Carotid Ultrasound for Assessment of Nonobstructive Carotid Atherosclerosis in Young Adults with Cryptogenic Stroke

Buon, Raphael; Guidolin, Brigitte; Jaffre, Aude; Lafuma, Marie; Barbieux, Marianne; Nasr, Nathalie; Larrue, Vincent

doi:10.1016/j.jstrokecerebrovasdis.2017.11.043

Cited by 29 publications

(19 citation statements)

References 22 publications

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“…During recent years, there has been emerging evidence supporting an important etiological association between ESUS and atherosclerotic plaques. A recent analysis of the NAVIGATE‐ESUS (New Approach Rivaroxaban Inhibition of Factor Xa in a Global Trial versus ASA to Prevent Embolism in Embolic Stroke of Undetermined Source) trial [27] as well as several other studies [28–40] showed that the prevalence of carotid plaques is higher ipsilateral to the infarct than contralateral in patients with ESUS. In addition, the AF‐ESUS (Prediction of Atrial Fibrillation after Embolic Stroke of Undetermined Source) study showed that new incident AF is less frequently detected in patients with ESUS and carotid plaques compared to those without [18].…”

Section: Discussionmentioning

confidence: 99%

“…Trial versus ASA to Prevent Embolism in Embolic Stroke of Undetermined Source) trial [27] as well as several other studies [28][29][30][31][32][33][34][35][36][37][38][39][40] showed that the prevalence of carotid plaques is higher ipsilateral to the infarct than contralateral in patients with ESUS. In addition, the AF-ESUS (Prediction of Atrial Fibrillation after Embolic Stroke of Undetermined Source) study showed that new incident AF is less frequently detected in patients with ESUS and carotid plaques compared to those without [18].…”

Section: During Recent Years There Has Been Emerging Evidence Suppormentioning

confidence: 99%

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Data‐driven machine‐learning analysis of potential embolic sources in embolic stroke of undetermined source

Ntaios

Weng

Perlepe

et al. 2020

Euro J of Neurology

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Background and purpose: Hierarchical clustering, a common 'unsupervised' machine-learning algorithm, is advantageous for exploring potential underlying aetiology in particularly heterogeneous diseases. We investigated potential embolic sources in embolic stroke of undetermined source (ESUS) using a data-driven machine-learning method, and explored variation in stroke recurrence between clusters. Methods: We used a hierarchical k-means clustering algorithm on patients' baseline data, which assigned each individual into a unique clustering group, using a minimum-variance method to calculate the similarity between ESUS patients based on all baseline features. Potential embolic sources were categorised into atrial cardiopathy, atrial fibrillation, arterial disease, left ventricular disease, cardiac valvulopathy, patent foramen ovale (PFO) and cancer. Results: Among 800 consecutive ESUS patients (43.3% women, median age 67 years), the optimal number of clusters was four. Left ventricular disease was most prevalent in cluster 1 (present in all patients) and perfectly associated with cluster 1. PFO was most prevalent in cluster 2 (38.9% of patients) and associated significantly with increased likelihood of cluster 2 [adjusted odds ratio: 2.69, 95% confidence interval (CI): 1.64-4.41]. Arterial disease was most prevalent in cluster 3 (57.7%) and associated with increased likelihood of cluster 3 (adjusted odds ratio: 2.21, 95% CI: 1.43-3.13). Atrial cardiopathy was most prevalent in cluster 4 (100%) and perfectly associated with cluster 4. Cluster 3 was the largest cluster involving 53.7% of patients. Atrial fibrillation was not significantly associated with any cluster. Conclusions: This data-driven machine-learning analysis identified four clusters of ESUS that were strongly associated with arterial disease, atrial cardiopathy, PFO and left ventricular disease, respectively. More than half of the patients were assigned to the cluster associated with arterial disease.

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Section: Discussionmentioning

confidence: 99%

Section: During Recent Years There Has Been Emerging Evidence Suppormentioning

confidence: 99%

Data‐driven machine‐learning analysis of potential embolic sources in embolic stroke of undetermined source

Ntaios

Weng

Perlepe

et al. 2020

Euro J of Neurology

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“…Of note, the ACSRC study included ICA-stenosis of 12–99%, whereas our study was restricted to ICA-stenosis of 20–40%. Moreover, previous studies reported that plaque thickness, length, and volume were more prominent on the ipsilateral side among patients with stroke (2, 4). Similarly, another study showed that carotid plaque volume of the endarterectomy specimens was larger in symptomatic than in asymptomatic ICA-stenosis (mean, 0.97 vs. 0.74 cm 3 , p < 0.001) (16).…”

Section: Discussionmentioning

confidence: 88%

Symptomatic vs. Asymptomatic 20–40% Internal Carotid Artery Stenosis: Does the Plaque Size Matter?

