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Predictive factors driving positive awake test in carotid endarterectomy using machine learning

2025-02Artigo científico Acesso restrito
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datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg04:Educação de Qualidade
dc.contributor.authorPereira-Macedo, Juliana
dc.contributor.authorDuarte-Gamas, Luís
dc.contributor.authorPereira Pias, Ana Daniela
dc.contributor.authorMyrcha, Piotr
dc.contributor.authorAndrade, José P.
dc.contributor.authorAntónio, Nuno
dc.contributor.authorMarreiros, Ana
dc.contributor.authorRocha-Neves, João
dc.date.accessioned2026-04-29T10:05:21Z
dc.date.available2026-04-29T10:05:21Z
dc.date.issued2025-02
dc.description.abstractBackground: 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 (INDs). 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, 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 of 0.82, with an accuracy of 0.75, precision of 0.88, sensitivity of 0.59, and F1Score 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 hemoglobin 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.eng
dc.identifier.doi10.1016/j.avsg.2024.10.011
dc.identifier.issn0890-5096
dc.identifier.urihttp://hdl.handle.net/10400.1/28798
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationInformation Management Research Center
dc.relation.ispartofAnnals of Vascular Surgery
dc.rights.uriN/A
dc.titlePredictive factors driving positive awake test in carotid endarterectomy using machine learningeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.citation.endPage121
oaire.citation.startPage110
oaire.citation.titleAnnals of Vascular Surgery
oaire.citation.volume111
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNamePereira Pias
person.familyNameMarreiros
person.givenNameAna Daniela
person.givenNameAna
person.identifier.ciencia-id9A12-9450-7051
person.identifier.orcid0009-0002-0989-6598
person.identifier.orcid0000-0001-9410-4772
person.identifier.scopus-author-id57194785077
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication1707fc8b-5247-4e7f-bedf-7f1771565765
relation.isAuthorOfPublicationc0a8e5da-26ae-42a8-ab04-fa4df4356375
relation.isAuthorOfPublication.latestForDiscoveryc0a8e5da-26ae-42a8-ab04-fa4df4356375
relation.isProjectOfPublication26b45b04-11c7-4fd2-8cda-5dabb80d1a1a
relation.isProjectOfPublication.latestForDiscovery26b45b04-11c7-4fd2-8cda-5dabb80d1a1a

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