Mitral Regurgitation Evaluation in Modern Echocardiography: Bridging Standard Techniques and Advanced Tools for Enhanced Assessment

Mitral regurgitation (MR) is one of the most common valvular heart diseases worldwide. Echocardiography remains the first line and most effective imaging modality for the diagnosis of mitral valve (MV) pathology and quantitative assessment of MR. The advent of three-dimensional echocardiography has significantly enhanced the evaluation of MV anatomy and function. Furthermore, recent advancements in cardiovascular imaging software have emerged as step-forward tools, providing a powerful support for acquisition, analysis, and interpretation of cardiac ultrasound images in the context of MR. This review aims to provide an overview of the contemporary workflow for echocardiographic assessment of MR, encompassing standard echocardiographic techniques and the integration of semiautomated and automated ultrasound solutions. These novel approaches include advancements in segmentation, phenotyping, morphological quantification, functional grading, and chamber quantification.

The Unique Challenge of Coronary Artery Disease in Adult Patients with Congenital Heart Disease

Advances in medical and surgical interventions have resulted in a steady increase in the number of patients with congenital heart disease (CHD) reaching adult age. Unfortunately, this ever-growing population faces an added challenge: an increased risk of acquiring coronary artery disease. This review provides insight into the complex interactions between coronary artery disease and CHD in adults. We describe the peculiar features of cardiac anatomy in these patients, the possible role cardiac sequelae may play in an increased risk of myocardial ischemia, and the diagnostic challenges in this patient group. Furthermore, this review outlines the risk factors and potential mechanisms of accelerated atherosclerosis in adults with CHD by pointing out areas where current knowledge is incomplete and highlighting areas for further research. The review concludes by examining potential management strategies for this particular population, emphasizing the necessity for a multidisciplinary approach. Understanding the unique coronary risks that adults with CHD experience can enhance patient care and improve long-term results.

Lipoprotein(a) and long-term in-stent restenosis after percutaneous coronary intervention

AIMS

Lipoprotein(a) [Lp(a)] has demonstrated its association with atherosclerosis and myocardial infarction. However, its role in the development of in-stent restenosis (ISR) after percutaneous coronary intervention (PCI) is not clearly established. The aim of this study is to investigate the association between Lp(a) and ISR.

METHODS AND RESULTS

A retrospective study of adult patients who underwent successful PCI between January 2006 and December 2017 at the three Mayo Clinic sites and had a preprocedural Lp(a) measurement was conducted. Patients were divided into two groups according to the serum Lp(a) concentration [high Lp(a) ≥ 50 mg/dL and low Lp(a) < 50 mg/dL]. Univariable and multivariable analyses were performed to compare risk of ISR between patients with high Lp(a) vs. those with low Lp(a). A total of 1209 patients were included, with mean age 65.9 ± 11.7 years and 71.8% were male. Median follow-up after baseline PCI was 8.8 [interquartile range (IQR) 7.4] years. Restenosis was observed in 162 (13.4%) patients. Median serum levels of Lp(a) were significantly higher in patients affected by ISR vs. non-affected cases: 27 (IQR 73.8) vs. 20 (IQR 57.5) mg/dL, P = 0.008. The rate of ISR was significantly higher among patients with high Lp(a) vs. patients with low Lp(a) values (17.0% vs. 11.6%, P = 0.010). High Lp(a) values were independently associated with ISR events (hazard ratio 1.67, 95% confidence interval 1.18-2.37, P = 0.004), and this association was more prominent after the first year following the PCI.

CONCLUSION

Lipoprotein(a) is an independent predictor for long-term ISR and should be considered in the evaluation of patients undergoing PCI.

Late Outcomes of Ascending-to-Descending Bypass for Aortic Coarctation

BACKGROUND

Ascending-to-descending aortic bypass is a repair option for patients with complex aortic coarctation. This technique is reported to have minimal early morbidity and mortality; however, the long-term results of the procedure are unknown.

METHODS

This study analyzed the late outcomes of 81 consecutive patients with aortic coarctation who underwent ascending-to-descending aortic bypass through a median sternotomy from January 1985 to December 2012. The study was limited to this interval to allow for a minimum of 10-year follow-up. Fifty-two patients had recurrent coarctation after previous repair, and 44 patients had concomitant cardiac procedures at the time of ascending-to-descending bypass.

RESULTS

There was no perioperative mortality, and overall survival at 5, 10, and 20 years was 94%, 90%, and 85%, respectively, similar to an age- and sex-matched population. There were no interventions for the ascending-to-descending bypass during follow-up, and no deaths were known to be related to the bypass graft. Subsequent cardiac operations were performed through a median sternotomy in 7 patients (9%), and late imaging in 48 patients (59%) demonstrated no hemodynamically significant stenoses or pseudoaneurysms. At late follow-up, median systolic blood pressure was 124 mm Hg, and one-half of the patients were receiving either no or only 1 antihypertensive agent.

CONCLUSIONS

Ascending-to-descending aortic bypass is a safe operation with excellent long-term outcomes for adult patients with complex aortic coarctation. The procedure is durable and appears to improve systemic hypertension. Importantly, ascending-to-descending bypass does not hinder the safe performance of subsequent operations performed through a median sternotomy.

Research priorities for the study of atrial fibrillation during acute and critical illness: recommendations from the Symposium on Atrial Fibrillation in Acute and Critical Care

Atrial fibrillation (AF) is a common arrhythmia encountered in acute and critical illness and is associated with poor short and long-term outcomes. Given the consequences of developing AF, research into prevention, prediction and treatment of this arrhythmia in the critically ill are of great potential benefit, however, study of AF in critically ill patients faces unique challenges, leading to a sparse evidence base to guide management in this population. Major obstacles to the study of AF in acute and critical illness include absence of a common definition, challenges in designing studies that capture complex etiology and assess causality, lack of a clear outcome set, difficulites in recruitment in acute environments with respect to timing, consent, and workflow, and failure to embed studies into clinical care platforms and capitalize on emerging technologies. Collaborative effort by researchers, clinicians, and stakeholders should be undertaken to address these challenges, both through interdisciplinary cooperation for the optimization of research efficiency and advocacy to advance the understanding of this common and complex arrhythmia, resulting in improved patient care and outcomes. The Symposium on Atrial Fibrillation in Acute and Critical Care was convened to address some of these challenges and propose potential solutions.

Iatrogenic atrial septal defects in structural heart interventions: Opening the Pandora's box

In the modern era of structural heart interventions, the total number of transseptal procedures is growing exponentially, thus increasing the rate and need for management of iatrogenic atrial septal defects (iASDs). To date, there are no official guidelines on the assessment and management of iASDs, due to inconclusive evidence on whether patients benefit more from the percutaneous closure of iASD than from conservative management and vigorous follow-up. Despite the abundance of observational studies on iASDs, there is still a lack of randomized studies. Evidence so far show that percutaneous closure is no superior over conservative treatment in patients with iASDs, however, it has been demonstrated that patients with spontaneous closure of iASDs experience less heart failure (HF) hospitalizations. On the other hand, researchers have investigated the beneficial nature of interatrial shunt therapy in patients with HFpEF and, more recently, with HFrEF, due to the presumed hemodynamic benefits. Herein, we provide an updated review of relevant literature, focusing on iASD persistence rates, predicting factors for their persistence, and clinical outcomes of iASD persistence, to summarize available evidence and discuss future directions in the field.

