Unexplained sudden cardiac arrest (SCA) and sudden cardiac death (SCD) in the young remains a critical issue for clinicians, researchers, patients and their family members. In this review, we explore the current status of SCA and SCD evaluation in the young, including recent monogenic and polygenic disease discoveries, advancements in cardiac imaging and our growing understanding of the role of the Purkinje system in triggering life threatening and even fatal ventricular arrhythmias. Yet, despite these advancements, over a third of SCA and SCD among individuals with seemingly structurally normal hearts remain unexplained. We explore the future role of artificial intelligence, novel biomarkers, multimodality cardiac imaging, genetic discoveries, as well as wearable devices and sensors in closing this current gap. With the overarching framework provided in this review, we envision a future in which collaboration among patients, clinicians, researchers, innovators, and policy makers culminates in our ability to diagnose, predict, and ultimately prevent SCA and SCD in the young.

Cardiovascular disease remains a major contributor to maternal morbidity and mortality in the United States. Cardio Obstetrics is a nascent field for which most cardiovascular clinicians have not received any formal training. This has resulted in knowledge and care gaps. In this review we provide principles to guide the care for the evaluation and management of pregnancy capable individuals, which should be considered the standard knowledge for all clinicians.

Personalized healthcare is becoming increasingly popular given the vast heterogeneity in disease manifestation between individuals. Many commonly encountered diseases within cardiology are multifactorial in nature and disease progression and response is often variable due to environmental and genetic factors influencing disease states. This makes accurate early identification and primary prevention difficult in certain populations, especially young patients with limited Atherosclerotic Cardiovascular Disease (ASCVD) risk factors. Newer strategies, such as coronary artery calcium (CAC) scans and polygenic risk scores (PRS), are being implemented to aid in the detection of subclinical disease and heritable risk, respectively. Data surrounding CAC scans have shown promising results in their ability to detect subclinical atherosclerosis and predict the risk of future coronary events, especially at the extremes; however, predictive variability exists among different patient populations, limiting the test's specificity. Furthermore, relying only on CAC scores and ASCVD risk scores may fail to identify a large group of patients needing primary prevention who lack subclinical disease and traditional risk factors, but harbor genetic variabilities strongly associated with certain cardiovascular diseases. PRS can overcome these limitations. These scores can be measured in individuals as early as birth to identify genetic variants placing them at elevated risk for developing cardiovascular disease, irrespective of their current cardiovascular health status. By applying PRS alongside CAC scores, previously overlooked patient populations can be identified and begin primary prevention strategies early to achieve optimal outcomes. In this review, we expand on the current knowledge surrounding CAC scores and PRS and highlight the future possibilities of these technologies for preventive cardiology.

Optimal guideline-directed medical therapy for heart failure with reduced ejection fraction comprises the angiotensin receptor-neprilysin inhibitor (sacubitril/valsartan), an evidence-based beta-blocker (bisoprolol, carvedilol, or sustained-release metoprolol), a mineralocorticoid antagonist (spironolactone or eplerenone), and a sodium-glucose cotransporter-2 inhibitor (dapagliflozin or empagliflozin). Optimal guideline-directed medical therapy for heart failure with preserved ejection fraction comprises a sodium-glucose cotransporter-2 inhibitor with emerging evidence to support the use of a mineralocorticoid antagonist and glucagon-like peptide-1 receptor agonists. This review will summarize the evidence behind the guideline recommendations, the impact of newer trials on management of patients with HF, and strategies for implementation into clinical practice.

Arrhythmogenic cardiomyopathy (ACM) is an inherited cardiac disease which predisposes to ventricular arrhythmias and sudden cardiac death. Since the introduction of the first diagnostic criteria in 1994, which focused exclusively on right ventricular involvement, diagnostic guidelines have evolved significantly over the past 30 years to encompass the full complexity of the ACM phenotype. In this issue of Trends in Cardiovascular Medicine, Graziano and colleagues review the advancements in ACM diagnostics which emphasizes a comprehensive evaluation of morpho-functional, structural, electrical, and genetic characteristics. The review outlines a three-step clinical approach for diagnosing ACM that involves assessing left and/or right ventricular involvement, identifying the specific ACM subtype, and determining its underlying etiology. This highlights the importance of interdisciplinary teamwork when approaching the complexities of diagnosing ACM and managing the family at risk.

