The Predictive Value of Carotid Ultrasonography With Cardiovascular Risk Factors-A "SPIDER" Promoting Atherosclerosis

Carotid Plaques and Hypertension as Risk Factors for Cardiovascular Disease and All-Cause Mortality in Middle-Aged Adults

Background/Objectives: Both hypertension and carotid atherosclerosis are independent risk factors for cardiovascular disease. We aim to investigate the synergistic effects of hypertension and carotid plaques on cardiovascular events and all-cause mortality. Methods: A follow-up study was conducted at the Preventive Cardiology Department of Vilnius University Hospital Santaros Klinikos between 2012 and 2021. The study recruited participants aged 40-65 who did not have overt cardiovascular disease (CVD) and were part of the Lithuanian High Cardiovascular Risk primary preventive program. The study collected demographic and clinical data, including an ultrasound assessment of carotid plaque. Results: The participants were monitored for 4-10 years for CVD events and all-cause mortality. Among 6138 participants, 954 (16%) experienced CVD events. The presence of carotid plaque on both sides was significantly associated with CVD events, myocardial infarction, and all-cause mortality. However, the combination of hypertension and carotid plaque did not significantly increase the risk for CVD events or all-cause mortality. Conclusions: The risk of CVD events or all-cause mortality was not significantly increased by the combination of hypertension and carotid plaque. Cardiovascular events depend on the extent of atherosclerosis in the carotid arteries.

Added Clinical Value of Intraplaque Neovascularization Detection to Color Doppler Ultrasound for Assessing Ischemic Stroke Risk

Purpose

Intraplaque neovascularization, assessed using contrast-enhanced ultrasound (CEUS), is associated with ischemic stroke. It remains unclear whether detection of intraplaque neovascularization combined with color Doppler ultrasound (CDUS) provides additional value compared with CDUS alone in assessing ischemic stroke risk. Therefore, we investigated the clinical value of combined CEUS, CDUS, and clinical features for ischemic stroke risk stratification.

Patients and Methods

We recruited 360 patients with ≥50% carotid stenosis between January 2019 and September 2022. Patients were examined using CDUS and CEUS. Covariates associated with ischemic stroke were identified using multivariate logistic regression analysis. The discrimination and calibration were verified using the C-statistic and Hosmer-Lemeshow test. The incremental value of intraplaque neovascularization in the assessment of ischemic stroke was analyzed using the Delong test.

Results

We analyzed the data of 162 symptomatic and 159 asymptomatic patients who satisfied the inclusion and exclusion criteria, respectively. Based on multivariate logistic regression analysis, we constructed a nomogram using intraplaque neovascularization, degree of carotid stenosis, plaque hypoechoicity, and smoking status, with a C-statistic of 0.719 (95% confidence interval [CI]: 0.666-0.768) and a Hosmer-Lemeshow test p value of 0.261. The net reclassification index of the nomogram was 0.249 (95% CI: 0.138-0.359), and the integrated discrimination improvement was 0.053 (95% CI: 0.029-0.079). Adding intraplaque neovascularization to the combination of CDUS and clinical features (0.672; 95% CI: 0.617-0.723) increased the C-statistics (p=0.028).

Conclusion

Further assessment of intraplaque neovascularization after CDUS may help more accurately identify patients at risk of ischemic stroke. Combining multiparametric carotid ultrasound and clinical features may help improve the risk stratification of patients with ischemic stroke with ≥50% carotid stenosis.

Impact of Lipids and Vascular Damage on Early Atherosclerosis in Adolescents with Parental Premature Coronary Artery Disease

AIM

To assess the relationship of cardiovascular risk factors (CRFs) with carotid intima media thickness (IMT) in adolescents with a parental history of premature coronary artery disease (PCAD).

METHODS

This cross-sectional study included 50 healthy adolescents, aged 14-18 years, both sexes, with a parental history of PCAD, that were compared to 50 controls without this history. Questionnaires regarding information of CRFs were applied. Blood chemistry analyses, included lipid profile, lipoprotein (a), low density lipoprotein (LDL) susceptibility to oxidation, and inflammatory cytokine levels. The IMT was evaluated by ultrasound.

