Focused Vascular Ultrasound for the Assessment of Atherosclerosis: A Proof-of-Concept Study

The association of carotid plaque burden and composition and the coronary artery calcium score in intermediate cardiovascular risk patients

Both the carotid ultrasound and coronary artery calcium (CAC) score quantify subclinical atherosclerosis and are associated with cardiovascular disease and events. This study investigated the association between CAC score and carotid plaque quantity and composition. Adult participants (n = 43) without history of cardiovascular disease were recruited to undergo a carotid ultrasound. Maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (CIMT), and plaque score were measured. Grayscale pixel distribution analysis of ultrasound images determined plaque tissue composition. Participants then underwent CT to determine CAC score, which were also categorized as absent (0), mild (1-99), moderate (100-399), and severe (400+). Spearman correlation coefficients between carotid variables and CAC scores were computed. The mean age of participants was 63 ± 11 years. CIMT, TPA, MPH, and plaque score were significantly associated with CAC score (ρ = 0.60, p < 0.0001; ρ = 0.54, p = 0.0002; ρ = 0.38, p = 0.01; and ρ = 0.49, p = 0.001). Echogenic composition features %Calcium and %Fibrous tissue were not correlated to a clinically relevant extent. There was a significant difference in the TPA, MPH, and plaque scores of those with a severe CAC score category compared to lesser categories. While carotid plaque burden was associated with CAC score, plaque composition was not. Though CAC score reliably measures calcification, carotid ultrasound gives information on both plaque burden and composition. Carotid ultrasound with assessment of plaque features used in conjunction with traditional risk factors may be an alternative or additive to CAC scoring and could improve the prediction of cardiovascular events in the intermediate risk population.

HEART + score: integrating carotid ultrasound to chest pain assessment in the emergency department

OBJECTIVES

The HEART score is a clinical decision tool that stratifies patients into categories of low, moderate, and high-risk of major adverse cardiac events in the emergency department (ED) but cannot identify underlying cardiovascular disease in patients without prior history. The presence of atherosclerosis can easily be detected at the bedside using carotid ultrasound. Plaque quantification is well established, and plaque composition can be assessed using ultrasound grayscale pixel distribution analysis. This study aimed to determine whether carotid plaque burden and/or composition correlated with risk of events and could improve the sensitivity of the HEART score in risk stratifying ED patients with chest pain.

METHODS

The HEART score was calculated based on history, electrocardiogram, age, risk factors, and initial troponin in patients presenting to the ED with chest pain (n = 321). Focused carotid ultrasound was performed, and maximum plaque height and total plaque area were used to determine plaque burden (quantity). Plaque composition (% blood, fat, muscle, fibrous, calcium-like tissue) was assessed by pixel distribution analysis.

RESULTS

Carotid plaque height and area increased with HEART score (p < 0.0001). Carotid plaque % fibrous and % calcium also increased with HEART score. The HEART score had a higher area under the curve (AUC = 0.84) in predicting 30-day events compared to the plaque variables alone (AUCs < 0.70). Integrating plaque quantity into the HEART score slightly increased test sensitivity (62-69%) for 30-day events and reclassified 11 moderate-risk participants to high-risk (score 7-10).

CONCLUSION

Plaque burden with advanced composition features (fibrous and calcium) was associated with increased HEART score. Integrating plaque assessment into the HEART score identified subclinical atherosclerosis in moderate-risk patients.

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.

A new predictor of coronary artery disease in acute ischemic stroke or transient ischemic attack patients: pericarotid fat density

OBJECTIVES

The study aims to evaluate the incremental predictive value of pericarotid fat density (PFD) on head and neck computed tomography angiography (CTA) for the obstructive coronary artery disease (CAD) (≥ 50% stenosis) relative to a clinical risk model (Framingham risk score (FRS)) and the degree of carotid artery stenosis and plaque type in acute ischemic stroke (AIS) or transient ischemic attack (TIA) patients without a known history of CAD.