Arrhythmogenic Cardiomyopathy (ACM) is a cardiac disorder characterized by non-ischemic myocardial scarring, which may lead to ventricular electrical instability and systolic dysfunction. Diagnosing ACM is challenging as there is no single gold-standard test and a combination of criteria is required. The first diagnostic criteria were established in 1994 and revised in 2010, focusing primarily on right ventricular involvement. However, in 2019, an international expert report identified limitations of previous diagnostic scoring and developed the 2020 Padua criteria with also included criteria for diagnosis of left ventricular variants and introduced cardiac magnetic resonance tissue characterization findings for detection of left ventricular myocardial scar. These criteria were further refined and published in 2023 as the European Task Force criteria, gaining international recognition. This review provides an overview of the 20 years of progresses on the disease diagnostic from the original 1994 criteria to the most recent 2023 European criteria, highlighting the evolution into our understanding of the pathobiology and morpho-functional features of the disease.

Historically, individuals with HCM have been restricted from vigorous competitive sports due to concerns for risk of sudden death. More recently, prospective data are emerging that individuals with HCM who participate in vigorous sports do not have increased arrhythmic risk compared to the less active, and series of athletes with HCM continuing to compete, while small, have not shown high risk. Guidelines are evolving, and while differences exist, all now recommend an individualized approach and shared decision-making for athletes with HCM wishing to return to play.

Drug-coated balloons have emerged as a promising therapeutic option in the treatment of cardiovascular disease. This review article provides an overview of the concept of drug-coated balloons and their clinical applications in both de novo and treated coronary artery disease. A summary of key clinical trials and registry studies evaluating drug-coated balloons is presented for reference. Overall, this article aims to provide clinicians and researchers with a comprehensive understanding of the current state of drug-coated balloon technology and its implications in clinical practice.

Hypertrophic Cardiomyopathy (HCM) presents a complex diagnostic and prognostic challenge due to its heterogeneous phenotype and clinical course. Artificial Intelligence (AI) and Machine Learning (ML) techniques hold promise in transforming the role of Electrocardiography (ECG) in HCM diagnosis, prognosis, and management. AI, including Deep Learning (DL), enables computers to learn patterns from data, allowing for the development of models capable of analyzing ECG signals. DL models, such as convolutional neural networks, have shown promise in accurately identifying HCM-related abnormalities in ECGs, surpassing traditional diagnostic methods. In diagnosing HCM, ML models have demonstrated high accuracy in distinguishing between HCM and other cardiac conditions, even in cases with normal ECG findings. Additionally, AI models have enhanced risk assessment by predicting arrhythmic events leading to sudden cardiac death and identifying patients at risk for atrial fibrillation and heart failure. These models incorporate clinical and imaging data, offering a comprehensive evaluation of patient risk profiles. Challenges remain, including the need for larger and more diverse datasets to improve model generalizability and address imbalances inherent in rare event prediction. Nevertheless, AI-driven approaches have the potential to revolutionize HCM management by providing timely and accurate diagnoses, prognoses, and personalized treatment strategies based on individual patient risk profiles. This review explores the current landscape of AI applications in ECG analysis for HCM, focusing on advancements in AI methodologies and their specific implementation in HCM care.

Renal denervation as an option for difficult to treat hypertension has been a concept for several decades, with recent U.S. FDA approval of new, minimally invasive devices. However, while renal denervation has the potential to improve hypertension management, several challenges require consideration prior to widespread adoption. The effect relative to sham control is modest, and generally similar to addition of a single blood pressure lowering medication. It is possible that with additional technique refinement greater effects may be possible. Key factors to consider beyond the direction, strengths, and limitations of the renal denervation technologies themselves, are an understanding of patient groups that derive greatest benefit and phenotypes or biomarkers that predict greater response. This review provides an update on these challenges in addition to the current state and future of renal denervation within the context of hypertension management and treatment.

Quinidine, the first antiarrhythmic drug, was widely used during the 20th century. Multiple studies have been conducted to provide insights into the pharmacokinetics and pleiotropic effects of Class Ia antiarrhythmic drugs. However, safety concerns and the emergence of new drugs led to a decline in their use during the 1990s. Despite this, recent studies have reignited the interest in quinidine, particularly for ventricular arrhythmias, where other antiarrhythmics have failed. In conditions such as Brugada syndrome, idiopathic ventricular fibrillation, early repolarization syndrome, short QT syndrome, and electrical storms, quinidine remains a valuable asset. Starting from the European and American recommendations, this comprehensive review aimed to explore the various indications for quinidine and the studies that support its use. We also discuss the potential future of quinidine, including the necessary research to optimize its use and patient selection. Additionally, it addresses the imperative task of mitigating the iatrogenic burden associated with quinidine usage and confronts the challenge of ensuring drug accessibility.