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.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.

Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.

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.

The Canadian Women's Heart Health Alliance ATLAS on the Epidemiology, Diagnosis, and Management of Cardiovascular Disease in Women - Chapter 8: Knowledge Gaps and Status of Existing Research Programs in Canada.

Despite significant progress in medical research and public health efforts, gaps in knowledge of women's heart health remain across epidemiology, presentation, management, outcomes, education, research, and publications. Historically, heart disease was viewed primarily as a condition in men and male individuals, leading to limited understanding of the unique risks and symptoms that women experience. These knowledge gaps are particularly problematic because globally heart disease is the leading cause of death for women. Until recently, sex and gender have not been addressed in cardiovascular research, including in preclinical and clinical research. Recruitment was often limited to male participants and individuals identifying as men, and data analysis according to sex or gender was not conducted, leading to a lack of data on how treatments and interventions might affect female patients and individuals who identify as women differently. This lack of data has led to suboptimal treatment and limitations in our understanding of the underlying mechanisms of heart disease in women, and is directly related to limited awareness and knowledge gaps in professional training and public education. Women are often unaware of their risk factors for heart disease or symptoms they might experience, leading to delays in diagnosis and treatments. Additionally, health care providers might not receive adequate training to diagnose and treat heart disease in women, leading to misdiagnosis or undertreatment. Addressing these knowledge gaps requires a multipronged approach, including education and policy change, built on evidence-based research. In this chapter we review the current state of existing cardiovascular research in Canada with a specific focus on women.

En dépit des avancées importantes de la recherche médicale et des efforts en santé publique, il reste des lacunes dans les connaissances sur la santé cardiaque des femmes sur les plans de l'épidémiologie, du tableau clinique, de la prise en charge, des résultats, de l'éducation, de la recherche et des publications. Du point de vue historique, la cardiopathie a d'abord été perçue comme une maladie qui touchait les hommes et les individus de sexe masculin. De ce fait, la compréhension des risques particuliers et des symptômes qu'éprouvent les femmes est limitée. Ces lacunes dans les connaissances posent particulièrement problème puisqu'à l'échelle mondiale la cardiopathie est la cause principale de décès chez les femmes. Jusqu'à récemment, la recherche en cardiologie, notamment la recherche préclinique et clinique, ne portait pas sur le sexe et le genre. Le recrutement souvent limité aux participants masculins et aux individus dont l'identité de genre correspond au sexe masculin et l'absence d'analyses de données en fonction du sexe ou du genre ont eu pour conséquence un manque de données sur la façon dont les traitements et les interventions nuisent aux patientes féminines et aux individus dont l'identité de genre correspond au sexe féminin, et ce, de façon différente. Cette absence de données a mené à un traitement sous-optimal et à des limites de notre compréhension des mécanismes sous-jacents de la cardiopathie chez les femmes, et est directement reliée à nos connaissances limitées, et à nos lacunes en formation professionnelle et en éducation du public. Le fait que les femmes ne connaissent souvent pas leurs facteurs de risque de maladies du cœur ou les symptômes qu'elles peuvent éprouver entraîne des retards de diagnostic et de traitements. De plus, le fait que les prestataires de soins de santé ne reçoivent pas la formation adéquate pour poser le diagnostic et traiter la cardiopathie chez les femmes les mène à poser un mauvais diagnostic ou à ne pas traiter suffisamment. Pour pallier ces lacunes de connaissances, il faut une approche à plusieurs volets, qui porte notamment sur l'éducation et les changements dans les politiques, et qui repose sur la recherche fondée sur des données probantes. Dans ce chapitre, nous passons en revue l'état actuel de la recherche existante sur les maladies cardiovasculaires au Canada, plus particulièrement chez les femmes.

GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides.

