MultiNet 2.0: A lightweight attention-based deep learning network for stenosis measurement in carotid ultrasound scans and cardiovascular risk assessment.

BACKGROUND: Cardiovascular diseases (CVD) cause 19 million fatalities each year and cost nations billions of dollars. Surrogate biomarkers are established methods for CVD risk stratification; however, manual inspection is costly, cumbersome, and error-prone. The contemporary artificial intelligence (AI) tools for segmentation and risk prediction, including older deep learning (DL) networks employ simple merge connections which may result in semantic loss of information and hence low in accuracy. METHODOLOGY: We hypothesize that DL networks enhanced with attention mechanisms can do better segmentation than older DL models. The attention mechanism can concentrate on relevant features aiding the model in better understanding and interpreting images. This study proposes MultiNet 2.0 (AtheroPoint, Roseville, CA, USA), two attention networks have been used to segment the lumen from common carotid artery (CCA) ultrasound images and predict CVD risks. RESULTS: The database consisted of 407 ultrasound CCA images of both the left and right sides taken from 204 patients. Two experts were hired to delineate borders on the 407 images, generating two ground truths (GT1 and GT2). The results were far better than contemporary models. The lumen dimension (LD) error for GT1 and GT2 were 0.13±0.08 and 0.16±0.07 mm, respectively, the best in market. The AUC for low, moderate and high-risk patients' detection from stenosis data for GT1 were 0.88, 0.98, and 1.00 respectively. Similarly, for GT2, the AUC values for low, moderate, and high-risk patient detection were 0.93, 0.97, and 1.00, respectively. The system can be fully adopted for clinical practice in AtheroEdge™ model by AtheroPoint, Roseville, CA, USA.

Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data.

BACKGROUND: The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0HDL (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well the features obtained from carotid plaques determine the risk of CVD. We hypothesize that hybrid deep learning (HDL) will outperform unidirectional deep learning, bidirectional deep learning, and machine learning (ML) paradigms. METHODOLOGY: 500 people who had undergone targeted carotid B-mode ultrasonography and coronary angiography were included in the proposed study. ML feature selection was carried out using three different methods, namely principal component analysis (PCA) pooling, the chi-square test (CST), and the random forest regression (RFR) test. The unidirectional and bidirectional deep learning models were trained, and then six types of novel HDL-based models were designed for CVD risk stratification. The AtheroEdge™ 3.0HDL was scientifically validated using seen and unseen datasets while the reliability and statistical tests were conducted using CST along with p-value significance. The performance of AtheroEdge™ 3.0HDL was evaluated by measuring the p-value and area-under-the-curve for both seen and unseen data. RESULTS: The HDL system showed an improvement of 30.20% (0.954 vs. 0.702) over the ML system using the seen datasets. The ML feature extraction analysis showed 70% of common features among all three methods. The generalization of AtheroEdge™ 3.0HDL showed less than 1% (p-value < 0.001) difference between seen and unseen data, complying with regulatory standards. CONCLUSIONS: The hypothesis for AtheroEdge™ 3.0HDL was scientifically validated, and the model was tested for reliability and stability and is further adaptable clinically.

Carotid Plaque Score is Associated with 10-year Major Adverse Cardiovascular Events in Low-Intermediate Risk Patients Referred to a General Cardiology Community Clinic.

AIMS: Atherosclerotic carotid plaque assessments have not been integrated into routine clinical practice due to the time-consuming nature of both imaging and measurements. Plaque score, Rotterdam method, is simple, quick, and only requires 4-6 B-mode ultrasound images. The aim was to assess the benefit of plaque score in a community cardiology clinic to identify patients at risk for major adverse cardiovascular events (MACE). METHODS AND RESULTS: Patients ≥40 years presenting for risk assessment were given a carotid ultrasound. Exclusions included a history of vascular disease or MACE and being >75 years. Kaplan-Meier curves and hazard ratios were performed. The left and right common carotid artery (CCA), bulb, and internal carotid artery (ICA) were given 1 point per segment if plaque present (plaque score 0 to 6). Administrative data holdings at ICES were used for 10-year event follow-up. Of 8,472 patients, 60% were females (n = 5,121). Plaque was more prevalent in males (64% vs 53.9%; P <0.0001). The 10-year MACE cumulative incidence estimate was 6.37% with 276 events (males 6.9 % vs females 6.0%; P = 0.004). Having both maximal CCA IMT <1.00 mm and plaque score = 0, was associated with less events. A plaque score <2 was associated with a low 10-year event rate (4.1%) compared to 2-4 (8.7%) and 5-6 (20%). CONCLUSION: A plaque score ≥2 can re-stratify low-intermediate risk patients to a higher risk for events. Plaque score may be used as a quick assessment in a cardiology office to guide treatment management of patients.

UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review.

Cardiovascular disease (CVD) diagnosis and treatment are challenging since symptoms appear late in the disease's progression. Despite clinical risk scores, cardiac event prediction is inadequate, and many at-risk patients are not adequately categorised by conventional risk factors alone. Integrating genomic-based biomarkers (GBBM), specifically those found in plasma and/or serum samples, along with novel non-invasive radiomic-based biomarkers (RBBM) such as plaque area and plaque burden can improve the overall specificity of CVD risk. This review proposes two hypotheses: (i) RBBM and GBBM biomarkers have a strong correlation and can be used to detect the severity of CVD and stroke precisely, and (ii) introduces a proposed artificial intelligence (AI)-based preventive, precision, and personalized ( aiP 3 ) CVD/Stroke risk model. The PRISMA search selected 246 studies for the CVD/Stroke risk. It showed that using the RBBM and GBBM biomarkers, deep learning (DL) modelscould be used for CVD/Stroke risk stratification in the aiP 3 framework. Furthermore, we present a concise overview of platelet function, complete blood count (CBC), and diagnostic methods. As part of the AI paradigm, we discuss explainability, pruning, bias, and benchmarking against previous studies and their potential impacts. The review proposes the integration of RBBM and GBBM, an innovative solution streamlined in the DL paradigm for predicting CVD/Stroke risk in the aiP 3 framework. The combination of RBBM and GBBM introduces a powerful CVD/Stroke risk assessment paradigm. aiP 3 model signifies a promising advancement in CVD/Stroke risk assessment.

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.

Carotid Ultrasound-Based Plaque Score for the Allocation of Aspirin for the Primary Prevention of Cardiovascular Disease Events: The Multi-Ethnic Study of Atherosclerosis and the Atherosclerosis Risk in Communities Study.

BACKGROUND: Coronary artery calcium testing using noncontrast cardiac computed tomography is a guideline-indicated test to help refine eligibility for aspirin in primary prevention. However, access to cardiac computed tomography remains limited, with carotid ultrasound used much more often internationally. We sought to update the role of aspirin allocation in primary prevention as a function of subclinical carotid atherosclerosis. METHODS AND RESULTS: The study included 11 379 participants from the MESA (Multi-Ethnic Study of Atherosclerosis) and ARIC (Atherosclerosis Risk in Communities) studies. A harmonized carotid plaque score (range, 0-6) was derived using the number of anatomic sites with plaque from the left and right common, bifurcation, and internal carotid artery on ultrasound. The 5-year number needed to treat and number needed to harm as a function of the carotid plaque score were calculated by applying a 12% relative risk reduction in atherosclerotic cardiovascular disease (ASCVD) events and 42% relative increase in major bleeding events related to aspirin use, respectively. The mean age was 57 years, 57% were women, 23% were Black, and the median 10-year ASCVD risk was 12.8%. The 5-year incidence rates (per 1000 person-years) were 5.5 (4.9-6.2) for ASCVD and 1.8 (1.5-2.2) for major bleeding events. The overall 5-year number needed to treat with aspirin was 306 but was 2-fold lower for individuals with carotid plaque versus those without carotid plaque (212 versus 448). The 5-year number needed to treat was less than the 5-year number needed to harm when the carotid plaque score was ≥2 for individuals with ASCVD risk 5% to 20%, whereas the presence of any carotid plaque demarcated a favorable risk-benefit for individuals with ASCVD risk >20%. CONCLUSIONS: Quantification of subclinical carotid atherosclerosis can help improve the allocation of aspirin therapy.

Personalized Intervention Based on Early Detection of Atherosclerosis: JACC State-of-the-Art Review.

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide and challenges the capacity of health care systems globally. Atherosclerosis is the underlying pathophysiological entity in two-thirds of patients with CVD. When considering that atherosclerosis develops over decades, there is potentially great opportunity for prevention of associated events such as myocardial infarction and stroke. Subclinical atherosclerosis has been identified in its early stages in young individuals; however, there is no consensus on how to prevent progression to symptomatic disease. Given the growing burden of CVD, a paradigm shift is required-moving from late management of atherosclerotic CVD to earlier detection during the subclinical phase with the goal of potential cure or prevention of events. Studies must focus on how precision medicine using imaging and circulating biomarkers may identify atherosclerosis earlier and determine whether such a paradigm shift would lead to overall cost savings for global health.

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.

