Predictive Factors Driving Positive Awake Test in Carotid Endarterectomy Using Machine Learning

Pereira-Macedo, Juliana; Pias, Ana Daniela; Duarte-Gamas, Luís; Myrcha, Piotr; Andrade, José P.; António, Nuno; Marreiros, Ana; Rocha-Neves, João

http://hdl.handle.net/10362/176149

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Autores

Pereira-Macedo, Juliana

Resumo(s)

OBJECTIVES Positive neurologic awake testing during the carotid cross-clamping may be present in around 8% of patients undergoing carotid endarterectomy (CEA). The present work aimed to assess the accuracy of an artificial intelligence (AI)-powered risk calculator in predicting intraoperative neurologic deficits (IND). METHODS Data was collected from carotid interventions performed between January 2012 and January 2023 under regional anesthesia. Patients with IND were selected along with consecutive controls without IND in a case-control study design. A predictive model for IND was developed using machine learning (ML), specifically Extreme Gradient Boosting (XGBoost) model, and its performance was assessed and compared to an existing predictive model. Shapley Additive exPlanations (SHAP) analysis was employed for the model interpretation. RESULTS Among 216 patients, 108 experienced IND during CEA. The AI-based predictive model achieved a robust area under the curve (AUC) of 0.82, with an accuracy of 0.71. High body mass index (BMI) increased contralateral carotid stenosis, and a history of limb paresis or plegia were significant IND risk factors. Elevated preoperative platelet and haemoglobin levels were associated with reduced IND risk. CONCLUSIONS This AI model provides precise IND prediction in CEA, enabling tailored interventions for high-risk patients and ultimately improving surgical outcomes. BMI, contralateral stenosis, and selected blood parameters emerged as pivotal predictors, bringing significant advancements to decision-making in CEA procedures. Further validation in larger cohorts is essential for broader clinical implementation.

Descrição

Pereira-Macedo, J., Pias, A. D., Duarte-Gamas, L., Myrcha, P., Andrade, J. P., António, N., Marreiros, A., & Rocha-Neves, J. (2025). Predictive Factors Driving Positive Awake Test in Carotid Endarterectomy Using Machine Learning. Annals of vascular surgery, 111, 110-121. https://doi.org/10.1016/j.avsg.2024.10.011 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS) (https://doi.org/10.54499/UIDB/04152/2020)

Palavras-chave

Carotid stenosis intraoperative neurologic deficits perioperative stroke regional anesthesia Surgery Cardiology and Cardiovascular Medicine SDG 3 - Good Health and Well-being