Due to the intricate relationship between the small non-coding ribonucleic acid (miRNA) sequences, the classification of miRNA species, namely Human, Gorilla, Rat, and Mouse is challenging. Previous methods are not robust and accurate. In this study, we present AtheroPoint's GeneAI 3.0, a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep learning (EDL) frameworks. GeneAI 3.0 utilized five conventional (Entropy, Dissimilarity, Energy, Homogeneity, and Contrast), and three contemporary (Shannon entropy, Hurst exponent, Fractal dimension) features, to generate a composite feature set from given miRNA sequences which were then passed into our ML and DL classification framework. A set of 11 new classifiers was designed consisting of 5 EML and 6 EDL for binary/multiclass classification. It was benchmarked against 9 solo ML (SML), 6 solo DL (SDL), 12 hybrid DL (HDL) models, resulting in a total of 11 + 27 = 38 models were designed. Four hypotheses were formulated and validated using explainable AI (XAI) as well as reliability/statistical tests. The order of the mean performance using accuracy (ACC)/area-under-the-curve (AUC) of the 24 DL classifiers was: EDL > HDL > SDL. The mean performance of EDL models with CNN layers was superior to that without CNN layers by 0.73%/0.92%. Mean performance of EML models was superior to SML models with improvements of ACC/AUC by 6.24%/6.46%. EDL models performed significantly better than EML models, with a mean increase in ACC/AUC of 7.09%/6.96%. The GeneAI 3.0 tool produced expected XAI feature plots, and the statistical tests showed significant p-values. Ensemble models with composite features are highly effective and generalized models for effectively classifying miRNA sequences.

A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool.

BACKGROUND: Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD. OBJECTIVE: This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP3) framework benefiting the pharmaceutical paradigm. METHOD: The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdgeTM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers. CONCLUSIONS: Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdgeTM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm.

Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review.

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.

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.

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.

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.

Vascular imaging of atherosclerosis: Strengths and weaknesses.

Atherosclerosis is an inflammatory disease that can lead to several complications such as ischemic heart disease, stroke, and peripheral vascular disease. Therefore, researchers and clinicians rely heavily on the use of imaging modalities to identify, and more recently, quantify the burden of atherosclerosis in the aorta, carotid arteries, coronary arteries, and peripheral vasculature. These imaging techniques vary in invasiveness, cost, resolution, radiation exposure, and presence of artifacts. Consequently, a detailed understanding of the risks and benefits of each technique is crucial prior to their introduction into routine cardiovascular screening. Additionally, recent research in the field of microvascular imaging has proven to be important in the field of atherosclerosis. Using techniques such as contrast-enhanced ultrasound and superb microvascular imaging, researchers have been able to detect blood vessels within a plaque lesion that may contribute to vulnerability and rupture. This paper will review the strengths and weaknesses of the various imaging techniques used to measure atherosclerotic burden. Furthermore, it will discuss the future of advanced imaging modalities as potential biomarkers for atherosclerosis.

Multiclass machine learning vs. conventional calculators for stroke/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: a 500 participants study.

Machine learning (ML)-based algorithms for cardiovascular disease (CVD) risk assessment have shown promise in clinical decisions. However, they usually predict binary events using only conventional risk factors. Our overall goal was to develop the "multiclass machine learning (MCML)-based algorithms" (labelled as AtheroEdge 3.0ML) and assess whether considering carotid ultrasound imaging fused with conventional risk factors can provide better CVD/stroke risk prediction than conventional CVD risk calculators (CCVRC). Carotid ultrasound and coronary angiography were performed on 500 participants. Stenosis in the coronary arteries was used to assign participants a coronary angiographic score (CAS). CVD/stroke risk was determined using three types of MCML algorithms: (i) support vector machine (SVM), (ii) random forest (RF), and (iii) extreme gradient boost (XGBoost). The performance of CVD risk assessment using MCML and CCVRC (such as Framingham Risk Score, the Systematic Coronary Risk Evaluation score, and the Atherosclerotic CVD) was evaluated on test patients against the CAS as the gold standard for each class using the area-under-the-curve (AUC) and classification accuracy. The mean percentage improvement in AUC and the mean absolute improvement in accuracy over CCVRC using 90% training and 10% testing protocol (labelled as K10) were ~ 105% and ~ 28%, respectively. Of all the three MCML systems, RF showed the best performance. Further, carotid image phenotypes showed the most effective clinical feature in AtheroEdge 3.0ML performance. The AtheroEdge 3.0ML using carotid imaging are reliable, accurate, and superior to traditional CVD risk scoring methods for predicting the CVD/stroke risk due to coronary artery disease.

Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort.

