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PMID: 42604806 Published · ppublish English

Preoperative Risk Stratification of Cerebral Infarction in Aortic Arch Surgery Using Quantitative Atheroma Assessment with Computational Fluid Dynamics.

Ikeuchi H, Kohno H, Matsuura K, Watanabe M, Inui T, Yakita Y, Matsumiya G

Abstract

We developed a quantitative method to evaluate aortic atheroma and investigated its association with postoperative cerebral infarction (CI) after aortic arch surgery. In addition, computational fluid dynamics (CFD) analysis was performed to explore the underlying hemodynamic mechanisms. We retrospectively analyzed 72 patients who underwent aortic arch surgery. Atheroma scores were calculated from preoperative computed tomography, and a qualitative assessment was also performed. Multivariate logistic regression identified risk factors for CI. CFD analysis was conducted in selected patients to provide mechanistic insights. CI occurred in 8 patients (11.1%). Atheroma scores were significantly higher in patients with CI (P = .02), whereas qualitative assessment did not predict CI. Receiver operating characteristic analysis showed an area under the curve of 0.79 for the ascending aorta, with a cutoff of 352.3. Multivariate analysis including age demonstrated that hemoglobin A1c and an ascending atheroma score ≥352.3 were independent predictors of CI. CFD demonstrated high wall shear stress in the ascending aorta, providing mechanistic insight into embolic events. Quantitative assessment of ascending aortic atheroma provides superior risk stratification compared with qualitative evaluation. CFD findings offer mechanistic insight into embolic risk, supporting the clinical relevance of atheroma burden in aortic arch surgery.

Keywords
aortic arch surgery atherosclerotic change computational fluid dynamics computed tomography postoperative cerebral infarction
Article Info
Journal
Annals of thoracic and cardiovascular surgery : official journal of the Association of Thoracic and Cardiovascular Surgeons of Asia
Abbr.
Ann Thorac Cardiovasc Surg
ISSN
2186-1005
Language
English
Country/Region
Japan
NLM ID
9703158
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