Use of artificial intelligence-guided echocardiography to detect cardiac dysfunction and heart valve disease in rural and remote areas: Rationale and design of the AGILE-echo trial

BACKGROUND

Transthoracic echocardiography (TTE) is essential in the diagnosis of cardiovascular diseases (CVD), including but not limited to heart failure (HF) and heart valve disease (HVD). However, its dependence on expert acquisition means that its accessibility in rural areas may be limited, leading to delayed management decisions and potential missed diagnoses. Artificial intelligence-guided (AI)-TTE offers a solution by permitting non-expert image acquisition. The impact of AI-TTE on the timing of diagnosis and early initiation of cardioprotection is undefined.

METHODS

AGILE-Echo (use of Artificial intelligence-Guided echocardiography to assIst cardiovascuLar patient managEment) is a randomized-controlled trial conducted in 5 rural and remote areas around Australia. Adults with CV risk factors and exercise intolerance, or concerns regarding HVD are randomized into AI-TTE or usual care (UC). AI-TTE participants may have a cardiovascular problem excluded, identified (leading to AI-guided interventions) or unresolved (leading to conventional TTE). UC participants undergo usual management, including referral for standard TTE. The primary endpoint is a composite of HVD or HF diagnosis at 12-months. Subgroup analysis, stratified based on age range and sex, will be conducted. All statistical analyses will be conducted using R.

RESULTS

Of the first 157 participants, 78 have been randomized into AI-TTE (median age 68 [IQR 17]) and 79 to UC (median age 65 [IQR 17], P = .034). HVD was the primary concern in 37 participants (23.6%) while 84.7% (n = 133) experienced exercise intolerance. The overall 10-year HF incidence risk was 13.4% and 20.0% (P = .089) for UC and AI-TTE arm respectively. Atrial remodeling, left ventricular remodeling and valvular regurgitation were the most common findings. Thirty-three patients (42.3%) showed no abnormalities.

CONCLUSIONS

This randomized-controlled trial of AI-TTE will provide proof-of-concept for the role of AI-TTE in identifying pre-symptomatic HF or HVD when access to TTE is limited. Additionally, this could promote the usage of AI-TTE in rural or remote areas, ultimately improving health and quality of life of community dwelling adults with risks, signs or symptoms of cardiac dysfunction.

Case report of the successful treatment of a rare complication of pulmonary vein stenting: atrial rupture and stent detachment

Pulmonary vein stenosis (PVS) is a relatively rare pulmonary vascular disease in clinical practice, and radiofrequency ablation (RFA) is a common cause of pulmonary vein stenosis. Owing to its nonspecific symptoms and irregular follow-up, PVS is often misdiagnosed. In severe cases, intervention is needed, with the main treatment options being pulmonary vein stenting and/or balloon dilation, both of which carry risks of serious complications. We report a patient with severe pulmonary vein stenosis who experienced right atrial wall rupture and stent detachment during an interventional procedure. Through active intraoperative intervention, we successfully avoided serious complications.

Omics research in atherosclerosis

Atherosclerosis (AS) is a chronic inflammatory disease characterized by lipid deposition within the arterial intima, as well as fibrous tissue proliferation and calcification. AS has long been recognized as one of the primary pathological foundations of cardiovascular diseases in humans. Its pathogenesis is intricate and not yet fully elucidated. Studies have shown that AS is associated with oxidative stress, inflammatory response, lipid deposition, and changes in cell phenotype. Unfortunately, there is currently no effective prevention or targeted treatment for AS. The rapid advancement of omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, has opened up novel avenues to elucidate the fundamental pathophysiology and associated mechanisms of AS. Here, we review articles published over the past decade and focus on the current status, challenges, limitations, and prospects of omics in AS research and clinical practice. Emphasizing potential targets based on omics technologies will improve our understanding of this pathological condition and assist in the development of potential therapeutic approaches for AS-related diseases.

Machine learning for pacemaker implantation prediction after TAVI using multimodal imaging data

Pacemaker implantation (PMI) after transcatheter aortic valve implantation (TAVI) is a common complication. While computed tomography (CT) scan data are known predictors of PMI, no machine learning (ML) model integrating CT with clinical, ECG, and transthoracic echocardiography (TTE) data has been proposed. This study investigates the contribution of ML methods to predict PMI after TAVI, with a focus on the role of CT imaging data. A retrospective analysis was conducted on a cohort of 520 patients who underwent TAVI. Recursive feature elimination with SHAP values was used to select key variables from clinical, ECG, TTE, and CT data. Six ML models, including Support Vector Machines (SVM), were trained using these selected variables. The model's performance was evaluated using AUC-ROC, F1 score, and accuracy metrics. The PMI rate was 18.8%. The best-performing model achieved an AUC-ROC of 92.1% ± 4.7, an F1 score of 71.8% ± 9.9, and an accuracy of 87.9% ± 4.7 using 22 variables, 9 of which were CT-based. Membranous septum measurements and their dynamic variations were critical predictors. Our ML model provides robust PMI predictions, enabling personalized risk assessments. The model is implemented online for broad clinical use.

New ultrasound techniques. Present and future

The present study highlights the advances in ultrasound, especially regarding its clinical applications to critically ill patients. Artificial intelligence (AI) is crucial in automating image interpretation, improving accuracy and efficiency. Software has been developed to make it easier to perform accurate bedside ultrasound examinations, even by professionals lacking prior experience, with automatic image optimization. In addition, some applications identify cardiac structures, perform planimetry of the Doppler wave, and measure the size of vessels, which is especially useful in hemodynamic monitoring and continuous recording. The "strain" and "strain rate" parameters evaluate ventricular function, while "auto strain" automates its calculation from bedside images. These advances, and the automatic determination of ventricular volume, make ultrasound monitoring more precise and faster. The next step is continuous monitoring using gel devices attached to the skin.

The Emerging and Important Role of Artificial Intelligence in Cardiac Surgery

Artificial Intelligence (AI) has greatly affected our everyday lives and holds great promise to change the landscape of medicine. AI is particularly positioned to improve care for the increasingly complex patients undergoing cardiac surgery using the immense amount of data generated in the course of their care. When deployed, AI can be used to analyze this information at the patient's bedside more expediently and accurately, all while providing new insights. This review summarizes the current applications of AI in cardiac surgery from the vantage point of a patient's journey. Applications of AI include preoperative risk assessment, intraoperative planning, postoperative patient care, and outpatient telemonitoring, encompassing the spectrum of cardiac surgical care. Offloading of administrative processes and enhanced experience with information gathering also represent a unique and under-represented avenue for future use of AI. As clinicians, understanding the nomenclature and applications of AI is important to contextualize issues, to ensure problem-driven solutions, and for clinical benefit. Precision medicine, and thus clinically relevant AI, remains dependent on data curation and warehousing to gather insights from large multicentre repositories while treating privacy with the utmost importance. AI tasks should not be siloed but rather holistically integrated into clinical workflow to retain context and relevance. As cardiac surgeons, AI allows us to look forward to a bright future of more efficient use of our clinical expertise toward high-level decision making and technical prowess.

The use of echocardiography in the management of shock in critical care: a prospective, multi-centre, observational study

PURPOSE

Echocardiography is recommended as a first-line tool in the assessment of patients with shock. The current provision of echocardiography in critical care is poorly defined. The aims of this work were to evaluate the utilisation of echocardiography in patients presenting to critical care with shock, its impact on decision making, and adherence to governance guidelines.