This study developed an office-based cardiovascular risk calculator using a machine learning (ML) algorithm that utilized a focused carotid ultrasound. The design of this study was divided into three steps. The first step involved collecting 18 office-based biomarkers consisting of six clinical risk factors (age, sex, body mass index, systolic blood pressure, diastolic blood pressure, and smoking) and 12 carotid ultrasound image-based phenotypes. The second step consisted of the design of an ML-based cardiovascular risk calculator-called "AtheroEdge Composite Risk Score 2.0" (AECRS2.0ML) for risk stratification, considering chronic kidney disease (CKD) as the surrogate endpoint of cardiovascular disease. The last step consisted of comparing AECRS2.0ML against the currently utilized office-based CVD calculators, namely the Framingham risk score (FRS) and the World Health Organization (WHO) risk scores. A cohort of 379 Asian-Indian patients with type-2 diabetes mellitus, hypertension, and chronic kidney disease (stage 1 to 5) were recruited for this cross-sectional study. From this retrospective cohort, 758 ultrasound scan images were acquired from the far walls of the left and right common carotid arteries [mean age = 55 ± 10.8 years, 67.28% males, 91.82% diabetic, 86.54% hypertensive, and 83.11% with CKD]. The mean office-based cardiovascular risk estimates using FRS and WHO calculators were 26% and 19%, respectively. AECRS2.0ML demonstrated a better risk stratification ability having a higher area-under-the-curve against FRS and WHO by ~30% (0.871 vs. 0.669) and ~ 20% (0.871 vs. 0.727), respectively. The office-based machine-learning cardiovascular risk-stratification tool (AECRS2.0ML) shows superior performance compared to currently available conventional cardiovascular risk calculators.

The use of ultrasound to assess aortic biomechanics: Implications for aneurysm and dissection.

Arterial stiffening, which occurs when conduit arteries thicken and lose elasticity, has been associated with cardiovascular disease and increased risk for future cardiovascular events. Specifically, aortic stiffening plays a large role in the pathogenesis of vascular diseases, such as aneurysm formation and dissection. Current parameters used to assess risk of aortic rupture include absolute diameter and growth rate. However, these properties lack the reliability required to accurately risk-stratify patients. As with any elastic conduit, it is important to assess the biomechanical properties of the aorta in order to assess cardiovascular risk and prevent disease progression. There are several invasive and noninvasive methods by which stiffness of the large arteries can be assessed. Of particular interest are ultrasound-based methods, such as tissue Doppler imaging and speckle-tracking echocardiography, due to their noninvasive and feasible nature. In this review, we summarize studies demonstrating utility of noninvasive ultrasound imaging methods for measuring aortic biomechanics for the assessment and management of aortic aneurysms.

Combined Femoral and Carotid Plaque Burden Identifies Obstructive Coronary Artery Disease in Women.

BACKGROUND: It remains difficult to assess cardiovascular risk in symptomatic women. The development of femoral plaque precedes adverse cardiovascular events. However, associations of femoral plaque burden with coronary artery disease (CAD) severity and extent are unknown. The aim of this study was to determine sex-specific plaque quantification markers by vascular ultrasound for identifying significant, obstructive CAD. METHODS: In this cross-sectional study, 500 participants (34% women) underwent carotid and femoral ultrasound following coronary angiography. Maximal plaque height and total plaque area were quantified. Logistic regression was used to determine associations of plaque burden with significant, obstructive CAD (≥50% stenosis), when adjusted for age and cardiac risk factors. CAD prediction was evaluated using receiver operating characteristic areas under the curve (AUCs). RESULTS: Two hundred thirty-one men (70%) and 78 women (46%) had significant CAD. A combined assessment of femoral bifurcation and carotid maximal plaque height was the most accurate identifier of CAD in men (AUC = 0.773, cutoff ≥ 2.7 mm, 87% sensitivity, 53% specificity) but a poorer indicator of CAD in women (AUC = 0.659, P < .01). In contrast, the strongest identification of CAD in women was achieved by a combined analysis of common femoral and carotid total plaque area (AUC = 0.764, cutoff ≥ 42.0 mm2, 86% sensitivity, 53% specificity). At this value, more than half of women with false-positive stress test results were correctly identified as having no significant CAD. CONCLUSION: Combined femoral and carotid plaque burden assessments effectively ruled out significant disease in both sexes. Vascular ultrasound may have particular value for cardiovascular risk stratification in women, in whom traditional screening tools are less effective.

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.