Most patients with acute central nervous system (CNS) lesions, including acute ischemic stroke, intracranial bleeding, traumatic brain or spinal cord injury, exhibit electrocardiographic (ECG) abnormalities. These are generally transient and considered secondary to the neurological condition. The most common neurogenic ECG alterations include ST-segment elevation or depression, T wave inversion, prominent U wave, prolonged QTc interval, sinus bradycardia and atrioventricular block. The primary mechanism involves autonomic nervous system alterations due to damage to the central autonomic network, causing regional changes in myocardial action potential duration, altered ventricular repolarization gradients, altered automaticity, or altered atrioventricular node conduction. These may promote arrhythmias including atrial fibrillation and ventricular arrhythmias. Sympathetic hyperactivation may additionally induce non-ischemic acute myocardial injury characterized by contraction band necrosis. The functional neuroanatomy of the central autonomic network determines the diverse cardiac manifestations observed with distinct CNS injuries. Insular cortex lesions typically result in sympathetic hyperactivation, whereas brain stem or spinal cord lesions above T6 can cause autonomic failure with reduced sympathetic tone and altered autonomic reflexes. While most patients exhibit asymptomatic ECG abnormalities, initial clinical evaluation should identify underlying cardiac conditions such as Takotsubo cardiomyopathy, myocardial infarction, or pre-existing chronic coronary syndromes. Serial ECG, cardiac biomarkers and echocardiography are key. Additional targeted work-up may be useful in selected patients. The implications of distinct ECG findings for neurological and cardiovascular prognosis should also be considered.

Cardiac implantable electronic device (CIED) infections remain a major source of morbidity, mortality, and healthcare expenditure despite continuing advances in device technology. Increasing procedural complexity, repeat interventions, and an aging population with multiple comorbidities have shifted the focus from treatment alone toward comprehensive infection prevention. Biofilm formation is now recognized as the central pathogenic mechanism underlying CIED infection, explaining both the limited efficacy of antimicrobial therapy in the presence of retained hardware and the need for complete system extraction in established infection. This narrative review summarizes contemporary evidence supporting a multimodal approach to CIED infection prevention and personalized management. Prevention extends beyond perioperative antibiotic prophylaxis and skin antisepsis to encompass structured patient optimization, procedural contamination control, hematoma prevention, targeted Staphylococcus aureus decolonization, antibacterial envelopes, taurolidine-based pocket antisepsis, emerging antibiofilm technologies, and novel device platforms such as leadless pacemakers and extravascular implantable cardioverter-defibrillators. Risk stratification using PADIT, BLISTER, and complementary prediction models enables individualized allocation of preventive interventions according to estimated infection risk rather than uniform prophylaxis. Advances in diagnosis now combine multimodality imaging with biofilm-oriented microbiology, including device sonication, molecular diagnostics, and metagenomic sequencing, supporting pathogen-directed antimicrobial therapy and individualized clinical decision-making. Contemporary management likewise requires multidisciplinary expertise integrating extraction-risk assessment, complete hardware removal when indicated, optimized antimicrobial stewardship, and carefully planned reimplantation strategies. Rather than using the term precision medicine in its traditional genomic sense, this review emphasizes precision prevention and personalized management, whereby preventive and therapeutic interventions are tailored to each patient's quantified infection risk by integrating procedural, microbiological, host-related, and biofilm-associated determinants. Future improvements in clinical outcomes will depend on implementing integrated, risk-guided prevention strategies supported by multidisciplinary expert teams and emerging diagnostic and preventive technologies.