RESULTS

The mean age of all participants was 15.9 years. Anthropometric measurements, blood pressure, and lipid profile were similar in both groups. However, the parental history of PCAD group exhibited lower high density lipoprotein cholesterol concentrations, shorter LDL particle oxidation time, and higher lipoprotein (a) levels compared to the control group. IMT was significantly higher in adolescents with a parental history of PCAD compared to controls, (0.53 ± 0.04 mm vs 0.47 ± 0.02 mm, p = 0.001). Among adolescents with a parental history of PCAD, those with ≥ 3 CRFs had significantly higher IMT values (0.56 mm) than those with < 3 CRFs (0.52 mm) and controls (0.48 mm). Multivariable analyses identified that systolic blood pressure and parental history of PCAD explained 26.8% and 16.1% of the variation in IMT. Furthermore, body mass index, LDL-C, ApoB-100, triglycerides and lipoprotein (a) interact with blood pressure levels to explain the IMT values.

CONCLUSION

Adolescents with a parental history of PCAD had higher IMT values than the control group, primary explained by systolic blood pressure and the parental inheritance. Adolescents with parental history of PCAD and ≥ 3 CRFs exhibited the highest IMT values. Notably, lipids and systolic blood pressure jointly contribute to explain IMT in these adolescents.

Carotid Plaque Score for Stroke and Cardiovascular Risk Prediction in a Middle-Aged Cohort From the General Population

Background We aimed to explore the predictive value of the carotid plaque score, compared with the Systematic Coronary Risk Evaluation 2 (SCORE2) risk prediction algorithm, on incident ischemic stroke and major adverse cardiovascular events and establish a prognostic cutoff of the carotid plaque score. Methods and Results In the prospective ACE 1950 (Akershus Cardiac Examination 1950 study), carotid plaque score was calculated with ultrasonography at inclusion in 2012 to 2015. The largest plaque diameter in each extracranial segment of the carotid artery on both sides was scored from 0 to 3 points. The sum of points in all segments provided the carotid plaque score. The cohort was followed up by linkage to national registries for incident ischemic stroke and major adverse cardiovascular events (nonfatal ischemic stroke, nonfatal myocardial infarction, and cardiovascular death) throughout 2020. Carotid plaque score was available in 3650 (98.5%) participants, with mean±SD age of 63.9±0.64 years at inclusion. Only 462 (12.7%) participants were free of plaque, and and 970 (26.6%) had a carotid plaque score of >3. Carotid plaque score predicted ischemic stroke (hazard ratio [HR], 1.25 [95% CI, 1.15-1.36]) and major adverse cardiovascular events (HR, 1.21 [95% CI, 1.14-1.27]) after adjustment for SCORE2 and provided strong incremental prognostic information to SCORE2. The best cutoff value of carotid plaque score for ischemic stroke was >3, with positive predictive value of 2.5% and negative predictive value of 99.3%. Conclusions The carotid plaque score is a strong predictor of ischemic stroke and major adverse cardiovascular events, and it provides incremental prognostic information to SCORE2 for risk prediction. A cutoff score of >3 seems to be suitable to discriminate high-risk subjects. Registration Information clinicaltrials.gov. Identifier: NCT01555411.

A new score for improving cardiovascular risk prediction and prevention

BACKGROUND AND AIMS

The ultrasonographic detection of subclinical atherosclerosis (scATS) at carotid and femoral vascular sites using the atherosclerosis burden score (ABS) improves the risk stratification for atherosclerotic cardiovascular disease beyond traditional cardiovascular (CV) risk factors. However, its predictive value should be further enhanced. We hypothesize that combining the ABS and the Framingham risk score (FHRS) to create a new score called the FHRABS will improve CV risk prediction and prevention. We aim to investigate if incorporating the ABS into the FHRS improved CV risk prediction in a primary prevention setting.