RéSUMé: OBJECTIFS: Le score HEART est un outil de décision clinique qui stratifie les patients en catégories de risque faible, modéré et élevé d'événements cardiaques indésirables majeurs à l'urgence (ED), mais ne peut pas identifier les maladies cardiovasculaires sous-jacentes chez les patients sans antécédents. La présence d'athérosclérose peut facilement être détectée au chevet du patient à l'aide de l'échographie carotide. La quantification de la plaque est bien établie et la composition de la plaque peut être évaluée à l'aide d'une analyse échographique de la distribution des pixels en niveaux de gris. Cette étude visait à déterminer si la charge et/ou la composition de la plaque carotidienne étaient corrélées avec le risque d'événements et pouvaient améliorer la sensibilité du score HEART chez les patients souffrant de douleurs thoraciques stratifiés. MéTHODES: Le score HEART a été calculé sur la base des antécédents, de l'électrocardiogramme, de l'âge, des facteurs de risque et de la troponine initiale chez les patients présentant une douleur thoracique à l'urgence (n = 321). L'échographie carotidienne focalisée a été effectuée, et la hauteur maximale de la plaque et la surface totale de la plaque ont été utilisées pour déterminer la charge de plaque (quantité). La composition de la plaque (% de sang, de graisse, de muscle, de tissu fibreux, de type calcique) a été évaluée par analyse de la distribution des pixels. RéSULTATS: La hauteur et la surface de la plaque carotide ont augmenté avec le score HEART (p<0,0001). Le pourcentage de plaque carotide fibreuse et le pourcentage de calcium ont également augmenté avec le score HEART. Le score HEART avait une zone plus élevée sous la courbe (ASC = 0,84) pour prédire les événements de 30 jours par rapport aux seules variables de la plaque (CCU < 0,70). L'intégration de la quantité de plaque dans le score HEART a légèrement augmenté la sensibilité au test (62 % à 69 %) pour les événements de 30 jours et a reclassé 11 participants à risque modéré à risque élevé (score de 7 à 10). CONCLUSION: La charge de plaque avec des caractéristiques de composition avancées (fibreuse et calcique) était associée à une augmentation du score HEART. Intégrer l'évaluation de la plaque dans le score HEART a identifié l'athérosclérose subclinique chez les patients à risque modéré.

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 Application of Point of Care Ultrasound to Screen for Pulmonary Hypertension: A Narrative Review.

BACKGROUND: Pulmonary Hypertension (PH) is a condition with several cardiopulmonary etiologies that has the potential of progressing to right heart failure without proper intervention. After a history, physical exam, and investigations, cases of suspected PH typically undergo imaging via a transthoracic echocardiogram (TTE). This is a resource-intensive procedure that is less accessible in remote communities. However, point of care ultrasound (POCUS), a portable ultrasound administered at the bedside, has potential to aid in the diagnostic process of PH. METHODS: The MEDLINE, Embase, and CENTRAL databases were searched to screen the intersection of POCUS and PH. Studies involved adult patients, and only English articles were accepted. Reviews, case reports, unfinished research, and conference abstracts were excluded. Our aim was to identify primary studies that correlated POCUS scan results and additional clinical findings related to PH. RESULTS: Nine studies were included after our search. In these studies, POCUS was effective in identifying dilatation of inferior vena cava (IVC); internal jugular vein (IJV); and hepatic, portal, and intrarenal veins in patients with PH. The presence of pericardial effusion, pleural effusion, or b-lines on POCUS are also associated with PH. CONCLUSIONS: This review suggests important potential for the use of POCUS in the initial screening of PH. IVC and basic cardiopulmonary POCUS exams are key for PH screening in patients with dyspnea. Right-heart dilatation can be visualized, and peripheral veins may be scanned based on clinical suspicion. POCUS offers screening as an extension of a physical exam, with direct visualization of cardiac morphology. However, more studies are required to develop a statistically validated POCUS exam for PH diagnosis. More studies should also be conducted at the primary-care level to evaluate the value of screening using POCUS for PH in less-differentiated patients.

Patient Involvement in Care, Psychosocial Outcomes, and Quality of Life in Hypertrophic Cardiomyopathy: A Pilot Study.

This cross-sectional study evaluated the impact of patient involvement in care (PIC) on psychosocial outcomes and health-related quality of life (HRQoL) in patients with hypertrophic cardiomyopathy (HCM) (n = 34). Patients with low-to-moderate PIC were older than those with high PIC (66.8 years vs 57.3 years; P = 0.04). PIC was negatively correlated with depressive symptoms (r = -0.39; P = 0.02) and positively correlated with heart-focused attention (r = 0.39; P = 0.02). No significant correlations were observed between PIC and HRQoL. Greater PIC was associated with reduced depressive symptoms but increased cardiac anxiety. Future studies should investigate the relationship between PIC and HRQoL in a larger cohort.