METHODS

In a cohort of 134 consecutive stable patients diagnosed with AIS or TIA undergoing head and neck CTA between January 2010 and December 2021, pericarotid adipose tissue density (PFD) was quantified using a dedicated software. We collected demographic and clinical data, assessed the risk of CAD using the FRS, and analyzed coronary and carotid artery CTA images. Univariate and multivariate logistic regression analyses were performed to assess associations between FRS, PFD, CTA variables, and obstructive CAD risk. Four prediction models were established to evaluate the incremental predictive value of PFD relative to FRS, stenosis degree, and plaque types. Receiver operating characteristic (ROC) curves were generated, and the areas under the curves (AUC) were compared.

RESULTS

Increasing FRS, stenosis degree, and PFD values were positively correlated with obstructive CAD (all p < 0.05). In the predictive models for obstructive CAD, the model incorporating carotid stenosis exhibited superior predictive performance compared to FRS alone (p < 0.05). Moreover, the predictive model integrating PFD demonstrated enhanced performance and yielded the highest AUC of the receiver operator characteristic curve (AUC = 0.783), with sensitivity and specificity values of 86.89% and 65.75%, respectively.

CONCLUSION

CTA-derived PFD measurements offer supplementary predictive value for obstructive CAD beyond FRS and stenosis, thereby facilitating improved risk stratification of TIA or stroke patients without a history of CAD history.

CLINICAL RELEVANCE STATEMENT

CTA-derived PFD provides incremental predictive value for obstructive coronary artery disease in acute ischemic stroke or transient ischemic attack patients without CAD history, beyond Framingham risk score and carotid artery stenosis degree, improving risk stratification.

KEY POINTS

• Pericarotid fat density is associated with obstructive coronary artery disease in acute ischemic stroke or transient ischemic attack patients. • Higher pericarotid fat density corresponds to an increased risk of obstructive coronary artery disease. • Estimation of pericarotid fat density using computed tomography angiography imparts additional predictive value for obstructive CAD in risk stratification of acute ischemic stroke or transient ischemic attack patients.

Biomarkers of aging

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

Association between Carotid Intraplaque Neovascularization Detected by Contrast-Enhanced Ultrasound and the Progression of Coronary Lesions in Patients Undergoing Percutaneous Coronary Intervention

BACKGROUND

It is thought that the progression of vulnerable plaque is due in part to neovascularization, and plaque vulnerability is a useful approach for classifying cardiovascular risk. The aim of this retrospective study was to evaluate the correlation between carotid intraplaque neovascularization (IPN) detected on contrast-enhanced ultrasound and the progression of coronary lesions in patients undergoing percutaneous coronary intervention (PCI).

METHODS

Contrast-enhanced ultrasound and angiography were performed in 131 patients undergoing PCI. All patients had angiograms obtained ≥12 months after PCI, and progression was defined using those angiograms. On the basis of angiographic images, patients were divided into progression and nonprogression groups. IPN was graded from 0 to 3 according to each plaque's microbubble appearance and extent, detected using contrast-enhanced ultrasound. The plaque with the highest IPN was used for analysis. Logistic regression and receiver operating characteristic analyses were applied to evaluate risk factors for predicting the progression of coronary lesions in patients undergoing PCI.

RESULTS

In the progression group, the numbers of patients with IPN values of 0, 1, 2, and 3 were one (3.3%), nine (30.0%), 16 (53.3%), and four (13.3%), respectively. Significant differences were found in maximum plaque height and IPN between groups. IPN and maximum plaque height were independent risk contributors to coronary lesion progression in patients undergoing PCI. The sensitivity, specificity, positive predictive value, and negative predictive value of IPN of 1.5 and to predict the progression of coronary lesions were 67%, 91%, 68%, and 89%, respectively. The area under the curve was 0.822.

CONCLUSIONS

Carotid plaque neovascularization was correlated with the progression of coronary lesions in patients undergoing PCI. IPN is a clinically useful tool for detecting the progression of coronary lesions and for risk stratification, especially in patients >60 years old.

Focused carotid ultrasound to predict major adverse cardiac events among emergency department patients with chest pain

BACKGROUND AND OBJECTIVE

Point-of-care focused vascular ultrasound (FOVUS), an assessment of carotid artery plaque, predicts coronary artery disease in outpatients referred for coronary angiography. Our primary objective was to determine the diagnostic accuracy of sonographer-performed FOVUS to predict major adverse cardiac events (MACE) within 30 days among patients with suspected cardiac ischemia in the emergency department (ED).

