Pre-operative Cerebral Small Vessel Disease on MR Imaging Is Associated With Cerebral Hyperperfusion After Carotid Endarterectomy
Objectives: To determine whether pre-operative cerebral small vessel disease is associated with cerebral hyperperfusion (CH) after carotid endarterectomy (CEA).Methods: Seventy-seven patients (mean age of 66 years and 58% male) undergoing CEA for carotid stenosis were investigated using brain MRI be...
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| Autores principales: | , , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Frontiers Media S.A.
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/df7c7a707d604c09a99b4a1c0bc03c43 |
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| Sumario: | Objectives: To determine whether pre-operative cerebral small vessel disease is associated with cerebral hyperperfusion (CH) after carotid endarterectomy (CEA).Methods: Seventy-seven patients (mean age of 66 years and 58% male) undergoing CEA for carotid stenosis were investigated using brain MRI before and after surgery. CH was defined as an increase in cerebral blood flow > 100% compared with pre-operative values on arterial spin labeling MR images. The grade or the number of four cerebral small vessel disease markers (white matter hyperintensities, lacunes, perivascular spaces, and cerebral microbleeds) were evaluated based on pre-operative MRI. Cerebral small vessel disease markers were correlated with CH by using multivariate logistic regression analysis. The cutoff values of cerebral small vessel disease markers for predicting CH were assessed by receiver-operating characteristic curve analysis.Results: CH after CEA was observed in 16 patients (20.78%). Logistic regression analysis revealed that white matter hyperintensities (OR 3.09, 95% CI 1.72–5.54; p < 0.001) and lacunes (OR 1.37, 95% CI 1.06–1.76; p = 0.014) were independently associated with post-operative CH. Receiver-operating characteristic curve analysis showed that Fazekas score of white matter hyperintensities ≥3 points [area under the curve (AUC) = 0.84, sensitivity = 81.3%, specificity = 73.8%, positive predictive value (PPV) = 44.8% and negative predictive value (NPV) = 93.8%] and number of lacunes ≥ 2 (AUC = 0.73, sensitivity = 68.8%, specificity = 78.7%, PPV = 45.8% and NPV = 90.6%) were the optimal cutoff values for predicting CH.Conclusion: In patients with carotid stenosis, white matter hyperintensities and lacunes adversely affect CH after CEA. Based on the NPVs, pre-operative MR imaging can help identify patients who are not at risk of CH. |
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