METHODS AND RESULTS

1024 patients were included in this prospective observational cohort study. Carotid and femoral plaques were ultra-sonographic detected. Major incident cardiovascular events (MACEs) were collected. The receiver operating characteristic curve (ROC-AUC) and Youden's index (Ysi) were used to compare the incremental contributions of each marker to predict MACEs. After a median follow-up of 6.0 ± 3.3 years, 60 primary MACEs (5.8%) occurred. The ROC-AUC for MACEs prediction was significantly higher for the FHRABS (0.74, p < 0.024) and for the ABS (0.71, p < 0.013) compared to the FHRS alone (0.71, p < 0.46). Ysi or the FHRABS (42%, p < 0.001) and ABS (37%, p < 0.001) than for the FHRS (31%). Cox proportional-hazard models showed that the CV predictive performance of FHRS was significantly enhanced by the ABS (10.8 vs. 5.5, p < 0.001) and FHRABS (HR 23.30 vs. 5.50, p < 0.001).

CONCLUSIONS

FHRABS is a useful score for improving CV risk stratification and detecting patients at high risk of future MACEs. FHRABS offers a simple-to-use, and radiation-free score with which to detect scATS in order to promote personalized CV prevention.

Validity and Reliability of Point-of-Care Ultrasound for Detecting Moderate- or High-Grade Carotid Atherosclerosis in an Outpatient Department

The prevalence of carotid stenosis is considerably higher in asymptomatic individuals with multiple risk factors than in the general population. We investigated the validity and reliability of carotid point-of-care ultrasound (POCUS) for rapid carotid atherosclerosis screening. We prospectively enrolled asymptomatic individuals with carotid risk scores of ≥7 who underwent outpatient carotid POCUS and laboratory carotid sonography. Their simplified carotid plaque scores (sCPSs) and Handa's carotid plaque scores (hCPSs) were compared. Of 60 patients (median age, 81.9 years), 50% were diagnosed as having moderate- or high-grade carotid atherosclerosis. The overestimation and underestimation of outpatient sCPSs were more likely in patients with low and high laboratory-derived sCPSs, respectively. Bland-Altman plots indicated that the mean differences between the participants' outpatients and laboratory sCPSs were within two standard deviations of their laboratory sCPSs. A Spearman's rank correlation coefficient revealed a strong positive linear correlation between outpatient and laboratory sCPSs (r = 0.956, p < 0.001). An intraclass correlation coefficient analysis indicated excellent reliability between the two methods (0.954). Both carotid risk score and sCPS were positively and linearly correlated with laboratory hCPS. Our findings indicate that POCUS has satisfactory agreement, strong correlation, and excellent reliability with laboratory carotid sonography, making it suitable for rapid screening of carotid atherosclerosis in high-risk patients.

Development and Validation of a Personalized, Sex-Specific Prediction Algorithm of Severe Atheromatosis in Middle-Aged Asymptomatic Individuals: The ILERVAS Study

Background

Although European guidelines recommend vascular ultrasound for the assessment of cardiovascular risk in low-to-moderate risk individuals, no algorithm properly identifies patients who could benefit from it. The aim of this study is to develop a sex-specific algorithm to identify those patients, especially women who are usually underdiagnosed.

Methods

Clinical, anthropometrical, and biochemical data were combined with a 12-territory vascular ultrasound to predict severe atheromatosis (SA: ≥ 3 territories with plaque). A Personalized Algorithm for Severe Atheromatosis Prediction (PASAP-ILERVAS) was obtained by machine learning. Models were trained in the ILERVAS cohort (n = 8,330; 51% women) and validated in the control subpopulation of the NEFRONA cohort (n = 559; 47% women). Performance was compared to the Systematic COronary Risk Evaluation (SCORE) model.

Results

The PASAP-ILERVAS is a sex-specific, easy-to-interpret predictive model that stratifies individuals according to their risk of SA in low, intermediate, or high risk. New clinical predictors beyond traditional factors were uncovered. In low- and high-risk (L&amp;H-risk) men, the net reclassification index (NRI) was 0.044 (95% CI: 0.020–0.068), and the integrated discrimination index (IDI) was 0.038 (95% CI: 0.029–0.048) compared to the SCORE. In L&amp;H-risk women, PASAP-ILERVAS showed a significant increase in the area under the curve (AUC, 0.074 (95% CI: 0.062–0.087), p-value: &lt; 0.001), an NRI of 0.193 (95% CI: 0.162–0.224), and an IDI of 0.119 (95% CI: 0.109–0.129).