Consumer wearables have made atrial fibrillation (AF) detectable at population scale. Since 2019, smartwatch and smartphone studies enrolling thousands of participants have shown that photoplethysmography-based algorithms reliably generate AF notifications in real-world use, and that such a notification carries a high positive predictive value when benchmarked against a gold-standard electrocardiogram patch. This review follows the evidence along the clinical chain, from detection through notification and confirmation of diagnosis to treatment and outcome, and the strength of that evidence diminishes at each link. Population-scale studies established feasibility but did not measure sensitivity. Diagnostic accuracy is heterogeneous and conditional: it depends on the reference standard, the degree of supervision, the population studied, and the handling of inconclusive recordings. Randomised trials show that wearable-enabled pathways can accelerate diagnosis, a process outcome; yet none has shown that wearable-detected AF screening reduces stroke, systemic embolism or death. The treatment evidence that would justify acting on a detected arrhythmia comes from implanted-device populations, not consumer-wearable cohorts, and shows that anticoagulating device-detected AF trades a reduction in ischaemic stroke against an increase in major bleeding. Implementation raises further unresolved problems: confirmation-pathway heterogeneity, workflow burden, adherence, equity and overdiagnosis. Consumer wearables can detect AF at population scale, yet whether this improves hard clinical outcomes remains unresolved. Ongoing randomised trials must answer this critical question.

Racial and ethnic disparities in percutaneous coronary intervention (PCI) persist despite major advances in contemporary acute coronary syndrome (ACS) care and represent an important challenge in achieving equitable cardiovascular outcomes. In this State-of-the-Art Review, we propose a PCI care-continuum framework demonstrating how inequities emerge sequentially across multiple stages of care; including baseline cardiovascular risk, prehospital triage, invasive coronary angiography, PCI decision-making, procedural optimization, and post-procedural outcomes. Contemporary evidence from randomized trials, large PCI registries, administrative datasets, and systems-of-care analyses demonstrate that Black, Hispanic, and certain Asian populations experience higher cardiometabolic burden, delayed presentation, reduced access to invasive management, and persistent disparities in clinical outcomes compared with White populations. These inequities are further amplified by structural determinants including residential segregation, unequal distribution of PCI-capable facilities, insurance-related barriers, and variation in healthcare delivery processes. Addressing these disparities requires coordinated multi-level interventions focused on standardized prehospital pathways, equitable access to invasive care, reduction of structural barriers, and implementation of equity-focused quality metrics. Improving PCI equity will require health systems to move beyond procedural excellence alone and address disparities across the entire cardiovascular care pathway.

Transcatheter aortic valve replacement (TAVR) is a more prevalent procedure than surgical aortic valve replacement (SAVR) for severe aortic stenosis (AS). With increasing TAVR utilization in young and low-risk patients, understanding long-term outcomes of TAVR is critical. Three randomized TAVR vs SAVR trials in patients with severe AS were identified with outcomes reported beyond 5 years: the NOTION (10-year outcomes), PARTNER 3 (7-year outcomes), and Evolut Low-Risk (6 year overall outcomes and 7 year reintervention data) trials. Primary trial outcomes, all of which were composite outcomes including all-cause mortality, stroke, myocardial infarction, and/or valve-, procedure-, or heart failure-related readmission, demonstrated similar outcomes between TAVR and SAVR (NOTION: 65.5% vs 65.5%, P = 0.9; PARTNER 3: 34.6% vs 37.2%, HR 0.87 [95% CI 0.70-1.08]; Evolut Low Risk: 23.3% vs 20.4%, P = 0.43). Amongst secondary outcomes, the cumulative incidence of TAVR reintervention was similar to SAVR reintervention in the NOTION trial (4.3%vs 2.2%, P = 0.3) and the PARTNER 3 trial (6.7%vs 6.0%, HR 1.11 [0.63-1.94]), but TAVR reintervention was higher than SAVR reintervention in the Evolut Low-Risk trial (9.8%vs 6.0%; p = 0.02). Study limitations include small cohort size (145 undergoing TAVR) of older patients undergoing TAVR with early generation self-expanding TAVRs in the NOTION trial and no data yet beyond 7 years in the PARTNER 3 and Evolut Low-Risk trials. Despite limitations, these data highlight the criticality in extremely careful patient selection for TAVR to optimize long term outcomes in the era of lifetime management of AS.