METHODS

We conducted a prospective, multi-centre, observational study in 178 critical care units across the United Kingdom (UK) and Crown Dependencies, led by the UK's Trainee Research in Intensive Care Network. Consecutive adult patients (≥ 18 years) admitted with shock were followed up for 72 h to ascertain whether they received an echocardiogram, the nature of any scan performed, and its effect on critical treatment decision making.

RESULTS

1015 patients with shock were included. An echocardiogram was performed on 545 (54%) patients within 72 h and 436 (43%) within 24 h of admission. Most scans were performed by the critical care team (n = 314, 58%). Echocardiography was reported to either reduce diagnostic uncertainty or change management in 291 (54%) cases. Patients with obstructive or cardiogenic shock had their management altered numerically more often by echocardiography (n = 15 [75%] and n = 100 [58%] respectively). Twenty-five percent of echocardiograms performed adhered to current national governance and image storage guidance.

CONCLUSION

Use of echocardiography in the assessment of patients with shock remains heterogenous. When echocardiography is used, it improves diagnostic certainty or changes management in most patients. Future research should explore barriers to increasing use of echocardiography in assessing patients presenting with shock.

The Role of Cardiovascular Imaging in the Diagnosis of Athlete's Heart: Navigating the Shades of Grey

Athlete's heart (AH) represents the heart's remarkable ability to adapt structurally and functionally to prolonged and intensive athletic training. Characterized by increased left ventricular (LV) wall thickness, enlarged cardiac chambers, and augmented cardiac mass, AH typically maintains or enhances systolic and diastolic functions. Despite the positive health implications, these adaptations can obscure the difference between benign physiological changes and early manifestations of cardiac pathologies such as dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and arrhythmogenic cardiomyopathy (ACM). This article reviews the imaging characteristics of AH across various modalities, emphasizing echocardiography, cardiac magnetic resonance (CMR), and cardiac computed tomography as primary tools for evaluating cardiac function and distinguishing physiological adaptations from pathological conditions. The findings highlight the need for precise diagnostic criteria and advanced imaging techniques to ensure accurate differentiation, preventing misdiagnosis and its associated risks, such as sudden cardiac death (SCD). Understanding these adaptations and employing the appropriate imaging methods are crucial for athletes' effective management and health optimization.

A multi-task deep learning approach for real-time view classification and quality assessment of echocardiographic images

High-quality standard views in two-dimensional echocardiography are essential for accurate cardiovascular disease diagnosis and treatment decisions. However, the quality of echocardiographic images is highly dependent on the practitioner's experience. Ensuring timely quality control of echocardiographic images in the clinical setting remains a significant challenge. In this study, we aimed to propose new quality assessment criteria and develop a multi-task deep learning model for real-time multi-view classification and image quality assessment (six standard views and "others"). A total of 170,311 echocardiographic images collected between 2015 and 2022 were utilized to develop and evaluate the model. On the test set, the model achieved an overall classification accuracy of 97.8% (95%CI 97.7-98.0) and a mean absolute error of 6.54 (95%CI 6.43-6.66). A single-frame inference time of 2.8 ms was achieved, meeting real-time requirements. We also analyzed pre-stored images from three distinct groups of echocardiographers (junior, senior, and expert) to evaluate the clinical feasibility of the model. Our multi-task model can provide objective, reproducible, and clinically significant view quality assessment results for echocardiographic images, potentially optimizing the clinical image acquisition process and improving AI-assisted diagnosis accuracy.

Application of Echocardiography in Anaesthesia: From Preoperative Risk Assessment to Postoperative Care

Echocardiography has carved out a fundamental niche in anaesthesiology, revolutionizing the monitoring and management of cardiac function during surgery. Clinical practice has changed from simple 2D and 3D echocardiography to more sophisticated applications such as incorporating artificial intelligence. Echocardiography provides detailed real-time information about cardiac anatomy and function, helping anaesthesiologists make better decisions regarding tailoring anesthetic interventions and optimizing patient outcomes. From optimizing hemodynamic management in patients with severe aortic stenosis to fine-tuning fluid and vasopressor therapy in patients with right heart dysfunction, echocardiography has improved the care provided in the perioperative period. These applications permit the demonstration of not only technical advantages that could accrue from echocardiography but are also a part of individualized care to improve the outcomes of patients. The challenges in integrating echocardiography with anaesthesia include operator dependency, a steep learning curve in acquiring echocardiographic skills, and limitations due to patient factors and technological limitations, which lead to poor echocardiographic performance. Additionally, transoesophageal echocardiography (TEE) is an invasive procedure with several potential risks that must be considered cautiously. Continuing education, certification recommendations, and skill development are prerequisites for this echocardiography tool to remain robust and reliable in anaesthesiology. Technological innovation, especially in improving 3D imaging and integration with artificial intelligence, is where a very bright future lies ahead for echocardiography. It would further accelerate the process of echocardiographic evaluation and improve diagnostic accuracy. All these would turn out to be more person-centered for each patient. Anaesthesiologists must, therefore, pace themselves with such developments so these can be appropriately applied in the clinics. In summary, echocardiography became so integrally ingrained into anaesthesia that it propelled the specialty with essential tools anaesthesiologists use to manage patients for optimum outcomes. Its application has difficulties and limitations, but continued professional development and development of echocardiographic technology will make sure that its benefits are maximized. Quickly, echocardiography is becoming central to anaesthesiology's role in optimizing patient care and surgical success as we move into the application of evermore sophisticated echocardiographic techniques.

CT Angiography for Aortic Arch Anomalies: Prevalence, Diagnostic Efficacy, and Illustrative Findings

Aortic arch anomalies encompass a diverse spectrum of conditions. Elucidating the prevalence of these anomalies, their impact on patient wellbeing, and the most effective diagnostic tools are crucial steps in ensuring optimal patient care. This paper aims to explore the various presentations of aortic arch anomalies, emphasizing the remarkable utility of computed tomography (CT) angiography in their definitive diagnosis and characterization. We conducted a retrospective study on patients who were submitted to the CT angiography of the thoracic aorta or supra-aortic trunks, or the contrast-enhanced CT scans of the thorax and/or cervical region between January 2021 and February 2024 in our Hospital. Out of the total of 2350 patients, 18 were diagnosed with aortic arch anomalies, with an average age of approximately 55 years. The aortic arch anomalies identified in the study were as follows: left aortic arch with the aberrant origin of the right subclavian artery, right aortic arch (types I and II), double aortic arch, aortic coarctation, aortic pseudocoarctation, and ductus diverticulum. Although often asymptomatic, aortic arch anomalies require recognition and CT using advanced post-processing techniques is the optimal diagnostic method with the ability to also identify other associated cardiac or vascular malformations.

Assessment of an Artificial Intelligence Tool for Estimating Left Ventricular Ejection Fraction in Echocardiograms from Apical and Parasternal Long-Axis Views

This work aims to evaluate the performance of a new artificial intelligence tool (ExoAI) to compute the left ventricular ejection fraction (LVEF) in echocardiograms of the apical and parasternal long axis (PLAX) views. We retrospectively gathered echocardiograms from 441 individual patients (70% male, age: 67.3 ± 15.3, weight: 87.7 ± 25.4, BMI: 29.5 ± 7.4) and computed the ejection fraction in each echocardiogram using the ExoAI algorithm. We compared its performance against the ejection fraction from the clinical report. ExoAI achieved a root mean squared error of 7.58% in A2C, 7.45% in A4C, and 7.29% in PLAX, and correlations of 0.79, 0.75, and 0.89, respectively. As for the detection of low EF values (EF < 50%), ExoAI achieved an accuracy of 83% in A2C, 80% in A4C, and 91% in PLAX. Our results suggest that ExoAI effectively estimates the LVEF and it is an effective tool for estimating abnormal ejection fraction values (EF < 50%). Importantly, the PLAX view allows for the estimation of the ejection fraction when it is not feasible to acquire apical views (e.g., in ICU settings where it is not possible to move the patient to obtain an apical scan).

