Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.724787
Title: Risk stratification using non-invasive imaging in carotid artery disease and stroke
Author: Simpson, Richard
ISNI:       0000 0004 6425 8123
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2017
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Abstract:
Introduction: Carotid artery stenosis is associated with stroke and large randomised controlled trials (RCTs) have shown that carotid endarterectomy (CEA) can reduce subsequent stroke risk in those with symptomatic 50-99% carotid stenosis. However, not all patients benefit from CEA equally and therefore other methods of risk stratification are needed. Non-invasive plaque imaging with ultrasound (US) and Magnetic Resonance Imaging (MRI) have shown potential in this regard, but ultrasound is much cheaper and more widely available. Both are associated with plaque instability features on histology and relate to symptom status. There a number of studies in asymptomatic patient cohorts, but only one that shows echolucent plaques on US are a risk predictor for stroke and TIA. Further, there is limited longitudinal data on the predictive value of plaque echolucency or other US features in recently symptomatic patients. We have previously shown that the presence of hyperintense signal in the carotid artery on MRI relates to plaque haemorrhage (MIR-PH) and is a strong predictor of recurrent stroke. However, the temporal stability of the MIR-PH signal is not known for beyond 12 months and how it is linked stenosis progression. The aims of this thesis are to investigate US features for carotid plaque instability and if they can predict stroke in a cohort study of symptomatic patients. These were also compared to MRI carotid plaque imaging and a clinical risk score. Methods: Patients mild to moderate carotid stenosis, not planned for CEA, were recruited into a prospective cohort study. They had ultrasound plaque imaging was performed and five features were compared to risk markers for stroke in a cross-sectional design. The presence of plaque haemorrhage on MRI (MRI-PH) was determined and the Carotid Artery Risk (CAR) score calculated. Patients were followed up until the end of the study or event. In another cohort of patients, serial MRI and US scans were performed to determine the temporal stability of the MRI-PH signal and its effect on stenosis progression. Results: In a cross-sectional cohort study (91 symptomatic and 85 asymptomatic contralateral carotids) I showed that although the US plaque features were associated with symptom status, MRI-PH and a higher CAR score only one (Juxtaluminal Black Area - JBA) was predictive (OR =2.3 and 2.9 and 12.7, respectively). In the longitudinal study of 89 patients with a median of 905 days follow-up, the Greyscale Median (GSM) (log rank test: P=0.995) and JBA (log rank test: P=0.248) were not predictive of future cerebrovascular events. I found that the MRI-PH signal is generally stable over 24 months and MRI-PH negative showed in increase in hyperintense signal (P=0.020) with 7% becoming MRI-PH positive. In a cohort of 88 patients with > 50% carotid stenosis, there were 16 ipsilateral cerebrovascular events over a median follow-up of 1038 days. MRI-PH status predicted stroke (HR = 4.49 (95% CI 1.35-14.94, P=0.014). However the CAR score was not significantly predictive (HR = 1.272, 95% CI 0.410 – 3.951, P=0.677). Conclusions: This thesis shows that JBAs hold promise in being able to predict unstable plaques, but that in the small cohort study it was not predictive of cerebrovascular events. This is probably due to the small effect size and a low number of events recorded. Larger studies are required to properly test this finding. The MRI-PH is stable over a 2-year period and it may be associated with faster rates of stenosis progression. I found that MRI-PH is predictive of stroke in patients with >50% stenosis, but the CAR score is not.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.724787  DOI: Not available
Keywords: WL Nervous system
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