METHODS

We conducted a prospective cohort study of patients with chest pain presenting to a tertiary care ED who had an electrocardiogram and cardiac troponin testing. The primary outcome was a composite of death, acute myocardial infarction, or re-vascularization at 30 days. A sonographer performed FOVUS scans in consenting eligible subjects. Emergency physicians, blinded to the sonographer FOVUS result, performed a second FOVUS on some subjects.

RESULTS

We recruited 326 subjects (age 62.1 ± 13.5 years; 166 (52%) men), 319 of whom completed an FOVUS scan by the sonographer. Of these, 198 (62%) had a positive FOVUS scan and 41 (13%) had a 30-day MACE. The sensitivity was 83% (95% CI 71-94%), specificity 41% (95% CI 36-47%), positive-likelihood ratio 1.41 (95% CI 1.19-1.68), and negative-likelihood ratio 0.41 (95% CI 0.23-0.75). Among 71 subjects also scanned by an emergency physician, the Kappa was 0.50 (95% CI 0.31-0.70), suggesting moderate agreement between sonographer and emergency physician on the determination of significant carotid plaque.

CONCLUSIONS

The presence of carotid plaque on sonographer-performed FOVUS is associated with 30-day MACE in ED patients presenting with chest pain. The prognostic performance of FOVUS is not sufficient to support its use as a stand-alone risk stratification tool in the ED. Future work should investigate FOVUS in conjunction with validated clinical decision rules for chest pain and the impact of enhanced training and quality improvement in the conduct of FOVUS by emergency physicians.

REGISTRATION

This study was registered at clinicaltrials.gov (NCT02947360).

Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization

OBJECTIVE

Cardiovascular disease (CVD) is a major healthcare challenge and therefore early risk assessment is vital. Previous assessment techniques use either "conventional CVD risk calculators (CCVRC)" or machine learning (ML) paradigms. These techniques are ad-hoc, unreliable, not fully automated, and have variabilities. We, therefore, introduce AtheroEdge-MCDLAI (AE3.0DL) windows-based platform using multiclass Deep Learning (DL) system.

METHODS

Data was collected on 500 patients having both carotid ultrasound and corresponding coronary angiography scores (CAS), measured as stenosis in coronary arteries and considered as the gold standard. A total of 39 covariates were used, clubbed into three clusters, namely (i) Office-based: age, gender, body mass index, smoker, hypertension, systolic blood pressure, and diastolic blood pressure; (ii) Laboratory-based: Hyperlipidemia, hemoglobin A1c, and estimated glomerular filtration rate; and (iii) Carotid ultrasound image phenotypes: maximum plaque height, total plaque area, and intra-plaque neovascularization. Baseline characteristics for four classes (target labels) having significant (p < 0.0001) values were calculated using Chi-square and ANOVA. For handling the cohort's imbalance in the risk classes, AE3.0DL used the synthetic minority over-sampling technique (SMOTE). AE3.0DL used Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) DL models and the performance (accuracy and area-under-the-curve) was computed using 10-fold cross-validation (90% training, 10% testing) frameworks. AE3.0DL was validated and benchmarked.

RESULTS

The AE3.0DL using RNN and LSTM showed an accuracy and AUC (p < 0.0001) pairs as (95.00% and 0.98), and (95.34% and 0.99), respectively, and showed an improvement of 32.93% and 9.94% against CCVRC and ML, respectively. AE3.0DL runs in <1 s.

CONCLUSION

DL algorithms are a powerful paradigm for coronary artery disease (CAD) risk prediction and CVD risk stratification.

Progression of atherosclerosis with carnitine supplementation: a randomized controlled trial in the metabolic syndrome

Background

L-carnitine (L-C), a ubiquitous nutritional supplement, has been investigated as a potential therapy for cardiovascular disease, but its effects on human atherosclerosis are unknown. Clinical studies suggest improvement of some cardiovascular risk factors, whereas others show increased plasma levels of pro-atherogenic trimethylamine N-oxide. The primary aim was to determine whether L-C therapy led to progression or regression of carotid total plaque volume (TPV) in participants with metabolic syndrome (MetS).