Revolutionizing Cardiac Imaging: A Scoping Review of Artificial Intelligence in Echocardiography, CTA, and Cardiac MRI

BACKGROUND AND INTRODUCTION

Cardiac imaging is crucial for diagnosing heart disorders. Methods like X-rays, ultrasounds, CT scans, and MRIs provide detailed anatomical and functional heart images. AI can enhance these imaging techniques with its advanced learning capabilities.

METHOD

In this scoping review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) Guidelines, we searched PubMed, Scopus, Web of Science, and Google Scholar using related keywords on 16 April 2024. From 3679 articles, we first screened titles and abstracts based on the initial inclusion criteria and then screened the full texts. The authors made the final selections collaboratively.

RESULT

The PRISMA chart shows that 3516 articles were initially selected for evaluation after removing duplicates. Upon reviewing titles, abstracts, and quality, 24 articles were deemed eligible for the review. The findings indicate that AI enhances image quality, speeds up imaging processes, and reduces radiation exposure with sensitivity and specificity comparable to or exceeding those of qualified radiologists or cardiologists. Further research is needed to assess AI's applicability in various types of cardiac imaging, especially in rural hospitals where access to medical doctors is limited.

CONCLUSIONS

AI improves image quality, reduces human errors and radiation exposure, and can predict cardiac events with acceptable sensitivity and specificity.

Obicetrapib on top of maximally tolerated lipid-modifying therapies in participants with or at high risk for atherosclerotic cardiovascular disease: rationale and designs of BROADWAY and BROOKLYN

BACKGROUND

Obicetrapib, a novel, selective cholesteryl ester transfer protein (CETP) inhibitor, reduces low-density lipoprotein cholesterol (LDL-C), LDL particles, apolipoprotein (Apo) B, and lipoprotein(a) [Lp(a)] and increases high-density lipoprotein cholesterol (HDL-C) when added to statins with or without ezetimibe. By substantially reducing LDL-C, obicetrapib has the potential to lower atherogenic lipoproteins in patients with atherosclerotic cardiovascular disease (ASCVD) or heterozygous familial hypercholesterolemia (HeFH) whose LDL-C levels remain high despite treatment with available maximally tolerated lipid-modifying therapies, addressing an unmet medical need in a patient population at high risk for cardiovascular events.

METHODS AND RESULTS

BROADWAY (NCT05142722) and BROOKLYN (NCT05425745) are ongoing placebo-controlled, double-blind, randomized Phase III trials designed to examine the efficacy, safety, and tolerability of obicetrapib as an adjunct to dietary intervention and maximally tolerated lipid-modifying therapies in participants with a history of ASCVD and/or underlying HeFH whose LDL-C is not adequately controlled. The primary efficacy endpoint was the percent change in LDL-C from baseline to day 84. Other endpoints included changes in Apo B, non-HDL-C, HDL-C, Apo A1, Lp(a), and triglycerides in addition to parameters evaluating safety, tolerability, and pharmacokinetics. BROADWAY also included an adjudicated assessment of major adverse cardiovascular events, measurements of glucose homeostasis, and an ambulatory blood pressure monitoring substudy. A total of 2,532 participants were randomized in BROADWAY and 354 in BROOKLYN to receive obicetrapib 10 mg or placebo (2:1) for 365 days with follow-up through 35 days after the last dose. Results from both trials are anticipated in 2024.

CONCLUSION

These trials will provide safety and efficacy data to support the potential use of obicetrapib among patients with ASCVD or HeFH with elevated LDL-C for whom existing therapies are not sufficiently effective or well-tolerated.

Diagnostic Modalities in Heart Failure: A Narrative Review

Heart failure (HF) can present acutely or progress over time. It can lead to morbidity and mortality affecting 6.5 million Americans over the age of 20. The HF type is described according to the ejection fraction classification, defined as the percentage of blood volume that exits the left ventricle after myocardial contraction, undergoing ejection into the circulation, also called stroke volume, and is proportional to the ejection fraction. Cardiac catheterization is an invasive procedure to evaluate coronary artery disease leading to HF. Several biomarkers are being studied that could lead to early detection of HF and better symptom management. Testing for various biomarkers in the patient's blood is instrumental in confirming the diagnosis and elucidating the etiology of HF. There are various biomarkers elevated in response to increased myocardial stress and volume overload, including B-type natriuretic peptide (BNP) and its N-terminal prohormone BNP. We explored online libraries such as PubMed, Google Scholar, and Cochrane to find relevant articles. Our narrative review aims to extensively shed light on diagnostic modalities and novel techniques for diagnosing HF.

Iatrogenic Atrial Septal Defect After MitraClip Transcatheter Edge-to-Edge Repair: To Close or Not to Close?

The evolution of percutaneous procedures that use transseptal puncture to treat left-sided structural heart disease has led to the emergence of iatrogenic atrial septal defects as a potential complication. These defects can result in hemodynamic decompensation and worsening clinical outcomes. Some iatrogenic atrial septal defects require immediate closure, others do not. This case report presents 2 patients who underwent transcatheter edge-to-edge mitral valve repair with transseptal puncture and required iatrogenic atrial septal defect closure (1 immediate and 1 delayed). The goal of this report is to highlight iatrogenic atrial septal defect assessment and the possible need for closure after transseptal puncture.

A Novel Diastolic Doppler Index Less Affected by Aortic Arch Anomalies Co-existing with Coarctation

Severity assessment for coarctation of the aorta (CoA) is challenging due to concomitant morphological anomalies (complex CoA) and inaccurate Doppler-based indices. Promising diagnostic performance has been reported for the continuous flow pressure gradient (CFPG), but it has not been studied in complex CoA. Our objective was to characterize the effect of complex CoA and associated hemodynamics on CFPG in a clinical cohort. Retrospective analysis identified discrete juxtaductal (n = 25) and complex CoA (n = 43; transverse arch and/or isthmus hypoplasia) patients with arm-leg systolic blood pressure gradients (BPG) within 24 h of echocardiography for comparison to BPG by conventional Doppler indices (simplified Bernoulli equation and modified forms correcting for proximal kinetic energy and/or recovered pressure). Results were interpreted using the current CoA guideline (BPG ≥ 20 mmHg) to compare diagnostic performance indicators including receiver operating characteristic curves, sensitivity, specificity, and diagnostic accuracy, among others. Echocardiography Z-scored aortic diameters were applied with computational simulations from a preclinical CoA model to understand aspects of the CFPG driving performance differences. Diagnostic performance was substantially reduced from discrete to complex CoA for conventional Doppler indices calculated from patient data, and by hypoplasia and/or long segment stenosis in simulations. In contrast, diagnostic indicators for the CFPG only modestly dropped for complex vs discrete CoA. Simulations revealed differences in performance due to inclusion of the Doppler velocity index and diastolic pressure half-time in the CFPG calculation. CFPG is less affected by aortic arch anomalies co-existing with CoA when compared to conventional Doppler indices.

