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.

Female Risk Factors for Post-Infarction Depression and Anxiety: Trial Design.

INTRODUCTION: Female patients are at elevated risk for adverse mental health outcomes following hospital admission for ischemic heart disease. These psychosocial characteristics are correlated with unacceptably higher rates of cardiovascular (CV) morbidity and mortality. Guidelines to address mental health following acute coronary syndrome (ACS) can only be developed with the aid of studies elucidating which subgroups of female patients are at the highest risk. METHODS/DESIGN: The Female Risk factors for post-Infarction Depression and Anxiety (FRIDA) Study is a prospective multicenter questionnaire-based study of female participants admitted to hospital with ACS. Data are collected within 72 h of admission as well as at 3 and 6 months. At baseline, participants complete a sociodemographic questionnaire, social support survey, and Hospital Depression and Anxiety Scale (HADS). Follow-up will consist of a demographic questionnaire, HADS, changes to health status, and quality of life indicators. Statistical analysis will include descriptive and inferential methods to observe baseline distributions and significance between groups. DISCUSSION/CONCLUSION: Our primary outcome is to determine if specific CV and sociodemographic factors correlate with increased depression and anxiety scores (HADS-D >7; HADS-A >7) at baseline. Our secondary aim is to determine if increased HADS scores at baseline and follow-up correlate with 3 and 6-month health and quality of life outcomes. A total of 2,000 patients will be enrolled across seven study sites. The aim of the FRIDA Study is to understand which groups of female patients have the highest rates of depression and anxiety following ACS to better inform care.

Community Interventions for Out-of-Hospital Cardiac Arrest in Resource-Limited Settings: A Scoping Review Across Low, Middle, and High-Income Countries.

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) is a major global health challenge, characterized by poor survival outcomes worldwide. Resource-limited settings are burdened with suboptimal emergency response and worse outcomes than high-resource areas. Engaging the community in the response to OHCA has the potential to improve outcomes, although an overview of community interventions in resource-limited settings has not been provided. OBJECTIVE: This review evaluated the scope of community-based OHCA interventions in resource-limited settings. METHODS: Literature searches in electronic databases (MEDLINE, EMBASE, Global Health, CINAHL, Cochrane Central Register of Controlled Clinical Trials) and grey literature sources were performed. Abstract screening, full-text review, and data extraction of eligible studies were conducted independently by two reviewers. The PCC (Population, Concept, and Context) framework was used to assess study eligibility. Studies that evaluated community-based interventions for laypeople (Population), targeting emergency response activation, cardiopulmonary resuscitation (CPR), or automated external defibrillator (AED) use (Concept) in resource-limited settings (Context) were included. Resource-limited settings were identified by financial pressures (low-income or lower-middle-income country, according to World Bank data on year of publication) or geographical factors (setting described using keywords indicative of geographical remoteness in upper-middle-income or high-income country). RESULTS: Among 14,810 records identified from literature searches, 60 studies from 28 unique countries were included in this review. Studies were conducted in high-income (n = 35), upper-middle-income (n = 2), lower-middle-income (n = 22), and low-income countries (n = 1). Community interventions included bystander CPR and/or AED training (n = 34), community responder programs (n = 8), drone-delivered AED networks (n = 6), dispatcher-assisted CPR programs (n = 4), regional resuscitation campaigns (n = 3), public access defibrillation programs (n = 3), and crowdsourcing technologies (n = 2). CPR and/or AED training were the only interventions evaluated in low-income, lower-middle-income, and upper-middle-income countries. CONCLUSIONS: Interventions aimed at improving the community response to OHCA in resource-limited settings differ globally. There is a lack of reported studies from low-income countries and certain continental regions, including South America, Africa, and Oceania. Evaluation of interventions other than CPR and/or AED training in low- and middle-income countries is needed to guide community emergency planning and health policies.

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.

Does cardiovascular preparticipation screening cause psychological distress in athletes? A systematic review.

