Imaging biomarkers of vulnerable carotid plaques for stroke risk prediction and their potential clinical implications

We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (EHRs) and machine learning (ML) algorithms to establish a stroke risk prediction system. Due to different characteristics, such as accessibility, time, and spatial resolution of various wearable-based technologies, strategies of applying different types of wearables to maximize the efficacy of stroke risk prediction are also reported. In addition, based on the various applications of multimodal electroencephalography–functional near-infrared spectroscopy (EEG–fNIRS) on stroke patients, the perspective of using this technique to improve the prediction performance is elaborated. Expected prediction has to be dynamically delivered with high-precision outcomes. There is a need for stroke risk stratification and management to reduce the resulting social and economic burden.

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Abstract 17979: Left Atrial Cross Sectional Area is a Novel Echocardiographic Measure Which Improves the Predictive Value of Chads2 Score in Risk Stratification of Cardioembolic Strokes

Circulation ◽

10.1161/circ.130.suppl_2.17979 ◽

2014 ◽

Vol 130 (suppl_2) ◽

Author(s):

Timothy C Tan ◽

Mark Handschumacher ◽

Octavio M Pontes-Neto ◽

Maria C Nunes ◽

Yong H Park ◽

...

Keyword(s):

Risk Stratification ◽

Risk Prediction ◽

Left Atrial ◽

Stroke Risk ◽

Thrombus Formation ◽

Cross Sectional Area ◽

Sectional Area ◽

Chads2 Score ◽

Cross Sectional ◽

C Statistic

Background: Cardioembolic (CE) stroke carries significant morbidity and mortality. Current risk stratification tools such as CHADS2 score do not include any imaging parameters and are based on clinical features, which have limitations. Left atrial (LA) enlargement and remodeling may be associated with CE risk due to predisposition for atrial arrhythmias and thrombus formation. Left atrial cross sectional area (LACSA), a novel echo measure which reflects both LA size and shape, may improve CE stroke risk assessment. Aim: This study examined the value of LACSA in predicting CE stroke risk and the improvement in risk prediction when added to CHADS2 score. Methods: Clinical and echo parameters were examined in a prospective cohort of 1275 consecutive patients with ischemic stroke. Strokes were classified using the Causative Classification of Strokes and 259 (20%) were classified as CE stroke. LACSA was calculated using the formula: π/4*largest measured LA diameter*smallest measured LA diameter where mid LA diameter was measured in the parasternal long axis, 4 chamber and 2 chamber views. Results: Patients with CE stroke had greater LACSA (8.6 ± 2.3 vs 6.4 ± 1.8 cm2/m2; p<0.001) and mean CHADS2 score (2.25 ± 1.28 vs 1.87 ± 1.40; p<0.0001) compared to non-CE stroke patients. LACSA was independently associated with CE strokes (OR 1.21; 95% CI 1.08-1.34; p=0.001) in a multivariable model adjusted for CHADS2, gender, score, BMI, atrial fibrillation, anti-platelet and anti-coagulant use, E/E’ and LVEF. The addition of LACSA to CHADS2 score improved the prediction of CE stroke (c-statistic for predicting CE stroke using CHADS2 alone was 0.59 (95% CI 0.55-0.63) vs CHADS2 and LACSA 0.78 (95% CI 0.72-0.80) (p<0.001). Conclusion: LACSA is a novel measure of LA remodeling and associated with CE stroke. LACSA, an imaging parameter, enhances the risk prediction of the CHADS2 score, a clinical measure of risk, improving risk stratification for CE stroke and impacting therapeutic strategies.

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Reproductive lifespan and incident stroke risk among post-menopausal women: Is it time for sex-specific risk prediction tools?