Methods

This was a phase 2, prospective, double blinded, randomized, placebo-controlled, two-center trial. MetS was defined as ≥ 3/5 cardiac risk factors: elevated waist circumference; elevated triglycerides; reduced HDL-cholesterol; elevated blood pressure; elevated glucose or HbA1c; or on treatment. Participants with a baseline TPV ≥ 50 mm³ were randomized to placebo or 2 g L-C daily for 6 months.

Results

The primary outcome was the percent change in TPV over 6 months. In 157 participants (L-C N = 76, placebo N = 81), no difference in TPV change between arms was found. The L-C group had a greater increase in carotid atherosclerotic stenosis of 9.3% (p = 0.02) than the placebo group. There was a greater increase in total cholesterol and LDL-C levels in the L-C arm.

Conclusions

Though total carotid plaque volume did not change in MetS participants taking L-C over 6-months, there was a concerning progression of carotid plaque stenosis. The potential harm of L-C in MetS and its association with pro-atherogenic metabolites raises concerns for its further use as a potential therapy and its widespread availability as a nutritional supplement.

Trial registration: ClinicalTrials.gov, NCT02117661, Registered April 21, 2014, https://clinicaltrials.gov/ct2/show/NCT02117661.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12986-022-00661-9.

Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients

The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitus, and/or arterial hypertension, using conventional or office-based, laboratory-based blood biomarkers and carotid/femoral ultrasound image-based phenotypes. Two kinds of data (CVD risk factors and presence of CVD-defined as stroke, or myocardial infarction, or coronary artery syndrome, or peripheral artery disease, or coronary heart disease) as ground truth, were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3 years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) to study their effect on the ML classifiers. Three kinds of ML classifiers (Random Forest, Support Vector Machine, and Linear Discriminant Analysis) were applied in a two-fold cross-validation framework using the data augmented by synthetic minority over-sampling technique (SMOTE) strategy. The performance of the ML classifiers was recorded. In this cohort with overall 46 CVD risk factors (covariates) implemented in an online cardiovascular framework, that requires calculation time less than 1 s per patient, a mean accuracy and area-under-the-curve (AUC) of 98.40% and 0.98 (p < 0.0001) for CVD presence detection at visit 1, and 98.39% and 0.98 (p < 0.0001) at visit 2, respectively. The performance of the cardiovascular framework was significantly better than the classical CVD risk score. The ML paradigm proved to be powerful for CVD prediction in individuals at medium to high cardiovascular risk.

A machine learning framework for risk prediction of multi-label cardiovascular events based on focused carotid plaque B-Mode ultrasound: A Canadian study

MOTIVATION

Machine learning (ML) algorithms can provide better cardiovascular event (CVE) prediction. However, ML algorithms are mostly explored for predicting a single CVE at a time. The objective of this study is to design and develop an ML-based system to predict multi-label CVEs, such as (i) coronary artery disease, (ii) acute coronary syndrome, and (iii) a composite CVE-a class of AtheroEdge 3.0 (ML) system.

METHODS

Focused carotid B-mode ultrasound and coronary angiography are performed on a group of 459 participants consisting of three cardiovascular labels. Initially, 23 risk predictors comprising (i) patients' demographics, (ii) clinical blood-biomarkers, and (iii) carotid ultrasound image-based phenotypes are collected. Six types of classification techniques comprising (a) four problem transformation methods (PTM) and (b) two algorithm adaptation methods (AAM) are used for multi-label CVE prediction. The performance of the proposed system is evaluated for accuracy, sensitivity, specificity, F1-score, and area-under-the-curve (AUC) using 10-fold cross-validation. The proposed system is also verified using another database of 522 participants.

RESULTS

For the primary database, PTM demonstrated a better multi-label CVE prediction than AAM (mean accuracy: 80.89% vs. 62.83%, mean AUC: 0.89 vs. 0.63), validating our hypothesis. The PTM-based binary relevance (BR) technique provided optimal performance in multi-label CVE prediction. The overall multi-label classification accuracy, sensitivity, specificity, F1-score, and AUC using BR are 81.2 ± 3.01%, 76.5 ± 8.8%, 83.8 ± 3.8%, 75.37 ± 5.8%, and 0.89 ± 0.02 (p < 0.0001), respectively. When used on the second Canadian database with seven cardiovascular events (acute coronary syndrome, myocardial infarction, angina, stroke, transient ischemic attack, heart failure, and death), the proposed system showed an accuracy of 96.36 ± 0.87% (AUC: 0.61 ± 0.06, p < 0.0001).

