Lei Guo ¹ Jun Zhang ² Kai Lv ³ Xiong Li ¹ Meiling Guo ¹ Chunling Li ^1*

• ¹ Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China
• ² Department of Neurology, Chengdu Second People's Hospital, Chengdu, China
• ³ University of Electronic Science and Technology of China, Chengdu, China

Objective: To 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. Methods: We 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. Results: Fifty-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. Conclusion: CFD-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.