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Carotid 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 strategy. We aimed to develop an accurate post-CEA/CAS outcome prediction model using machine learning that will serve as a basis for a new decision support tool for patient-specific treatment planning. Retrospectively collected data from 165 consecutive patients with carotid stenosis underwent CEA or CAS and were divided into training and test samples. The following five machine learning algorithms were tuned, and their predictive performance was evaluated by comparison with surgeon predictions: an artificial neural network, logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost). Seventeen clinical factors were introduced into the models. Outcome was defined as any ischemic stroke within 30 days after treatment including asymptomatic diffusion-weighted imaging abnormalities. The XGBoost model performed the best in the evaluation; its sensitivity, specificity, positive predictive value, and accuracy were 31.9%, 94.6%, 47.2%, and 86.2%, respectively. These statistical measures were comparable to those of surgeons. Internal carotid artery peak systolic velocity, low-density lipoprotein cholesterol, and procedure (CEA or CAS) were the most contributing factors according to the XGBoost algorithm. We were able to develop a post-procedural 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.

Potential of machine learning to predict early ischemic events after carotid endarterectomy or stenting: a comparison with surgeon predictions