Multimodal monitoring of cerebral perfusion in carotid endarterectomy patients: a computational fluid dynamics study

ObjectiveTo evaluate postoperative cerebral perfusion changes and their influencing factors in carotid endarterectomy (CEA) patients by integrating multimodal monitoring methods, including cerebral regional oxygen saturation (rSO2), carotid ultrasound (CU), computed tomographic angiography (CTA), an...

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Main Authors: Lei Guo, Jun Zhang, Kai Lv, Xiong Li, Meiling Guo, Chunling Li
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-12-01
Series:Frontiers in Neurology
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Online Access:https://www.frontiersin.org/articles/10.3389/fneur.2024.1455401/full
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author Lei Guo
Lei Guo
Jun Zhang
Kai Lv
Xiong Li
Meiling Guo
Chunling Li
author_facet Lei Guo
Lei Guo
Jun Zhang
Kai Lv
Xiong Li
Meiling Guo
Chunling Li
author_sort Lei Guo
collection DOAJ
description ObjectiveTo evaluate postoperative cerebral perfusion changes and their influencing factors in carotid endarterectomy (CEA) patients by integrating multimodal monitoring methods, including cerebral regional oxygen saturation (rSO2), carotid ultrasound (CU), computed tomographic angiography (CTA), and computed tomographic perfusion imaging (CTP), with computational fluid dynamics (CFD) assessment.MethodsWe conducted a cohort study on patients with internal carotid artery (ICA) stenosis undergoing CEA at our institution. Pre- and postoperative assessments included CU, CTA, CTP, and rSO2 monitoring. Hemodynamic parameters recorded were mean flow velocity (MFV), peak systolic velocity (PSV), end diastolic velocity (EDV), resistance index (RI), rSO2, and cerebral blood flow (CBF). CFD quantified the total pressure (TP), wall shear stress (WSS), wall shear stress ratio (WSSR), and translesional pressure ratio (PR) of the ICA. Pearson correlation was used to analyze factors influencing cerebral perfusion changes. Multivariate logistic regression identified risk factors for cerebral hyperperfusion (CH). The predictive value of multimodal and single-modality monitoring for CH was evaluated using ROC curve analysis.ResultsFifty-six patients were included, with nine developing postoperative CH. CU showed significant reductions in MFV, PSV, EDV, and RI of the ICA (p < 0.001). Ipsilateral rSO2 increased significantly (p = 0.013), while contralateral rSO2 showed no significant change (p = 0.861). CFD revealed significant decreases in TP, WSS, and WSSR (p < 0.001), along with a significant increase in PR (p < 0.001). Pearson analysis indicated that change rate of CBF (ΔCBF) positively correlated with ΔPR and ΔrSO2, and negatively correlated with ΔTP, ΔWSS, and Δ WSSR. Multivariate logistic regression identified preoperative WSSR (pre-WSSR) and ΔPR as risk factors for CH following CEA. Combined ΔPR, ΔrSO2, ΔMFV, and pre-WSSR had higher sensitivity and specificity than single-modality monitoring for predicting CH.ConclusionCFD-based multimodal monitoring effectively identified cerebral perfusion changes and risk factors for CH in CEA patients, with superior predictive accuracy compared to single-modality methods. Nevertheless, further validation is necessary to establish its clinical utility.