Conclusion

The PASAP-ILERVAS improves SA prediction, especially in women. Thus, it could reduce the number of unnecessary complementary explorations selecting patients for a further imaging study within the intermediate risk group, increasing cost-effectiveness and optimizing health resources.

Clinical Trial Registration

[www.ClinicalTrials.gov], identifier [NCT03228459].

Verification of subclinical carotid atherosclerosis as part of risk stratification in overweight and obesity: the role of machine learning in the development of a diagnostic algorithm

Aim. Comparative analysis of mathematical models obtained using multivariate logistic regression (MLR) with stepwise inclusion of predictors and machine learning (ML) for assessing the probability of subclinical carotid atherosclerosis in normotensive overweight and obese patients without cardiovascular diseases and/or diabetes.

Material and methods. We received data on patients from the Webiomed platform database. The inclusion criteria were age ≥18 years, body mass index ≥25 kg/m2, extracranial artery ultrasound results, while the exclusion criteria included diabetes and/or cardiovascular disease. MLR analysis was carried out with stepwise inclusion of predictors. ML algorithms were used to create an alternative model.

Results. The overall percentage of true results for MLR model was 73,2%, while the proportion of true negative and positive predictions was 80,1% and 63,4%, respectively. Mathematical models created using ML methods are characterized by a predictive value from 75 to 97% with a sensitivity of 77 to 92% and a specificity of 80 to 98%.

Conclusion. A significant superiority of ML models was revealed in the study of available clinical and paraclinical parameters. Integration of ML mathematical models into a diagnostic algorithm for making a decision to refer a low-risk patient for extracranial artery ultrasound will significantly improve its accuracy and cost efficiency.

Nomogram and Carotid Risk Score for Predicting Moderate or High Carotid Atherosclerosis among Asymptomatic Elderly Recycling Volunteers

Carotid atherosclerosis is associated with cardiovascular and cerebrovascular events. We explored an appropriate method for selecting participants without ischemic cerebrovascular disease but with various comorbidities eligible for a carotid ultrasound. This was a retrospective subgroup analysis of the carotid plaque burden from a previous study involving a vascular and cognitive survey of 956 elderly recycling volunteers (778 women and 178 men; mean age: 70.8 years). We used carotid ultrasound to detect the carotid plaque and computed the carotid plaque score (CPS). A moderate or high degree of carotid atherosclerosis (MHCA) was defined as CPS > 5 and was observed in 22% of the participants. The CPS had positive linear correlations with age, systolic blood pressure, and fasting glucose. We stratified the participants into four age groups: 60−69, 70−74, 75−79, and ≥80 years. Multivariable analysis revealed that significant predictors for MHCA were age, male sex, hypertension, diabetes mellitus, hyperlipidemia, coronary artery disease, and a nonvegetarian diet. Coronary artery disease and advanced age were the two strongest predictors. We chose the aforementioned seven significant predictors to establish a nomogram for MHCA prediction. The area under the receiver operating characteristic curve in internal validation with 10-fold cross-validation and the classification accuracy of the nomogram were 0.785 and 0.797, respectively. We presumed people who have a ≥50% probability of MHCA warranted a carotid ultrasound. A flowchart table derived from the nomogram addressing the probabilities of all models of combinations of comorbidities was established to identify participants who had a probability of MHCA ≥ 50% (corresponding to a total nomogram score of ≥15 points). We further established a carotid risk score range from 0 to 17 comprising the seven predictors. A carotid risk score ≥ 7 was the most optimal cutoff value associated with a probability of MHCA ≥ 50%. Both total nomogram score ≥ 15 points and carotid risk score ≥ 7 can help in the rapid identification of individuals without stroke but who have a ≥50% probability of MHCA—these individuals should schedule a carotid ultrasound.