CONCLUSION

ML-based multi-label classification algorithms, such as binary relevance, yielded the best predictions for three cardiovascular endpoints.

Femoral plaque burden by ultrasound is a better indicator of significant coronary artery disease over ankle brachial index

The ankle-brachial index is a commonly used tool for identifying peripheral artery disease for cardiovascular risk stratification. An abnormal ankle-brachial index occurs only following extensive peripheral atherosclerosis occlusion, and thus has poor sensitivity for coronary atherosclerosis. There is a critical need for the development of tools that can detect risk prior to advanced stages of atherosclerosis. We sought to determine the sensitivity of femoral ultrasound for coronary artery disease. In this prospective, cross-sectional study, participants (n = 124) underwent ankle-brachial index measurement and femoral ultrasound for assessment of intima-media thickness, maximal plaque height, and total plaque area following coronary angiography. Receiver operating characteristic areas under the curve were plotted for identifying significant coronary artery disease (≥ 50% stenosis). Logistic regression was utilized to evaluate associations. 64% of participants had significant, angiography-confirmed coronary artery disease. Femoral ultrasound plaque area yielded the highest area under the curve for detecting significant coronary disease (area under the curve = 0.731). In contrast, an abnormal ankle-brachial index (≤ 0.90) produced an area under the curve of 0.568. Femoral ultrasound had a higher sensitivity (85%) than the ankle-brachial index (25%) for ruling out significant coronary artery disease. Both ankle-brachial index and femoral ultrasound have similar capacity to detect peripheral artery disease. Femoral ultrasound has a significantly greater discriminatory power than ankle-brachial index to detect clinically significant coronary artery disease. Ultrasound-captured femoral plaque burden directly delineates the extent of peripheral arterial disease and is better at ruling out significant coronary atherosclerosis than the ankle-brachial index.

Vascularized Carotid Atherosclerotic Plaque Models for the Validation of Novel Methods of Quantifying Intraplaque Neovascularization

BACKGROUND

Intraplaque neovascularization (IPN) in advanced lesions of the carotid artery has been linked to plaque progression and risk of rupture. Quantitative measurement of IPN may provide a more powerful tool for the detection of such "vulnerable" plaque than the current visual scoring method. The aim of this study was to develop a phantom platform of a neovascularized atherosclerotic plaque within a carotid artery to assess new methods of quantifying IPN.

METHODS

Ninety-two synthetic plaque models with various IPN architectures representing different ranges of IPN scoring were created and assessed using contrast-enhanced ultrasound. Intraplaque neovascularization volume was calculated from contrast infiltration in B mode. The plaque models were used to develop a testing platform for IPN quantification. A neovascularized enhancement ratio (NER) was calculated using commercially available software. The plaque model NERs were then compared to human plaque NERs (n = 42) to assess score relationship. Parametric mapping of dynamic intensity over time was used to differentiate IPN from calcified plaque regions.

RESULTS

A positive correlation between NER and IPN volume (rho = 0.45; P < .0001) was found in the plaque models. Enhancement of certain plaque model types showed that they resembled human plaques, with visual grade scores of 0 (NER mean difference = 1.05 ± SE 2.45; P = .67), 1 (NER mean difference = 0.22 ± SE 3.26; P = .95), and 2 (NER mean difference = -0.84 ± SE 3.33; P = .80). An optimal cutoff for NER (0.355) identified grade 2 human plaques with a sensitivity of 95% and specificity of 91%.

CONCLUSIONS

We developed a carotid artery model of neovascularized plaque and established a quantitative method for IPN using commercially available technology. We also developed an analysis method to quantify IPN in calcified plaques. This novel tool has the potential to improve clinical identification of vulnerable plaques, providing objective measures of IPN for cardiovascular risk assessment.