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spelling doaj-art-f0aa6f53c5c14eda8c91de98b434cced2024-12-05T05:10:04ZengFrontiers Media S.A.Frontiers in Neurology1664-22952024-12-011510.3389/fneur.2024.14554011455401Multimodal monitoring of cerebral perfusion in carotid endarterectomy patients: a computational fluid dynamics studyLei Guo0Lei Guo1Jun Zhang2Kai Lv3Xiong Li4Meiling Guo5Chunling Li6Department of Neurosurgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Neurology, Xindu District People's Hospital of Chengdu, Chengdu, ChinaDepartment of Neurology, Xindu District People's Hospital of Chengdu, Chengdu, ChinaSchool of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Neurosurgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Neurosurgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Neurosurgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, ChinaObjectiveTo evaluate postoperative cerebral perfusion changes and their influencing factors in carotid endarterectomy (CEA) patients by integrating multimodal monitoring methods, including cerebral regional oxygen saturation (rSO2), carotid ultrasound (CU), computed tomographic angiography (CTA), and computed tomographic perfusion imaging (CTP), with computational fluid dynamics (CFD) assessment.MethodsWe conducted a cohort study on patients with internal carotid artery (ICA) stenosis undergoing CEA at our institution. Pre- and postoperative assessments included CU, CTA, CTP, and rSO2 monitoring. Hemodynamic parameters recorded were mean flow velocity (MFV), peak systolic velocity (PSV), end diastolic velocity (EDV), resistance index (RI), rSO2, and cerebral blood flow (CBF). CFD quantified the total pressure (TP), wall shear stress (WSS), wall shear stress ratio (WSSR), and translesional pressure ratio (PR) of the ICA. Pearson correlation was used to analyze factors influencing cerebral perfusion changes. Multivariate logistic regression identified risk factors for cerebral hyperperfusion (CH). The predictive value of multimodal and single-modality monitoring for CH was evaluated using ROC curve analysis.ResultsFifty-six patients were included, with nine developing postoperative CH. CU showed significant reductions in MFV, PSV, EDV, and RI of the ICA (p < 0.001). Ipsilateral rSO2 increased significantly (p = 0.013), while contralateral rSO2 showed no significant change (p = 0.861). CFD revealed significant decreases in TP, WSS, and WSSR (p < 0.001), along with a significant increase in PR (p < 0.001). Pearson analysis indicated that change rate of CBF (ΔCBF) positively correlated with ΔPR and ΔrSO2, and negatively correlated with ΔTP, ΔWSS, and Δ WSSR. Multivariate logistic regression identified preoperative WSSR (pre-WSSR) and ΔPR as risk factors for CH following CEA. Combined ΔPR, ΔrSO2, ΔMFV, and pre-WSSR had higher sensitivity and specificity than single-modality monitoring for predicting CH.ConclusionCFD-based multimodal monitoring effectively identified cerebral perfusion changes and risk factors for CH in CEA patients, with superior predictive accuracy compared to single-modality methods. Nevertheless, further validation is necessary to establish its clinical utility.https://www.frontiersin.org/articles/10.3389/fneur.2024.1455401/fullcarotid artery stenosiscarotid endarterectomymultimodal monitoringcerebral hyperperfusioncomputational fluid dynamics
spellingShingle Lei Guo
Lei Guo
Jun Zhang
Kai Lv
Xiong Li
Meiling Guo
Chunling Li
Multimodal monitoring of cerebral perfusion in carotid endarterectomy patients: a computational fluid dynamics study
Frontiers in Neurology
carotid artery stenosis
carotid endarterectomy
multimodal monitoring
cerebral hyperperfusion
computational fluid dynamics
title Multimodal monitoring of cerebral perfusion in carotid endarterectomy patients: a computational fluid dynamics study
title_full Multimodal monitoring of cerebral perfusion in carotid endarterectomy patients: a computational fluid dynamics study
title_fullStr Multimodal monitoring of cerebral perfusion in carotid endarterectomy patients: a computational fluid dynamics study
title_full_unstemmed Multimodal monitoring of cerebral perfusion in carotid endarterectomy patients: a computational fluid dynamics study
title_short Multimodal monitoring of cerebral perfusion in carotid endarterectomy patients: a computational fluid dynamics study
title_sort multimodal monitoring of cerebral perfusion in carotid endarterectomy patients a computational fluid dynamics study
topic carotid artery stenosis
carotid endarterectomy
multimodal monitoring
cerebral hyperperfusion
computational fluid dynamics
url https://www.frontiersin.org/articles/10.3389/fneur.2024.1455401/full
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