Lipoprotein (a) and cerebrovascular disease

The role of lipoprotein (a) [Lp(a)] in cerebrovascular disease is a topic of importance. In this narrative review, pertinent studies have been leveraged to comprehensively examine this relationship from diverse perspectives.Lp(a) shares structural traits with low-density lipoprotein cholesterol. Lp(a) is synthesized by hepatocytes, and its plasma levels are genetically determined by the LPA gene, which produces apolipoprotein (a).Numerous epidemiological studies have confirmed the positive correlation between elevated serum Lp(a) levels and the occurrence or recurrence of cerebrovascular events, especially ischemic strokes, in adults. It should be noted that the correlation strength varies among studies and is marginal in Mendelian randomization studies.Regarding pediatric patients, screening is currently limited to those with a relevant medical history. Lp(a) seems to play a significant role in the pathogenesis of arterial ischemic stroke in children because environmental thrombotic and atherogenic factors are generally not present.Phase 3 trials of novel Lp(a) targeting agents, such as pelacarsen and olpasiran, are anticipated to demonstrate their efficacy in reducing the incidence of stroke. Given the richness of the literature, new guidelines regarding Lp(a) screening and management in targeted populations are warranted to provide more effective primary and secondary prevention.

The Role of Multimodality Imaging in Cardiomyopathy

PURPOSE OF REVIEW

There has been increasing use of multimodality imaging in the evaluation of cardiomyopathies.

RECENT FINDINGS

Echocardiography, cardiac magnetic resonance (CMR), cardiac nuclear imaging, and cardiac computed tomography (CCT) play an important role in the diagnosis, risk stratification, and management of patients with cardiomyopathies. Echocardiography is essential in the initial assessment of suspected cardiomyopathy, but a multimodality approach can improve diagnostics and management. CMR allows for accurate measurement of volumes and function, and can easily detect unique pathologic structures. In addition, contrast imaging and parametric mapping enable the characterization of tissue features such as scar, edema, infiltration, and deposition. In non-ischemic cardiomyopathies, metabolic and molecular nuclear imaging is used to diagnose rare but life-threatening conditions such amyloidosis and sarcoidosis. There is an expanding use of CCT for planning electrophysiology procedures such as cardioversion, ablations, and device placement. Furthermore, CCT can evaluate for complications associated with advanced heart failure therapies such as cardiac transplant and mechanical support devices. Innovations in multimodality cardiac imaging should lead to increased volumes and better outcomes.

Correlation Between Lipoprotein(a) and Prognosis for Coronary Artery Disease in Patients Undergoing Percutaneous Coronary Intervention

Background

Elevated lipoprotein(a) (Lp[a]) is a risk factor for first atherosclerotic thrombosis events, but the role of elevated Lp(a) in secondary prevention is controversial. This study aimed to retrospectively investigate the influence of elevated Lp(a) levels on the prognosis of patients with coronary artery disease.

Methods

The team collected and compared clinical information of patients hospitalized during percutaneous coronary intervention (PCI). This study used a multivariate logistic regression model to evaluate the relationships between Lp(a) levels, cardiovascular risk factors, and the prognosis of coronary artery disease in patients undergoing PCI.

Results

There were no statistically significant differences between patients grouped according to Lp(a) level in terms of sex; age; body mass index and obesity; hyperuricemia; smoking; cardiac insufficiency; acute myocardial infarction; multivessel lesion; in-stent restenosis; secondary PCI; apolipoprotein AI level; incidence of high total cholesterol or high low-density lipoprotein cholesterol; or family history of hypertension, diabetes, or coronary artery disease. The average Lp(a) concentration did not statistically significantly decrease after 1 year of statin treatment after PCI. One year after patients began statins, there were no significant differences between Lp(a) groups in the incidence of high triglycerides (P = .13), high total cholesterol (P = .52), or high low-density lipoprotein cholesterol (P = .051). Multivariate logistic regression analysis indicated that diabetes (P = .02) was associated with in-stent restenosis, whereas diabetes (P = .02) and multivessel lesions (P < .001) were associated with secondary PCI in patients who underwent coronary angiography 1 year after PCI. Compared with normal Lp(a) levels, high Lp(a) levels did not significantly increase the incidence of in-stent restenosis or secondary PCI in patients who underwent coronary angiography 1 year after PCI.

Conclusion

Sustained high concentrations of Lp(a) did not significantly increase the incidence of in-stent restenosis or secondary PCI in patients who underwent coronary angiography 1 year after PCI.

Real-time Elastography and Contrast-Enhanced Ultrasound for Evaluating Adventitia in the Early Diagnosis of Vulnerable Plaques: an Exploratory Study Based on Histopathology

Vulnerable plaque is closely related to the occurrence of ischemic stroke. Therefore, early accurate identification of plaque vulnerability is crucial in risk stratification. In the development of vulnerable plaques, the change of the adventitia is earlier than that of the intima. Currently, researchers focused on the ultrasound detection of intraplaque and intima, but adventitia was often ignored in the examination. Real-time elastography technology (RTE) provides an estimation of adventitia stiffness, and contrast-enhanced ultrasound (CEUS) provides the quantification of adventitial VV. Therefore, we aimed to evaluate the value of adventitia in the early diagnosis of plaque vulnerability by combining CEUS and RTE based on histopathology. Rabbit carotid atherosclerosis models were established, and CEUS and RTE were performed. Normalized maximal video-intensity enhancement (MVE) was calculated to quantify adventitial VV density, and strain values were acquired to evaluate the adventitial elasticity. After removal of the lesion lumen, histological analysis of each excised plaque and adventitia was performed, and vulnerable plaques (n = 32) and stable group (n = 13) were distinguished. Normalized MVE of the adventitial VV and adventitial strain values in the vulnerable group was significantly higher than those in the stable group. Normalized MVE and strain values had a positive linear correlation with histological findings. Normalized MVE of the adventitial VV combined with adventitial strain values could identify plaque vulnerability with the area under the curve of 0.913 (sensitivity 90% and specificity 97%). Accordingly, the multimodal ultrasound detection strategy of adventitia has a high diagnostic value for early plaque vulnerability.

Machine-learning approaches for risk prediction in transcatheter aortic valve implantation: Systematic review and meta-analysis

OBJECTIVES

With the expanding integration of artificial intelligence (AI) and machine learning (ML) into the structural heart domain, numerous ML models have emerged for the prediction of adverse outcomes after transcatheter aortic valve implantation (TAVI). We aim to identify, describe, and critically appraise ML prediction models for adverse outcomes after TAVI. Key objectives consisted in summarizing model performance, evaluating adherence to reporting guidelines, and transparency.

METHODS

We searched PubMed, SCOPUS, and Embase through August 2023. We selected published machine learning models predicting TAVI outcomes. Two reviewers independently screened articles, extracted data, and assessed the study quality according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Outcomes included summary C-statistics and model risk of bias assessed with the Prediction Model Risk of Bias Assessment Tool. C-statistics were pooled using a random-effects model.

RESULTS

Twenty-one studies (118,153 patients) employing various ML algorithms (76 models) were included in the systematic review. Predictive ability of models varied: 11.8% inadequate (C-statistic <0.60), 26.3% adequate (C-statistic 0.60-0.70), 31.6% acceptable (C-statistic 0.70-0.80), and 30.3% demonstrated excellent (C-statistic >0.80) performance. Meta-analyses revealed excellent predictive performance for early mortality (C-statistic: 0.81; 95% confidence interval [CI], 0.65-0.91), acceptable performance for 1-year mortality (C-statistic: 0.76; 95% CI, 0.67-0.84), and acceptable performance for predicting permanent pacemaker implantation (C-statistic: 0.75; 95% CI, 0.51-0.90).

