Carotid artery stiffness measured by strain elastography ultrasound is a stroke risk factor

Tjan, Anastasia; Widiana, I Gde Raka; Martadiani, Elysanti Dwi; Ayusta, I Made Dwijaputra; Asih, Made Widhi; Sitanggang, Firman Parulian

doi:10.1016/j.cegh.2021.100850

Cited by 4 publications

(3 citation statements)

References 30 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the field of vascular disease, US elastography has mainly been applied in the treatment of carotid disease [20][21][22][23]. Many studies have reported its feasibility, and the accuracy of quantitative elasticity measurements is comparable to those of other diagnostic modalities [23].…”

Section: Discussionmentioning

confidence: 99%

Ultrasound Elastography to Differentiate the Thrombus and Plaque in Peripheral Arterial Diseases

Kim

Cho

et al. 2022

Vasc Specialist Int

View full text Add to dashboard Cite

Purpose: Arterial stiffness and steno-occlusion of the lower-extremity can result from many vascular lesions, including acute thromboembolisms, soft plaques, calcified plaques, or inflammatory disease. Ultrasound (US) elastography measures the tissue deformation response to compression and displays tissue stiffness. This study aimed to evaluate the characteristics of arterial lesions in the lower extremities using US elastography. Materials and Methods:We retrospectively analyzed the data of 20 patients who visited our institute for arterial disease treatment between May 2016 and November 2017. An US examination with B-mode and strain elastography (SE) was performed of four different lesion types at 45 sites: acute and subacute thromboembolisms, soft plaques, calcified plaques, and thromboangiitis obliterans lesions (TAOs). During SE, stress was externally applied by the operator using the transducer. Strain ratio (SR) was calculated as the fraction of the average strain in the reference area divided by the average strain in the lesion. The SR was compared among different lesion types, with the accompanying vein as the reference region of interest. Results:The strain was highest in the soft plaques (0.63%±0.23%), followed by the TAOs (0.45%±0.11%), calcified plaques (0.44%±0.13%), and acute thromboembolisms (0.34%±0.23%), which were statistically significant (P=0.026). However, the mean SR was highest for the calcified plaques (2.33%±0.80%), followed by the TAOs (1.63%±0.40%), acute thromboembolisms (1.60%±0.48%), and soft plaques (1.51±0.39), and which were statistically significant (P=0.013). Conclusion: Despite several limitations, vascular elastography may be useful for differentiating between lesion types in peripheral arterial disease.

show abstract

Section: Discussionmentioning

confidence: 99%

Ultrasound Elastography to Differentiate the Thrombus and Plaque in Peripheral Arterial Diseases

Kim

Cho

et al. 2022

Vasc Specialist Int

View full text Add to dashboard Cite

show abstract

“…The association between carotid strain and ischemic stroke has been demonstrated in hypertensive and diabetic patients [ 31 , 32 ]. The findings regarding its association with coronary artery events remain unclear [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

The Predictive Value of Carotid Artery Strain and Strain-Rate in Assessing the 3-Year Risk for Stroke and Acute Coronary Syndrome in Patients with Metabolic Syndrome

Arnăutu¹,

Morariu²,

Arnăutu³

et al. 2022

Rev. Cardiovasc. Med.

View full text Add to dashboard Cite

Purpose: The goal of this study is to see if carotid strain and strain rate can predict major cardio-vascular events (MACE) in people who have metabolic syndrome (MS) over a 3-year period of time. Methods: In this prospective observational research, we enrolled 220 adult MS patients (60.7 7.5 years old, 53% male). Two-dimensional common carotid carotid artery (CCA) speckle-tracking ultrasound was used to determine the peak circumferential strain (CS) and the peak circumferential strain rate (CSR). Clinical outcomes were assessed throughout a three-year follow-up period. Results: After a 3-year follow-up period follow-up, 14 (7%) experienced MACE: Eight (4%) suffered an atherothrombotic ischemic stroke, four (2%) had acute coronary syndrome, and two (1%) were hospitalized for heart failure. Univariate regression analysis of the clinical and echocardiographic features of the MS patients found that age, systemic hypertension, diabetes mellitus, and the CCA circumferential strain and strain rate were significantly associated with the risk of MACE. Multivariate logistic regression identified two independent predictors of MACE in patients with MS, namely the CCA-related CS (%) and CSR (1/s), p 0.01. The Receiver operating characteristic (ROC) curve analyses of these independent predictors of MACE indicated appropriate sensitivities and specificities. CS (AUC = 0.806, sensitivity = 82.6%, specificity = 79.2%, p 0.0001) and CSR (AUC = 0.779, sensitivity = 82.6%, specificity = 72.4%, p 0.0001) with cut-off values of 2.9% for carotid CS and 0.35 for carotid CSR. Using these cut-off values, we obtained Kaplan-Meier survival curves, and these showed that MACE, ischemic stroke, and ACS-free survival was significantly lower among the MS patients with lower carotid CS and CSR ( p 0.0001). Conclusions: Carotid CS and CSR were independent predictors of major cardio- and cerebro-vascular events in prospectively monitored MS patients without established cardiovascular disease. Carotid deformation could be valuable as an early prognostic indicator for the cardiovascular risk in this population group.

show abstract

“…Many low-income countries are unable to cope with the burden posed by this disease. Moreover, Indonesia placed first in the highest death cases caused by stroke disease with 193,3/100.000 cases per year [4]. Some cause factors of stroke disease are hypertension, obesity, smoke, cholesterol increase, physical activity, low-density lipoprotein increase, excessive alcohol consumption, and diabetes [5].…”

Section: Introductionmentioning

confidence: 99%

Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model

Purwono

Ma’arif

Negara

et al. 2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

View full text Add to dashboard Cite

Stroke is a disease caused by brain tissue damage because of blockage in the cerebrovascular system that disrupts body sensory and motoric systems Stroke disease is one of the highest death cause in the world. Data collection from Electronic Health Records (EHR) is increasing and has been included in the health service big data. It can be processed and analyzed using machine learning to determine the risk group of stroke disease. Machine learning can be used as a predictor of stroke causes, while the predictor clarifies the influence of each cause factor of the disease. Our contribution in this research is to evaluate Feyn Qlattice machine learning models to detect the influence of stroke disease's main cause features. We attempt to obtain a correlation between features of the stroke disease, especially on the gender as a feature, whether any other features can influence the gender feature. This research utilizes 4908 data of the disease predictor using the Feyn Qlattice model. The result implies that gender highly impacts age and hypertension on stroke disease causes. Autorun in Feyn Qlattice model was run with ten epochs, resulting in 17596 test models at 57s. Query string parameter that was focused on age and hypertension features resulted in 1245 models at 4s. An increase of accuracy was found in training metrics from 0.723 to 0.732 and in testing metrics from 0.695 to 0.708. Evaluation results showed that the model is reasonably good as a predictor of stroke disease, indicated with blue lines of AUC in training and testing metrics close to ROC's left side peak curve.

show abstract

Carotid artery stiffness measured by strain elastography ultrasound is a stroke risk factor

Cited by 4 publications

References 30 publications

Ultrasound Elastography to Differentiate the Thrombus and Plaque in Peripheral Arterial Diseases

Ultrasound Elastography to Differentiate the Thrombus and Plaque in Peripheral Arterial Diseases

The Predictive Value of Carotid Artery Strain and Strain-Rate in Assessing the 3-Year Risk for Stroke and Acute Coronary Syndrome in Patients with Metabolic Syndrome

Linkage Detection of Features that Cause Stroke using Feyn Qlattice Machine Learning Model

Contact Info

Product

Resources

About