OBJECTIVE: To evaluate the psychological implications of cardiovascular preparticipation screening (PPS) in athletes. DESIGN: Systematic review. DATA SOURCES: MEDLINE, EMBASE, PubMed, CINAHL, SPORTDiscus, APA PsycInfo, Cochrane Library and grey literature sources. STUDY ELIGIBILITY CRITERIA: Observational and experimental studies assessing a population of athletes who participated in a cardiovascular PPS protocol, where psychological outcomes before, during and/or after PPS were reported. METHODS: Results of included studies were synthesised by consolidating similar study-reported measures for key psychological outcomes before, during and/or after screening. Summary measures (medians, ranges) were computed across studies for each psychological outcome. RESULTS: A total of eight studies were included in this review (median sample size: 479). Study cohorts consisted of high school, collegiate, professional and recreational athletes (medians: 59% male, 20.5 years). Most athletes reported positive reactions to screening and would recommend it to others (range 88%-100%, five studies). Increased psychological distress was mainly reported among athletes detected with pathological cardiac conditions and true-positive screening results. In comparison, athletes with false-positive screening results still reported an increased feeling of safety while participating in sport and were satisfied with PPS. A universal conclusion across all studies was that most athletes did not experience psychological distress before, during or after PPS, regardless of the screening modality used or accuracy of results. CONCLUSION: Psychological distress associated with PPS in athletes is rare and limited to athletes with true-positive findings. To mitigate downstream consequences in athletes who experience psychological distress, appropriate interventions and resources should be accessible prior to the screening procedure. PROSPERO REGISTRATION NUMBER: CRD42021272887.

Comparative evaluation of an electrocardiogram workflow model for athletic cardiovascular screening: Primary care network versus sports cardiology interpretation.

BACKGROUND: Electrocardiogram (ECG) testing in pre-participation screening (PPS) remains controversial due to its cost, resource dependency, and the potential for inaccurate interpretations. At most centres, ECGs are conducted internally by providers trained in athletic ECG interpretation. Outsourcing ECG requisitions to an athlete's primary care network (PCN) may reduce institutional demands. This study compared PCN-conducted athletic ECG interpretation to expert sports cardiology interpretation. METHODS: This was a retrospective, single-centre chart-review study of all athletes who underwent cardiovascular PPS between 2017 and 2021. All athletes submitted an ECG with their screening package, which was conducted and interpreted within their PCN. All ECGs were reinterpreted by a sports cardiologist using the International Criteria (IC) for electrocardiographic interpretation in athletes. Overall, positive, and negative percent agreement were used to compare PCN-conducted ECG interpretation with IC interpretation. RESULTS: A total of 740 athletes submitted a screening package with a valid ECG (mean age: 18.5 years, 39.6% female). PCN-conducted ECGs were interpreted by 181 unique physicians. Among 41 (5.5%) PCN-conducted ECGs that were initially interpreted as abnormal, only 5 (0.7%) were classified as abnormal according to the IC. All PCN-conducted ECGs reported as normal were also classified as normal according to the IC. The overall agreement between PCN-conducted and IC ECG interpretation was 95.1% (positive percent agreement: 100%, negative percent agreement: 95.1%). CONCLUSIONS: Normal PCN-conducted athletic ECGs are interpreted with high agreement to the IC. Majority of PCN-conducted ECGs interpreted as abnormal are indeed normal as per the IC. These findings suggest that a PPS workflow model that outsources ECG requisitions to a PCN may be a reliable approach to PPS, all while reducing screening-related institutional costs and resource requirements.

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.

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.

Bystander interventions and survival after exercise-related sudden cardiac arrest: a systematic review.