CONCLUSIONS

ML models for TAVI outcomes exhibit adequate-to-excellent performance, suggesting potential clinical utility. We identified concerns in methodology and transparency, emphasizing the need for improved scientific reporting standards.

Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years

BACKGROUND

Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology.

RESULTS

We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology.

CONCLUSIONS

The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

Fiberscope-Based Measurement of Coaptation Height for Intraoperative Assessment of Mitral Valve Repair

BACKGROUND

Restoring adequate coaptation height is a key principle of mitral valve (MV) repair. This study aimed to evaluate the utility of fiberscope (FS) technology to assess MV coaptation height for intraoperative use.

METHODS

Ex-vivo testing was performed on five adult porcine hearts. The left atrium (LA) was resected, and the left ventricle (LV) was pressurized retrograde to 27 ± 1mm Hg. An endoscope was inserted into the LV apex, centered under the MV orifice. An FS system (Milliscope II camera, LED light source, and 0.7 mm diameter × 15 cm long) 90° semirigid scope with 1.2 mm focal length) was mounted above the MV annulus in a custom alignment and measuring fixture. Three blinded measurements were taken at two locations on each MV, A2 and P2 segment, from the top of coaptation to the leaflet edge identified by the FS. Accurate positioning was verified using the LV endoscope. A control (metal rod of similar thickness) was used for comparison, with coaptation height recorded when the control was seen via the endoscope.

RESULTS

Coaptation heights were similar for the control and FS methods across all hearts at A2 (11.6 ± 2.6 mm control vs 11.8 ± 2.2 mm FS) and P2 (13.3 ± 2.6 mm control vs 13.4 ± 2.9 mm FS) segments, with similar measurement variability (control SD 0.1-1.0 mm; FS SD 0.1-0.9 mm). One outlier was excluded from analysis (n = 19/20). The maximum absolute difference and percent error between measurement methods were less than 1.1 mm (median [IQR], 0.6 [0.3-0.9] mm) and less than 14% (4.1 [2.2-7.6]%).

CONCLUSIONS

Utilization of a miniaturized FS enabled precise and accurate quantification of MV coaptation. This technique is promising for evaluating post-repair valve competence and coaptation height.

The usefulness of staged extra-anatomic bypass for coarctation of the aorta with arch hypoplasia, from premature neonate to adult

Various surgical techniques have been developed for coarctation of the aorta. However, coarctation repair in neonates with arch hypoplasia remains challenging. We herein report a case in which a premature neonate under 1500 g with coarctation of the aorta and arch hypoplasia underwent an extra-anatomical bypass at 18 days old. A second extra-anatomical bypass was performed at 3 years of age, and a third extra-anatomic bypass for recurrent coarctation was performed in adulthood. By increasing the size of the graft as the patient grows, extra-anatomic bypass can be a useful surgical option for premature neonates with coarctation and arch hypoplasia.

Discrimination and net-reclassification of cardiovascular disease risk with Lipoprotein(a) levels: The ATTICA study (2002-2022)

BACKGROUND

Lipoprotein(a) [Lp(a)] is a recognized as risk factor for atherosclerotic cardiovascular disease (ASCVD). However, its influence on clinical risk evaluations remains unclear.

OBJECTIVE

This study aimed to determine whether Lp(a) improves CVD risk prediction among apparently healthy adults from the general population.

METHODS

In 2002, n = 3,042 adults free of CVD, residing in Athens metropolitan area, in Greece, were recruited. A 20-year follow-up was conducted in 2022, comprising n = 2,169 participants, of which n = 1,988 had complete data for CVD incidence.

RESULTS

Lp(a) levels were significantly associated with 20-year ASCVD incidence in the crude model (Hazard Ratio per 1 mg/dL: 1.004, p = 0.048), but not in multi-adjusted models considering demographic, lifestyle, and clinical factors. Adding Lp(a) to the Reynolds Risk Score (RRS) and Framingham Risk Score (FRS) variables resulted in positive Net Reclassification Improvement (NRI) values (0.159 and 0.160 respectively), indicating improved risk classification. Mediation analysis suggested that C-reactive protein, Interleukin-6, and Fibrinogen mediate the relationship between Lp(a) and ASCVD. No significant interaction was observed between Lp(a) and potential moderators.

CONCLUSION

Lp(a) levels can predict 20-year CVD outcomes and improve CVD risk prediction within the general population, possibly via the intricate relationship between Lp(a), systemic inflammation, atherothrombosis.

Geographical and socioeconomic disparities in post-transcatheter aortic valve replacement pacemaker placement

INTRODUCTION

Pacemaker (PPM) implantation is indicated for conduction abnormalities which can develop post-transcatheter aortic valve replacement (TAVR). However, whether post-TAVR PPM risk is associated with the geographical location of the hospital and socioeconomic status of the patient is not well established. Our goal was to explore geographical and socioeconomic disparities in post-TAVR PPM implantation.

METHODS

A retrospective cohort analysis was conducted using the National Inpatient Sample 2016-2020 with respective ICD-10 codes for TAVR and PPM implantation. A weighted multivariate logistic regression model was used to analyze prognostic outcomes.

RESULTS

The number of patients hospitalized for undergoing TAVR was 296,740, out of which 28,265 patients had PPM implantation (prevalence 9.5 %). Patients' demographics including sex, ethnicity, household income, and insurance were not associated with risk of post-TAVR PPM except age (OR 1.01, CI 1.07-12.5, p < 0.001). Compared to rural hospitals, urban non-teaching hospitals were associated with a higher risk of post-TAVR PPM (OR 2.09, 1.3-3.43, p = 0.003). Compared to New England hospitals (ME, NH, VT, MA, RI, CT), middle Atlantic hospitals (NY, NJ, PA) were associated with highest post-TAVR PPM risk (OR 1.54, CI 1.2-1.98, p < 0.001), followed by Pacific (AK, WA, OR, CA, HI), mountain (ID, MT, WY, NV, UT, CO, AZ, NM) and east north central US.

CONCLUSION

Patients' demographics including sex, ethnicity, household income, and insurance were not associated with the risk of post-TAVR PPM except for age. Compared to New England hospitals, Middle Atlantic hospitals were associated with the highest post-TAVR PPM risk followed by Pacific, Mountain, and East North Central US. Prospective studies with data on TAVR wait times, expertise of the interventional staff, and post-TAVR management and discharge planning are required to further explore the observed regional distribution of TAVR outcomes.

Echocardiography in the neonatal unit: current status and future prospects

INTRODUCTION

Traditionally echocardiography was used by pediatric cardiologists to diagnose congenital heart defects in neonates. Formalized neonatal hemodynamic fellowships have been established where neonatologists acquire advanced echocardiographic skills to gain anatomical, physiological, and hemodynamic information in real time and utilize this information in making a timely and accurate physiology-based clinical decision.

AREA COVERED

Differences between a comprehensive formal structural echocardiography, neonatologist performed targeted echocardiography and limited assessment on point-of-care-ultrasonography for specific indications have been covered. This article is focused at providing a comprehensive review of the status of echocardiography in the neonatal units, recent advancements and its future prospects in the neonatal intensive care units.

