Rankin scale, hypertension, diabetes mellitus, medical history of arteriosclerotic disease, serum low-density lipoprotein cholesterol value, internal carotid artery peak systolic velocity, symptomatic, crescendo transient ischemic attack or stroke in evolution, previous neck irradiation, type III aorta, contralateral carotid occlusion, stenosis at a high position, mobile plaque, plaque ulceration, vulnerable plaque, and procedure (CEA or CAS). Outcome was defined as any ischemic stroke within 30 days after treatment.ResultsThe XGBoost model performed the best in the evaluation; its sensitivity, specificity, positive predictive value, and accuracy were 66.7%, 89.5%, 50.0%, and 86.4%, respectively. The average of the outcome predictions made by four surgeons had a sensitivity of 41.7%, specificity of 75.0%, positive predictive value of 20.1%, and accuracy of 70.5%. Internal carotid artery peak systolic velocity, serum low density lipoprotein cholesterol, and procedure (CEA or CAS) were the most contributing factors according to the XGBoost algorithm.ConclusionsWe were able to develop a post-CEA/CAS outcome prediction model comparable to surgeons in performance. The accurate outcome prediction model will make it possible to make a more appropriate patient-specific selection of CEA or CAS for the treatment of carotid artery stenosis.
Peak systolic velocity ratio as a new parameter for carotid artery stenosis grading