OBJECTIVE: To evaluate the provision of bystander interventions and rates of survival after exercise-related sudden cardiac arrest (SCA). DESIGN: Systematic review. DATA SOURCES: MEDLINE, EMBASE, PubMed, CINAHL, SPORTDiscus, Cochrane Library and grey literature sources were searched from inception to November/December 2020. STUDY ELIGIBILITY CRITERIA: Observational studies assessing a population of exercise-related SCA (out-of-hospital cardiac arrests that occurred during exercise or within 1 hour of cessation of activity), where bystander cardiopulmonary resuscitation (CPR) and/or automated external defibrillator (AED) use were reported, and survival outcomes were ascertained. METHODS: Among all included studies, the median (IQR) proportions of bystander CPR and bystander AED use, as well as median (IQR) rate of survival to hospital discharge, were calculated. RESULTS: A total of 29 studies were included in this review, with a median study duration of 78.7 months and a median sample size of 91. Most exercise-related SCA patients were male (median: 92%, IQR: 86%-96%), middle-aged (median: 51, IQR: 39-56 years), and presented with a shockable arrest rhythm (median: 78%, IQR: 62%-86%). Bystander CPR was initiated in a median of 71% (IQR: 59%-87%) of arrests, whereas bystander AED use occurred in a median of 31% (IQR: 19%-42%) of arrests. Among the 19 studies that reported survival to hospital discharge, the median rate of survival was 32% (IQR: 24%-49%). Studies which evaluated the relationship between bystander interventions and survival outcomes reported that both bystander CPR and AED use were associated with survival after exercise-related SCA. CONCLUSION: Exercise-related SCA occurs predominantly in males and presents with a shockable ventricular arrhythmia in most cases, emphasising the importance of rapid access to defibrillation. Further efforts are needed to promote early recognition and a rapid bystander response to exercise-related SCA.

Maternal Cardiovascular Function Following a Pregnancy Complicated by Preeclampsia.

Preeclampsia is a hypertensive pregnancy complication with an unknown etiology and high maternal burden worldwide. Burgeoning research has linked preeclampsia to adverse maternal health outcomes remote from pregnancy; however, the intermediary mechanisms responsible for this association have not been sufficiently established. In the present narrative review, we summarize leading evidence of structural and functional cardiovascular changes associated with prior preeclampsia, and how these changes may be linked to future maternal disease. KEY POINTS: · Prior preeclampsia is associated with subclinical structural and functional vascular changes remote from pregnancy.. · Maternal cardiac adaptations to preeclampsia may have long-term implications on cardiovascular health.. · Clinicians have an opportunity to minimize maternal disease risk following preeclampsia..

A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework.

Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi-automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from "ground truth" images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound.

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence.

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging.

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients.

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.

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.

Shock to the Heart: Psychosocial Implications and Applications of Sudden Cardiac Death in the Young.

PURPOSE OF REVIEW: Although rare, sudden cardiac death (SCD) in the young is a tragic event, having a dramatic impact upon all involved. The psychosocial burden associated with SCD can leave friends, families, and entire communities bereft. With only limited evidence to describe the volatile emotional reactions associated with a young SCD, there is an urgent need for care providers to better understand the psychological complexities and impacts faced by both at-risk individuals and those directly affected by these tragic events. RECENT FINDINGS: Current knowledge of the psychosocial implications associated with SCD in the young has recently generated interest in the cardiovascular community, with the goal of addressing prevention strategies (screening), family bereavement, and the psychological impact of at-risk or surviving individuals. With the emergence of novel strategies aimed at reducing the public health impact of SCD in the young, further discussion regarding the psychosocial impact of SCD, encompassing prevention, survivorship, and the downstream communal effects of a young death is required. Support systems and intervention could assist in the management of the associated psychosocial burden, yet there is a lack of clinical guidelines to direct this form of care. There is an important need for multidisciplinary collaboration across subspecialties to provide support to grieving individuals and manage patient well-being throughout the screening process for SCD. This collaborative approach requires the integration of cardiovascular and psychological expertise where relevant.

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.

Point-of-Care Ultrasound as a Component of Preparticipation Screening of Athletes: A Systematic Review.

This review examined whether the addition of point-of-care ultrasound (POCUS) to electrocardiography (ECG)-inclusive preparticipation screening strategies has the potential to reduce false-positive results and detect diseases associated with sudden cardiac death that may not be identified through current modalities. Five studies, representing 2646 athletes, demonstrated that ECG-inclusive preparticipation screening strategies resulted in positive results in 19.9% of the cohort. With the addition of POCUS, positive results were reduced to 4.9%, and 1 additional condition potentially associated with sudden cardiac death was identified. The magnitude of positive results with POCUS may be reduced if current ECG criteria were applied.