EXPERT OPINION

Comprehensive guidelines providing the scope of practice, a framework for training, and robust clinical governance process for the neonatologist performed targeted echocardiography have been established. In the last decade, echocardiography has emerged as essential vital bedside diagnostic tool in providing high-quality care to the sick infants in the neonatal units, and it has proved to improve the outcomes in neonates. It is now being considered as a modern hemodynamic monitoring tool. Advances in technology, machine learning, and application of artificial intelligence in applications of echocardiography seem promising adjunct tools for rapid assessment in emergency situations.

Mini-access ascending aorto-bifemoral bypass surgery for the treatment of aortic steno-occlusive disease

Objectives

Mid-aortic syndrome is a rare condition characterized by severe aortic narrowing, leading to high upper body blood pressure and organ hypoperfusion, necessitating surgical intervention. Although central bypassing is considered ideal, it involves extensive incisions. To overcome these limitations, less-invasive approaches have been developed. This study aims to introduce a mini-access approach using video-endoscopy and to evaluate the feasibility and outcomes of mini-access ascending aorto-bifemoral bypass surgery.

Methods

From November 2020 to May 2022, we performed ascending aorta to bifemoral artery bypass operations on 7 patients to treat steno-occlusive diseases in the downstream aorta. A Y-graft was created, and procedures were conducted under general anesthesia using video-endoscopy with limited skin incisions.

Results

Intraoperatively, there were no major complications, and none of the patients required cardiopulmonary bypass support. Furthermore, there were no postoperative mortalities or major complications. Postoperatively, the mean ankle-brachial index significantly improved from 0.59/0.59 to 0.96/0.92 (P = .004), and the mean glomerular filtration rate increased from 61.1 to 85.3 mL/min/1.73 mm2 (P = .012). Additionally, symptoms of claudication resolved in all patients.

Conclusions

Videoscope-assisted mini-access aortic bypass surgery not only provides favorable early postoperative outcomes but also represents a technically feasible alternative to traditional surgical approaches for the treatment of steno-occlusive aortic diseases.

Accuracy and Efficacy of Artificial Intelligence-Derived Automatic Measurements of Transthoracic Echocardiography in Routine Clinical Practice

Background: Transthoracic echocardiography (TTE) is the gold standard modality for evaluating cardiac morphology, function, and hemodynamics in clinical practice. While artificial intelligence (AI) is expected to contribute to improved accuracy and is being applied clinically, its impact on daily clinical practice has not been fully evaluated. Methods: We retrospectively examined 30 consecutive patients who underwent AI-equipped TTE at a single institution. All patients underwent manual and automatic measurements of TTE parameters using the AI-equipped TTE. Measurements were performed by three sonographers with varying experience levels: beginner, intermediate, and expert. Results: A comparison between the manual and automatic measurements assessed by the experts showed extremely high agreement in the left ventricular (LV) filling velocities (E wave: r = 0.998, A wave: r = 0.996; both p < 0.001). The automated measurements of LV end-diastolic and end-systolic diameters were slightly smaller (-2.41 mm and -1.19 mm) than the manual measurements, although without significant differences, and both methods showing high agreement (r = 0.942 and 0.977, both p < 0.001). However, LV wall thickness showed low agreement between the automated and manual measurements (septum: r = 0.670, posterior: r = 0.561; both p < 0.01), with automated measurements tending to be larger. Regarding interobserver variabilities, statistically significant agreement was observed among the measurements of expert, intermediate, and beginner sonographers for all the measurements. In terms of measurement time, automatic measurement significantly reduced measurement time compared to manual measurement (p < 0.001). Conclusions: This preliminary study confirms the accuracy and efficacy of AI-equipped TTE in routine clinical practice. A multicenter study with a larger sample size is warranted.

Unlocking the potential of artificial intelligence in sports cardiology: does it have a role in evaluating athlete's heart?

The integration of artificial intelligence (AI) technologies is evolving in different fields of cardiology and in particular in sports cardiology. Artificial intelligence offers significant opportunities to enhance risk assessment, diagnosis, treatment planning, and monitoring of athletes. This article explores the application of AI in various aspects of sports cardiology, including imaging techniques, genetic testing, and wearable devices. The use of machine learning and deep neural networks enables improved analysis and interpretation of complex datasets. However, ethical and legal dilemmas must be addressed, including informed consent, algorithmic fairness, data privacy, and intellectual property issues. The integration of AI technologies should complement the expertise of physicians, allowing for a balanced approach that optimizes patient care and outcomes. Ongoing research and collaborations are vital to harness the full potential of AI in sports cardiology and advance our management of cardiovascular health in athletes.

Team Approach: Diagnosis, Management, and Prevention of Sudden Cardiac Arrest in the Athlete

» Sudden cardiac events during sports competition are rare but tragic occurrences that require a timely, comprehensive response by well-prepared athletic trainers and medical providers. This sequence should prioritize prompt emergency medical system activation, immediate initiation of cardiopulmonary resuscitation (CPR), automated early defibrillation (AED), and comprehensive advanced life support efforts.» Exercise-induced cardiac remodeling, referred to as the "athlete's heart," refers to a host of adaptive changes that increase cardiac chamber size and wall thickness to allow for greater pressures and volumes during exercise. This remodeling phenotype may overlap with other inherited cardiomyopathies and cardiac abnormalities, which can complicate clinical care. The long-term implications of this electrical and structural remodeling on cardiac function are unknown.» Although the best screening strategies to optimize primary prevention of sudden cardiac arrest is an evolving topic, the effectiveness of CPR and early defibrillation use in treating out-of-hospital sudden cardiac arrest has been well-established, despite their reported underuse.

Rapid genomic sequencing for genetic disease diagnosis and therapy in intensive care units: a review

Single locus (Mendelian) diseases are a leading cause of childhood hospitalization, intensive care unit (ICU) admission, mortality, and healthcare cost. Rapid genome sequencing (RGS), ultra-rapid genome sequencing (URGS), and rapid exome sequencing (RES) are diagnostic tests for genetic diseases for ICU patients. In 44 studies of children in ICUs with diseases of unknown etiology, 37% received a genetic diagnosis, 26% had consequent changes in management, and net healthcare costs were reduced by $14,265 per child tested by URGS, RGS, or RES. URGS outperformed RGS and RES with faster time to diagnosis, and higher rate of diagnosis and clinical utility. Diagnostic and clinical outcomes will improve as methods evolve, costs decrease, and testing is implemented within precision medicine delivery systems attuned to ICU needs. URGS, RGS, and RES are currently performed in <5% of the ~200,000 children likely to benefit annually due to lack of payor coverage, inadequate reimbursement, hospital policies, hospitalist unfamiliarity, under-recognition of possible genetic diseases, and current formatting as tests rather than as a rapid precision medicine delivery system. The gap between actual and optimal outcomes in children in ICUs is currently increasing since expanded use of URGS, RGS, and RES lags growth in those likely to benefit through new therapies. There is sufficient evidence to conclude that URGS, RGS, or RES should be considered in all children with diseases of uncertain etiology at ICU admission. Minimally, diagnostic URGS, RGS, or RES should be ordered early during admissions of critically ill infants and children with suspected genetic diseases.