Role of artificial intelligence in cardiovascular risk prediction and outcomes: comparison of machine-learning and conventional statistical approaches for the analysis of carotid ultrasound features and intra-plaque neovascularization

The aim of this study was to compare machine learning (ML) methods with conventional statistical methods to investigate the predictive ability of carotid plaque characteristics for assessing the risk of coronary artery disease (CAD) and cardiovascular (CV) events. Focused carotid B-mode ultrasound, contrast-enhanced ultrasound, and coronary angiography were performed on 459 participants. These participants were followed for 30 days. Plaque characteristics such as carotid intima-media thickness (cIMT), maximum plaque height (MPH), total plaque area (TPA), and intraplaque neovascularization (IPN) were measured at baseline. Two ML-based algorithms-random forest (RF) and random survival forest (RSF) were used for CAD and CV event prediction. The performance of these algorithms was compared against (i) univariate and multivariate analysis for CAD prediction using the area-under-the-curve (AUC) and (ii) Cox proportional hazard model for CV event prediction using the concordance index (c-index). There was a significant association between CAD and carotid plaque characteristics [cIMT (odds ratio (OR) = 1.49, p = 0.03), MPH (OR = 2.44, p < 0.0001), TPA (OR = 1.61, p < 0.0001), and IPN (OR = 2.78, p < 0.0001)]. IPN alone reported significant CV event prediction (hazard ratio = 1.24, p < 0.0001). CAD prediction using the RF algorithm reported an improvement in AUC by ~ 3% over the univariate analysis with IPN alone (0.97 vs. 0.94, p < 0.0001). Cardiovascular event prediction using RSF demonstrated an improvement in the c-index by ~ 17.8% over the Cox-based model (0.86 vs. 0.73). Carotid imaging phenotypes and IPN were associated with CAD and CV events. The ML-based system is superior to the conventional statistically-derived approaches for CAD prediction and survival analysis.

Carotid intraplaque neovascularization predicts coronary artery disease and cardiovascular events

AIMS

It is thought that the majority of cardiovascular (CV) events are caused by vulnerable plaque. Such lesions are rupture prone, in part due to neovascularization. It is postulated that plaque vulnerability may be a systemic process and that vulnerable lesions may co-exist at multiple sites in the vascular bed. This study sought to examine whether carotid plaque vulnerability, characterized by contrast-enhanced ultrasound (CEUS)-assessed intraplaque neovascularization (IPN), was associated with significant coronary artery disease (CAD) and future CV events.

METHODS AND RESULTS

We investigated carotid IPN using carotid CEUS in 459 consecutive stable patients referred for coronary angiography. IPN was graded based on the presence and location of microbubbles within each plaque (0, not visible; 1, peri-adventitial; and 2, plaque core). The grades of each plaque were averaged to obtain an overall score per patient. Coronary plaque severity and complexity was also determined angiographically. Patients were followed for 30 days following their angiogram. This study found that a higher CEUS-assessed carotid IPN score was associated with significant CAD (≥50% stenosis) (1.8 ± 0.4 vs. 0.5 ± 0.6, P < 0.0001) and greater complexity of coronary lesions (1.7 ± 0.5 vs. 1.3 ± 0.8, P < 0.0001). Furthermore, an IPN score ≥1.25 could predict significant CAD with a high sensitivity (92%) and specificity (89%). The Kaplan-Meier analysis demonstrated a significantly higher proportion of participants having CV events with an IPN score ≥1.25 (P = 0.004).

CONCLUSION

Carotid plaque neovascularization was found to be predictive of significant and complex CAD and future CV events. CEUS-assessed carotid IPN is a clinically useful tool for CV risk stratification in high-risk cardiac patients.

The Role of Carotid and Femoral Plaque Burden in the Diagnosis of Coronary Artery Disease

PURPOSE OF REVIEW

With limitations of cardiovascular disease risk stratification by traditional risk factors, the role of noninvasive imaging techniques, such as vascular ultrasound, has emerged as a prominent utility for decision-making in coronary artery disease. A review of current guidelines and contemporary approaches for carotid and femoral plaque assessment is needed to better inform the diagnosis, management, and treatment of atherosclerosis in clinical practice.