Cette étude transversale visait à évaluer l'effet de la participation du patient à ses soins sur les issues psychosociales et la qualité de vie liée à la santé chez les patients atteints de cardiomyopathie hypertrophique (CMH) (n = 34). Les patients qui participaient peu ou modérément à leurs soins étaient plus âgés que ceux qui y participaient activement (66,8 ans vs 57,3 ans; p = 0,04). Il y a une corrélation négative entre la participation du patient aux soins et les symptômes dépressifs (r = -0,39; p = 0,02) et une corrélation positive entre la participation aux soins et l'attention portée au cœur (r = 0,39; p = 0,02). Aucune corrélation notable n'a été observée entre la participation du patient à ses soins et la qualité de vie liée à la santé. Une grande participation du patient à ses soins a été associée à une réduction des symptômes dépressifs, mais à une anxiété cardiaque accrue. D'autres études sont nécessaires pour examiner la relation entre la participation du patient à ses soins et la qualité de vie liée à la santé au sein d'une cohorte plus importante.

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.

Mediators of the Association Between Socioeconomic Status and Survival After Out-of-Hospital Cardiac Arrest: A Systematic Review.

Low socioeconomic status (SES) is associated with poor outcomes after out-of-hospital cardiac arrest (OHCA). Patient characteristics, care processes, and other contextual factors may mediate the association between SES and survival after OHCA. Interventions that target these mediating factors may reduce disparities in OHCA outcomes across the socioeconomic spectrum. This systematic review identified and quantified mediators of the SES-survival after OHCA association. Electronic databases (MEDLINE, Embase, PubMed, Web of Science) and grey literature sources were searched from inception to July or August 2023. Observational studies of OHCA patients that conducted mediation analyses to evaluate potential mediators of the association between SES (defined by income, education, occupation, or a composite index) and survival outcomes were included. A total of 10 studies were included in this review. Income (n = 9), education (n = 4), occupation (n = 1), and composite indices (n = 1) were used to define SES. The proportion of OHCA cases that had bystander involvement, presented with an initial shockable rhythm, and survived to hospital discharge or 30 days increased with higher SES. Common mediators of the SES-survival association that were evaluated included initial rhythm (n = 6), emergency medical services response time (n = 5), and bystander cardiopulmonary resuscitation (n = 4). Initial rhythm was the most important mediator of this association, with a median percent excess risk explained of 37.4% (range 28.6%-40.0%; n = 5; 1 study reported no mediation) and mediation proportion of 41.8% (n = 1). To mitigate socioeconomic disparities in outcomes after OHCA, interventions should target potentially modifiable mediators, such as initial rhythm, which may involve improving bystander awareness of OHCA and the need for prompt resuscitation.

ACR Appropriateness Criteria® Pulsatile Abdominal Mass, Suspected Abdominal Aortic Aneurysm: 2023 Update.

Abdominal aortic aneurysm (AAA) is defined as abnormal dilation of the infrarenal abdominal aortic diameter to 3.0 cm or greater. The natural history of AAA consists of progressive expansion and potential rupture. Although most AAAs are clinically silent, a pulsatile abdominal mass identified on physical examination may indicate the presence of an AAA. When an AAA is suspected, an imaging study is essential to confirm the diagnosis. This document reviews the relative appropriateness of various imaging procedures for the initial evaluation of suspected AAA. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

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.

Impact of transcatheter aortic valve implantation on circulating von Willebrand factor in patients with severe aortic stenosis.

INTRODUCTION: Severe aortic stenosis (AS) can lead to degradation of high molecular weight (HMW) von Willebrand factor (VWF) which can result in haemostatic abnormalities. While studies have explored changes in VWF profiles before and after surgical aortic valve replacement (SAVR), the longer-term changes in VWF profiles pre- and post-transcatheter aortic valve implantation (TAVI) are less understood. AIM: Our primary objective was to identify differences in VWF multimer profiles and VWF function pre-TAVI and 1-month post-TAVI. Our secondary objective was to correlate VWF markers with measures of AS severity. METHODS: Adult patients with severe AS referred for TAVI at our institution were prospectively enrolled in this cohort study. Blood samples were collected for plasma analysis at three time points for all patients: 1 day pre-TAVI, 3 days post-TAVI, and 1-month post-TAVI. VWF antigen, activity, propeptide, collagen binding, multimers, and factor VIII coagulant activity were determined at each time point. Correlations between VWF parameters and severity of AS were assessed. RESULTS: Twenty participants (15 males, five females) with severe AS were recruited for the study. There was a significant increase in HMW VWF between pre-procedure and 1-month post-TAVI (p < .05). There was a transient increase in VWF antigen levels and activity at 3-days post TAVI that decreased to pre-TAVI levels at 1-month. There were no statistically significant correlations between VWF markers and AS severity. CONCLUSIONS: This is the first study to elucidate longer-term (>1 week) improvements in HMW VWF after a TAVI procedure in severe AS patients.