Strengths-weaknesses-opportunities-threats analysis of artificial intelligence in anesthesiology and perioperative medicine

The use of artificial intelligence (AI) and machine learning (ML) in anesthesiology and perioperative medicine is quickly becoming a mainstay of clinical practice. Anesthesiology is a data-rich medical specialty that integrates multitudes of patient-specific information. Perioperative medicine is ripe for applications of AI and ML to facilitate data synthesis for precision medicine and predictive assessments. Examples of emergent AI models include those that assist in assessing depth and modulating control of anesthetic delivery, event and risk prediction, ultrasound guidance, pain management, and operating room logistics. AI and ML support analyzing integrated perioperative data at scale and can assess patterns to deliver optimal patient-specific care. By exploring the benefits and limitations of this technology, we provide a basis of considerations for evaluating the adoption of AI models into various anesthesiology workflows. This analysis of AI and ML in anesthesiology and perioperative medicine explores the current landscape to understand better the strengths, weaknesses, opportunities, and threats (SWOT) these tools offer.

In-Depth Genomic Analysis: The New Challenge in Congenital Heart Disease

The use of next-generation sequencing has provided new insights into the causes and mechanisms of congenital heart disease (CHD). Examinations of the whole exome sequence have detected detrimental gene variations modifying single or contiguous nucleotides, which are characterised as pathogenic based on statistical assessments of families and correlations with congenital heart disease, elevated expression during heart development, and reductions in harmful protein-coding mutations in the general population. Patients with CHD and extracardiac abnormalities are enriched for gene classes meeting these criteria, supporting a common set of pathways in the organogenesis of CHDs. Single-cell transcriptomics data have revealed the expression of genes associated with CHD in specific cell types, and emerging evidence suggests that genetic mutations disrupt multicellular genes essential for cardiogenesis. Metrics and units are being tracked in whole-genome sequencing studies.

Closing the Last Mile Gap in Access to Multimodality Imaging in Rural Settings: Design of the Imaging Core of the Risk Underlying Rural Areas Longitudinal Study

Achieving optimal cardiovascular health in rural populations can be challenging for several reasons including decreased access to care with limited availability of imaging modalities, specialist physicians, and other important health care team members. Therefore, innovative solutions are needed to optimize health care and address cardiovascular health disparities in rural areas. Mobile examination units can bring imaging technology to underserved or remote communities with limited access to health care services. Mobile examination units can be equipped with a wide array of assessment tools and multiple imaging modalities such as computed tomography scanning and echocardiography. The detailed structural assessment of cardiovascular and lung pathology, as well as the detection of extracardiac pathology afforded by computed tomography imaging combined with the functional and hemodynamic assessments acquired by echocardiography, yield deep phenotyping of heart and lung disease for populations historically underrepresented in epidemiological studies. Moreover, by bringing the mobile examination unit to local communities, innovative approaches are now possible including engagement with local professionals to perform these imaging assessments, thereby augmenting local expertise and experience. However, several challenges exist before mobile examination unit-based examinations can be effectively integrated into the rural health care setting including standardizing acquisition protocols, maintaining consistent image quality, and addressing ethical and privacy considerations. Herein, we discuss the potential importance of cardiac multimodality imaging to improve cardiovascular health in rural regions, outline the emerging experience in this field, highlight important current challenges, and offer solutions based on our experience in the RURAL (Risk Underlying Rural Areas Longitudinal) cohort study.

Pathomechanisms of Disturbances Underlying Chronic Disorders

Chronic disorders' complexity poses enormous challenges to our understanding of such disorders [...].

Exercise Stress Echocardiography in Athletes: Applications, Methodology, and Challenges

This comprehensive review explores the role of exercise stress echocardiography (ESE) in assessing cardiovascular health in athletes. Athletes often exhibit cardiovascular adaptations because of rigorous physical training, making the differentiation between physiological changes and potential pathological conditions challenging. ESE is a crucial diagnostic tool, offering detailed insights into an athlete's cardiac function, reserve, and possible arrhythmias. This review highlights the methodology of ESE, emphasizing its significance in detecting exercise-induced anomalies and its application in distinguishing between athlete's heart and other cardiovascular diseases. Recent advancements, such as LV global longitudinal strain (GLS) and myocardial work (MW), are introduced as innovative tools for the early detection of latent cardiac dysfunctions. However, the use of ESE also subsumes limitations and possible pitfalls, particularly in interpretation and potential false results, as explained in this article.

The Relationship between Pulmonary Artery Pressure and Mortality in Type 2 Diabetes: A Fremantle Diabetes Study Phase II and National Echocardiographic Database of Australia Data Linkage Study

An elevated estimated right ventricular systolic pressure (eRVSP) identified on echocardiography is present in one-third of individuals with type 2 diabetes, but its prognostic significance is unknown. To assess the relationship between eRVSP and mortality, prospective data from 1732 participants in the Fremantle Diabetes Study Phase II were linked with the National Echocardiographic Database of Australia. Of this cohort, 416 (mean age 70.6 years, 47.4% males) had an eRVSP measured and 381 (91.4%) had previously confirmed type 2 diabetes. Receiver- operating characteristic analysis of the relationship between eRVSP and all-cause mortality was conducted. Survival analyses were performed for participants with type 2 diabetes diagnosed before first measured eRVSP (n = 349). Cox regression identified clinical and echocardiographic associates of all-cause mortality. There were 141 deaths (40.4%) during 2348 person-years (mean ± SD 6.7 ± 4.0 years) of follow-up. In unadjusted Kaplan-Meier analysis, mortality rose with higher eRVSP (log-rank test, p < 0.001). In unadjusted pairwise comparisons, eRVSP >30 to 35, >35 to 40, and >40 mmHg had significantly increased mortality compared with eRVSP ≤ 30 mmHg (p = 0.025, p = 0.001, p < 0.001, respectively). There were 50 deaths in 173 individuals (29.1%) with eRVSP ≤ 30 mmHg, and 91 in 177 (51.4%) with eRVSP > 30 mmHg (log-rank test, p < 0.001). In adjusted models including age, Aboriginal descent, Charlson Comorbidity Index ≥ 3 and left heart disease, eRVSP > 30 mmHg predicted a two-fold higher all-cause mortality versus ≤ 30 mmHg. An eRVSP > 30 mmHg predicts increased all-cause mortality in type 2 diabetes. Where available, eRVSP could inform type 2 diabetes outcome models.

The Echocardiographic Evaluation of the Right Heart: Current and Future Advances

PURPOSE OF REVIEW

To discuss physiologic and methodologic advances in the echocardiographic assessment of right heart (RH) function, including the emergence of artificial intelligence (AI) and point-of-care ultrasound.

RECENT FINDINGS

Recent studies have highlighted the prognostic value of right ventricular (RV) longitudinal strain, RV end-systolic dimensions, and right atrial (RA) size and function in pulmonary hypertension and heart failure. While RA pressure is a central marker of right heart diastolic function, the recent emphasis on venous excess imaging (VExUS) has provided granularity to the systemic consequences of RH failure. Several methodological advances are also changing the landscape of RH imaging including post-processing 3D software to delineate the non-longitudinal (radial, anteroposterior, and circumferential) components of RV function, as well as AI segmentation- and non-segmentation-based quantification. Together with recent guidelines and advances in AI technology, the field is shifting from specific RV functional metrics to integrated RH disease-specific phenotypes. A modern echocardiographic evaluation of RH function should focus on the entire cardiopulmonary venous unit-from the venous to the pulmonary arterial system. Together, a multi-parametric approach, guided by physiology and AI algorithms, will help define novel integrated RH profiles for improved disease detection and monitoring.