RECENT FINDINGS

The recent consensus-based guidelines for carotid plaque assessment in coronary artery disease have been established, supported by some outcomes-based research. Currently, there is a gap of evidence on the use of femoral ultrasound to detect atherosclerosis, as well as predict adverse cardiovascular outcomes. The quantification and characterization of individualized plaque burden are important to stratify risk in asymptomatic or symptomatic atherosclerosis patients. Standardized quantification guidelines, supported by further outcomes-based research, are required to assess disease severity and progression.

Recommendations for the Assessment of Carotid Arterial Plaque by Ultrasound for the Characterization of Atherosclerosis and Evaluation of Cardiovascular Risk: From the American Society of Echocardiography

Atherosclerotic plaque detection by carotid ultrasound provides cardiovascular disease risk stratification. The advantages and disadvantages of two-dimensional (2D) and three-dimensional (3D) ultrasound methods for carotid arterial plaque quantification are reviewed. Advanced and emerging methods of carotid arterial plaque activity and composition analysis by ultrasound are considered. Recommendations for the standardization of focused 2D and 3D carotid arterial plaque ultrasound image acquisition and measurement for the purpose of cardiovascular disease stratification are formulated. Potential clinical application towards cardiovascular risk stratification of recommended focused carotid arterial plaque quantification approaches are summarized.

Point-of-care ultrasound in internal medicine: A position paper by the ultrasound working group of the European federation of internal medicine

Point-of-care ultrasound (POCUS) is increasingly used to assess medical patients. It has many uses in daily clinical practice, including improved diagnostic timeliness and accuracy, and providing information about a patient's prognosis and follow-up. It has been integrated into numerous specialities, but remains relatively undefined in internal medicine training programs. Ultrasonography is a useful tool in the standard clinical practice of internists in numerous clinical scenarios (Emergency Department, hospital ward, general and specific consultations, and home care). Although POCUS has been recently included in the European curriculum of internal medicine, there are differences between European internists in its use, ranging from not at all to well structured educational programs. The use of POCUS needs to be widespread in internal medicine departments, and to accomplish this we must encourage structured training. This document details the consensus-based recommendations by the European Federation of Internal Medicine (EFIM) Ultrasound working group. We establish POCUS core competencies and clinical settings for internists in a symptom-based approach. We also propose training requirements, providing a framework for training programs at a national level.

Circulating Gas6 is associated with reduced human carotid atherosclerotic plaque burden in high risk cardiac patients

OBJECTIVE

Pre-clinical studies suggest that growth arrest-specific protein 6 (Gas6), a member of the vitamin K dependent family of proteins, is implicated in atherosclerosis. A role for Gas6 in stabilizing atherosclerotic plaque has been suggested. Our aim was to determine the association between Gas6 and measures of carotid artery atherosclerosis in humans undergoing elective coronary angiography. Secondary aims were to determine the association between Gas6 and sex, diabetes, and obesity.

METHODS

In 204 outpatients referred for coronary angiography, EDTA plasma was collected and a focused carotid ultrasound performed. Degree of angiographic coronary artery disease was scored. Carotid intima media thickness as well as maximum plaque height, plaque area, and grayscale median were measured by vascular sonography. Gas6 was assessed by enzyme-linked immunosorbent assay.

RESULTS

We found that Gas6 concentrations were lower in males and were associated with diabetes, obesity, and lower kidney function. After adjustment for age, sex, kidney function, BMI and traditional cardiac risk factors; diabetes was associated with higher levels of Gas6, whilst there was a significant inverse relationship between Gas6 and total plaque area. Gas6 was inversely associated with maximum plaque height and total plaque area in adjusted multi-variable models.

CONCLUSIONS

We observed higher levels of Gas6 in participantswith adverse cardiovascular risk profiles (e.g. diabetes, obesity) yet Gas6 was independently associated with reduced plaque height and total plaque area. These findings suggest that Gas6 may play a role in human atherosclerotic plaque remodeling.

The Heart and Kidney: Abnormal Phosphate Homeostasis Is Associated With Atherosclerosis

Context

Phosphate has gained recognition as a risk factor for adverse cardiovascular outcomes, potentially due to accelerated vascular calcification. Fibroblast growth factor-23 (FGF-23) is a counter-regulatory hormone that increases renal phosphate excretion to maintain normal levels.