International Journal of Cardiology ◽

10.1016/j.ijcard.2020.12.032 ◽

2020 ◽

Author(s):

Islam Y. Elgendy ◽

Hend Mansoor ◽

Carl J. Pepine

Keyword(s):

Risk Prediction ◽

Stroke Risk ◽

Prediction Tools ◽

Specific Risk ◽

Menopausal Women ◽

Post Menopausal

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The Use of Molecular and Imaging Biomarkers in Lung Cancer Risk Prediction

Journal of Biometrics & Biostatistics ◽

10.4172/2155-6180.1000299 ◽

2016 ◽

Vol 7 (3) ◽

Author(s):

Fenghai D

Keyword(s):

Lung Cancer ◽

Cancer Risk ◽

Risk Prediction ◽

Lung Cancer Risk ◽

Imaging Biomarkers

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Carotid Plaque Morphology Improves Stroke Risk Prediction: Usefulness of a New Ultrasonographic Score

Cerebrovascular Diseases ◽

10.1159/000320852 ◽

2011 ◽

Vol 31 (3) ◽

pp. 300-304 ◽

Cited By ~ 43

Author(s):

P. Prati ◽

A. Tosetto ◽

M. Casaroli ◽

A. Bignamini ◽

L. Canciani ◽

...

Keyword(s):

Risk Prediction ◽

Carotid Plaque ◽

Stroke Risk ◽

Plaque Morphology

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Relationship between the non-HDLc-to-HDLc ratio and carotid plaques in a high stroke risk population: A cross-sectional study in China

10.21203/rs.3.rs-26172/v3 ◽

2020 ◽

Author(s):

Yan Liu ◽

Zhenwen Zhang ◽

Binlan Xia ◽

Liping Wang ◽

Hengzhong Zhang ◽

...

Keyword(s):

High Risk ◽

Stroke Risk ◽

Heart Diseases ◽

Density Lipoprotein ◽

Cross Sectional Study ◽

Sectional Study ◽

Cross Sectional ◽

Carotid Plaques ◽

Lack Of Exercise ◽

The Relationship

Abstract Background: Evidence on the association between the non-high-density lipoprotein cholesterol (non-HDLc)-to-high-density lipoprotein cholesterol (HDLc) ratio (non-HDLc/HDLc) and carotid plaques is still limited. This study aims to assess the relationship between the non-HDLc/HDLc and carotid plaques in a population with a high risk of stroke.Methods: A cross-sectional study based on the community was conducted in Yangzhou, China. Residents (no younger than 40 years old) underwent questionnaire interviews, physical examinations, and laboratory testing during 2013-2014. The subjects with a high risk of stroke were further selected (at least three of eight risk factors including hypertension, atrial fibrillation, type 2 diabetes mellitus, dyslipidaemia, smoking, lack of exercise, overweight, and family history of stroke) or a transient ischaemic attack (TIA) or stroke history. Carotid ultrasonography was then performed on the high stroke risk participants. Carotid plaque was defined as a focal carotid intima-media thickness (cIMT) ≥1.5 cm or a discrete structure protruding into the arterial lumen at least 50% of the surrounding cIMT. Logistic regression was employed to evaluate the relationship between the non-HDLc/HDLc and carotid plaques.Results: Overall, 839 subjects with a high risk of stroke were ultimately included in the analysis, and carotid plaques were identified in 341 (40.6%) of them. Participants in the highest non-HDLc/HDLc tertile group presented a higher proportion of carotid plaques than did those in the other two groups. After adjustment for other confounders, each unit increase in the non-HDLc/HDLc was significantly associated with carotid plaques (OR 1.55, 95%CI 1.28-1.88). In the subgroup analysis, the non-HDLc/HDLc was positively and significantly associated with the presence of carotid plaques in most subgroups. Additionally, the non-HDLc/HDLc interacted significantly with three stratification variables, including sex (OR 1.31 for males vs. OR 2.37 for females, P interaction = 0.016), exercise (OR 1.18 for subjects without lack of exercise vs. OR 1.99 for subjects with lack of exercise, P interaction = 0.004) and heart diseases (OR 1.40 for subjects without heart diseases vs. OR 3.12 for subjects with heart diseases, P interaction = 0.033). Conclusion: The non-HDLc/HDLc was positively associated with the presence of carotid plaques in a Chinese high stroke risk population. A prospective study or randomized clinical trial of lipid-lowering therapy in the Chinese population is needed to evaluate their causal relationship.

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