2019

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Background: Around 9–15% of ischemic strokes are related to internal carotid artery (ICA)-stenosis ≥50%. However, the extent to which ICA-stenosis <50% causes ischemic cerebrovascular events is uncertain. We examined the relation between plaque cross-sectional area and length and the risk of ischemic stroke or TIA among patients with ICA-stenosis of 20–40%.Methods: We retrospectively identified patients admitted to the Department of Neurology, University Hospital of Würzburg, from January 2011 until September 2016 with ischemic stroke or TIA and concomitant ICA-stenosis of 20–40%, either symptomatic or asymptomatic. Plaque length and cross-sectional area were assessed on ultrasound scans.Results: We identified 41 patients with ischemic stroke or TIA and ICA-stenosis of 20–40%; 14 symptomatic and 27 asymptomatic. The plaque cross-sectional area was significantly larger among symptomatic than asymptomatic ICA-stenosis; median values (IQR) were 0.45 (0.21–0.69) cm2 and 0.27 (0.21–0.38) cm2, p = 0.03, respectively. A plaque cross-sectional area ≥0.36 cm2 had a sensitivity of 71% and a specificity of 76% for symptomatic compared with asymptomatic ICA-stenosis. In a sex-adjusted multivariate logistic regression, a plaque cross-sectional area ≥0.36 cm2 and a plaque length ≥1.65 cm were associated with an OR (95% CI) of 5.54 (1.2–25.6), p = 0.028 and 1.78 (0.36–8.73), p = 0.48, respectively, for symptomatic ICA-stenosis.Conclusion: Large plaques might increase the risk of ischemic stroke or TIA among patients with low-grade ICA-stenosis of 20–40%. Sufficiently powered prospective longitudinal cohort studies are needed to definitively test the stroke risk stratification value of carotid plaque length and cross-sectional area in the setting of current optimal medical treatment.

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“…Buon et al (18) found in young patients with ICAS <50% that plaques of sICAS were significantly longer and more often echolucent compared to aICAS. Interestingly, a plaque length of 12 mm was the cut-off-value in the aforementioned study favoring symptomatic rather than asymptomatic carotid artery with a high sensitivity (86%) and specificity (73%).…”

Section: Discussionmentioning

confidence: 99%

Stenosis Length and Degree Interact With the Risk of Cerebrovascular Events Related to Internal Carotid Artery Stenosis

et al. 2019

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Background and Purpose: Internal carotid artery stenosis (ICAS)≥70% is a leading cause of ischemic cerebrovascular events (ICVEs). However, a considerable percentage of stroke survivors with symptomatic ICAS (sICAS) have <70% stenosis with a vulnerable plaque. Whether the length of ICAS is associated with high risk of ICVEs is poorly investigated. Our main aim was to investigate the relation between the length of ICAS and the development of ICVEs. Methods: In a retrospective cross-sectional study, we identified 95 arteries with sICAS and another 64 with asymptomatic internal carotid artery stenosis (aICAS) among 121 patients with ICVEs. The degree and length of ICAS as well as plaque echolucency were assessed on ultrasound scans. Results: A statistically significant inverse correlation between the ultrasound-measured length and degree of ICAS was detected for sICAS≥70% (Spearman correlation coefficient ρ = –0.57, p < 0.001, n = 51) but neither for sICAS<70% (ρ = 0.15, p = 0.45, n = 27) nor for aICAS (ρ = 0.07, p = 0.64, n = 54). The median (IQR) length for sICAS<70% and ≥70% was 17 (15–20) and 15 (12–19) mm ( p = 0.06), respectively, while that for sICAS<90% and sICAS 90% was 18 (15–21) and 13 (10–16) mm, respectively ( p < 0.001). Among patients with ICAS <70%, a cut-off length of ≥16 mm was found for sICAS rather than aICAS with a sensitivity and specificity of 74.1% and 51.1%, respectively. Irrespective of the stenotic degree, plaques of the sICAS compared to aICAS were significantly more often echolucent (43.2 vs. 24.6%, p = 0.02). Conclusion: We found a statistically insignificant tendency for the ultrasound-measured length of sICAS<70% to be longer than that of sICAS≥70%. Moreover, the ultrasound-measured length of sICAS<90% was significantly longer than that of sICAS 90%. Among patients with sICAS≥70%, the degree and length of stenosis were inversely correlated. Larger studies are needed before a clinical implication can be drawn from these results.

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Carotid Ultrasound for Assessment of Nonobstructive Carotid Atherosclerosis in Young Adults with Cryptogenic Stroke

Cited by 29 publications

References 22 publications

Data‐driven machine‐learning analysis of potential embolic sources in embolic stroke of undetermined source

Data‐driven machine‐learning analysis of potential embolic sources in embolic stroke of undetermined source

Symptomatic vs. Asymptomatic 20–40% Internal Carotid Artery Stenosis: Does the Plaque Size Matter?

Stenosis Length and Degree Interact With the Risk of Cerebrovascular Events Related to Internal Carotid Artery Stenosis

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