Cholesterol Crystals in Embolic Debris are Associated with Postoperative Cerebral Embolism after Carotid Artery Stenting

Maekawa, Kota; Shibata, Masunari; Nakajima, Hideki; Kitano, Yasushi; Seguchi, Masaru; Kobayashi, Kazuto; Sano, Takanori; Yabana, Tadashi; Miya, Fumitaka

doi:10.1159/000495795

Cited by 3 publications

(1 citation statement)

References 19 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ICA-PSV might be used in our model as a valid predictor in combination with other clinical factors. In a study investigating the components of emboli captured by the filter-protection device during CAS, more postoperative DWI high intensity lesions were observed in patients whose main component of the emboli was cholesterol [17]. It may be inferred from such results that serum LDL-cholesterol might be related to post-procedural outcome because it should be possible for a higher serum LDL-cholesterol value to leads to a higher cholesterol content of the plaque, which produces emboli.…”

Section: Feature Importancementioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

BackgroundCarotid 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. ObjectiveWe 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. MethodsRetrospectively collected data from 165 consecutive patients with carotid stenosis underwent CEA or CAS were divided into training and test samples. The following five machine learning algorithms were tuned, and their predictive performance 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. ResultsThe 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. Conclusion 3We 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.

show abstract

Section: Feature Importancementioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

Prediction of new cerebral ischemic lesion after carotid artery stenting: a high-resolution vessel wall MRI-based radiomics analysis

Zhang

et al. 2022

Eur Radiol

View full text Add to dashboard Cite

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

Matsuo¹,

Fujita²,

Hosoda

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Cholesterol Crystals in Embolic Debris are Associated with Postoperative Cerebral Embolism after Carotid Artery Stenting

Cited by 3 publications

References 19 publications

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

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

Prediction of new cerebral ischemic lesion after carotid artery stenting: a high-resolution vessel wall MRI-based radiomics analysis

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

Contact Info

Product

Resources

About