Arrhythmias are increasingly recognized cardiovascular complications in patients with cancer, arising from both the malignancy and its therapies. Once considered unimportant compared to cancer treatment-related heart failure, arrhythmias, particularly atrial fibrillation (AF) and ventricular arrhythmias (VA), are now recognized to play a major role in cardio-oncology due to their prognostic impact and effect on treatment continuity. Anthracyclines, immune checkpoint inhibitors and Bruton's tyrosine kinase inhibitors are linked to higher arrhythmic risk, ranging from asymptomatic ECG abnormalities to QT prolongation (QTp), conduction disorders and AF. Cellular therapies are linked to AF, mainly driven by cytokine-mediated myocardial inflammation. Cyclin-dependent kinase 4/6 inhibitors, especially ribociclib, may induce dose-related QTp and, less commonly, VA. Additionally, Takotsubo syndrome may occur in patients with active cancer due to stress and is often accompanied by early arrhythmic complications. Awareness and prevention of arrhythmias are essential to optimize patients' outcomes. Arrhythmias management requires individualized assessment, ECG surveillance, and multidisciplinary follow-up. We additionally propose a six-step framework to support early detection and prompt intervention of arrhythmias.

Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide, and current risk scores based on personal history, physical examination and basic tests fail to identify a proportion of individuals who will experience cardiovascular events. In this context, handheld echocardiography (HHE) is an intermediate step between clinical assessment alone and comprehensive transthoracic echocardiography, with the potential to act as an imaging-based tool within cardiovascular prevention programmes. Through focused, bedside or outpatient examinations, HHE can detect early signs of hypertensive organ damage as well as left and right ventricular dysfunction and other structural cardiovascular abnormalities, beyond traditional clinical evaluation. Through an examination of current evidence, technological developments, and clinical implementation pathways, this review provides a comprehensive overview of the evolving role of HHE as an adjunct to standard cardiovascular assessment, highlighting its potential to enhance early risk stratification in preventive cardiology. In this context, it proposes a pragmatic worklist for focused HHE examinations in at-risk individuals and illustrates representative HHE use cases, with involvement of non-cardiologists and the potential contribution of teleconsultation and digital integration. Finally, unresolved challenges are highlighted, defining the research priorities for the systematic integration of HHE into cardiovascular prevention strategies.

Sleep is a multidimensional physiological state, and it is increasingly recognized as a core determinant of cardiovascular (CV) health. This review summarises the CV health attributes of important sleep dimensions, including sleep duration, regularity, timing, quality, efficiency, and napping. New device-measured data continue to demonstrate the importance of both direct and synergistic effects of sleep with movement and dietary lifestyle behaviours in relation to CV risk. Emerging adoption of the 24-hour framework by guidelines highlights the importance of addressing sleep, as part of the broader cluster of lifestyle-related health behaviours, for a more comprehensive understanding of health risks and intervention opportunities. The reviewed literature indicates that sleep, both independently and through its interrelationships with other lifestyle behaviours, plays an important role in shaping CV health. We identified potential interconnected mechanisms through which poor sleep may influence risk of cardiovascular disease (CVD), including heightened sympathetic drive, altered neuroendocrine activity, inflammatory and immune changes, metabolic dysregulation, and weight gain. Large-scale device-based prospective studies capturing multidimensional sleep characteristics are needed to further delineate causal pathways linking sleep dimensions to CV risk. Such an evidence base will advance holistic CVD prevention through better clinical interventions and public health policy.

The global burden of valvular heart disease (VHD) is increasingly burdensome, and precise early diagnosis combined with accurate risk stratification constitutes the core strategy for improving patient prognosis. As the first-line imaging modality for VHD assessment, echocardiography is constrained by interobserver variability and cumbersome, time-consuming data processing workflows, which prevent it from fully meeting the demands of precision medicine. In recent years, breakthroughs in artificial intelligence (AI), particularly deep learning (DL) technologies, have been reshaping the paradigm of imaging-based evaluation for VHD. This review systematically summarizes the latest advances in AI applications across the entire workflow of echocardiographic assessment in VHD: from the precise segmentation of valvular anatomical structures and identification of lesions using convolutional neural networks, to the automated grading of hemodynamic severity achieved through end-to-end learning. More importantly, this article explores how AI can surpass the limitations of traditional imaging indicators by leveraging unsupervised clustering to unearth potential high-risk phenotypes and integrating multimodal data to predict adverse outcomes. Finally, the paper critically analyzes the current challenges in data standardization, model interpretability, and clinical translation, and offers perspectives on future directions in the intersection of clinical medicine and engineering.