Objective

The purpose of the study was to determine the association of phosphate and FGF-23 to atherosclerosis.

Design and Setting

A prospective cohort study (n = 204) of outpatients referred for coronary angiography over of a 1-year recruitment period at the Kingston General Hospital.

Intervention

Blood was collected, and a focused carotid ultrasound was performed.

Main Outcome Measure

Degree of angiographic coronary artery disease was scored. Carotid maximum plaque height, total area, grayscale median, and tissue pixel distribution were measured. Plasma phosphate was assessed by mineral assay and FGF-23 by ELISA.

Results

Carotid plaque burden [total plaque area (TPA)] was associated with higher levels of phosphate (TPA, r = 0.20, P < 0.01) and FGF-23 (r = 0.19, P < 0.01). FGF-23 was associated with increased plaque % calcium-like tissue. Participants with no coronary artery disease had significantly lower phosphate levels. Phosphate was associated with higher grayscale median (GSM) in male subjects but with lower GSM in female subjects. FGF-23 was associated with increased plaque % fat in male subjects but increased plaque % calcium in female subjects.

Conclusions

Phosphate was independently associated with the severity of atherosclerosis in terms of plaque burden and composition. FGF-23 was associated with plaque calcification. These findings suggest that abnormal phosphate homeostasis may play an under-recognized but potentially modifiable role in cardiovascular disease.

Interatrial block predicts atrial fibrillation in patients with carotid and coronary artery disease

Background

Interatrial block (IAB) has been previously shown to predict atrial fibrillation (AF) in cardiac populations. This study sought to evaluate the relationship between IAB and new-onset AF in a population of patients undergoing clinically indicated coronary angiography who received carotid ultrasonography.

Methods

A population of 355 subjects undergoing coronary angiography and carotid ultrasound were retrospectively studied. Common carotid artery (CCA) far-wall intima-media thickness (CIMT), and total carotid plaque area were measured. Coronary artery disease was measured by angiography and IAB by electrocardiograph (ECG).

Results

The mean population age was 64.4 years, 70.4% male, mean BMI 29.9 kg/m². IAB was a predictor of new-onset AF (OR =2.40, 95% CI: 1.33-4.29; P=0.003). There was a significant difference in AF free survival time between patients with IAB and without IAB via Cox proportional hazard analysis [52.9 months (95% CI: 47.1-58.7 months) vs. 62.6 months (95% CI: 58.8-66.5 months); P=0.006]. Patients with IAB had a significantly greater CIMT (0.883±0.193 vs. 0.829±0.192 mm; P=0.013) and a higher prevalence of significant (>70%) right coronary artery lesions than patients without (45.8% vs. 34.4%; P=0.026). Significant predictors of IAB on multivariate analysis were BMI ≥30 kg/m² (OR =3.14, 95% CI: 1.14-6.71, P=0.003), male sex (OR =1.78, 95% CI: 1.05-3.03, P=0.034), increased mean CIMT (per 0.1 mm increase) (OR =1.75, 95% CI: 1.00-3.07, P=0.050) and increased age (per 10-year increase) (OR =1.46, 95% CI: 1.14-1.88, P=0.003).

Conclusions

IAB is a predictor of new-onset AF in patients with carotid and coronary artery disease. Both carotid and coronary artery disease are associated with a higher prevalence of IAB.

Positioning document on incorporating point-of-care ultrasound in Internal Medicine departments

This positioning document describes the most important aspects of clinical ultrasonography in the internal medicine setting, from its fundamental indications to the recommended training period. There is no question as to the considerable usefulness of this tool in the standard clinical practice of internists in numerous clinical scenarios and settings (emergencies, hospital ward, general and specific consultations and home care). Ultrasonography has a relevant impact on the practitioner's ability to resolve issues, increasing diagnostic reliability and safety and providing important information on the prognosis and progression. In recent years, ultrasonography has been incorporated as a tool in undergraduate teaching, with excellent results. The use of ultrasonography needs to be widespread. To accomplish this, we must encourage structured training and the acquisition of equipment. This document was developed by the Clinical Ultrasonography Workgroup and endorsed by the Spanish Society of Internal Medicine.