Despite major technological advances, contemporary drug-eluting stents (DES) remain intrinsically limited by the permanent implantation of a metallic prosthesis, exposing patients to chronic vessel caging, impaired vasomotion, neoatherosclerosis, and persistent risks of restenosis and late thrombotic events. These limitations have renewed interest in implant-sparing coronary revascularization based on the "leave-nothing-behind" concept. Drug-coated balloons (DCB) have emerged as the most mature implant-free technology, with robust evidence supporting their use beyond in-stent restenosis and small-vessel disease to increasingly complex clinical scenarios, including chronic total occlusions, diffuse coronary artery disease, bifurcation lesions, diabetes mellitus, and patients at high bleeding risk. Successful DCB angioplasty, however, depends on meticulous lesion preparation, tailored plaque modification, and selective intracoronary imaging to optimize procedural outcomes. In parallel, new-generation bioresorbable scaffolds (BRS) and bioadaptors are revitalizing the concept of temporary vessel scaffolding. By incorporating thinner struts, improved mechanical properties, enhanced biocompatibility, and restoration of vascular physiology following resorption or uncaging, these technologies aim to overcome the thrombotic limitations that hindered first-generation devices. This review provides a comprehensive overview of the biological rationale, current clinical evidence, procedural optimization, and future perspectives of implant-sparing PCI, highlighting the ongoing transition from a device-centered strategy to a personalized, morphology-guided approach in which lesion characteristics, vascular biology, and patient-specific risk drive revascularization decision-making.

Genetic testing for dilated and arrhythmogenic cardiomyopathies (DCM, ACM) is increasingly important as it can refine diagnosis, inform risk stratification, and enable cascade screening. However, its clinical application remains challenging due to variable penetrance, phenotypic overlap, and marked genetic heterogeneity. This review provides a practical framework for the use of genetic testing in DCM and ACM. We outline a stepwise approach to clinical phenotyping and patient selection, review the principles of genetic testing and result interpretation, and summarize key pre- and post-test counseling considerations. We also highlight clinically important genotypes with implications for prognosis, arrhythmic risk, decisions regarding implantable cardioverter-defibrillator (ICD) implantation, family screening, and lifestyle counseling. Finally, we discuss the two-hit model that determines disease expressivity, emerging targeted therapies, and future directions in precision care.

Carotid artery disease remains a major cause of acute ischemic neurologic syndromes. Contemporary care has evolved into a multi-disciplinary model, involving vascular medicine, neurology, vascular surgery, interventional radiology, interventional cardiology and others. An integrated approach that combines plaque characteristics, symptom timing, cerebral vulnerability, vascular anatomy, patient age and comorbidities is employed to identify optimal treatment strategies. This review focuses on mechanisms of carotid-related stroke, imaging, medical therapy, revascularization of symptomatic and asymptomatic patients and procedural planning. Emphasis is placed on vascular anatomy-driven procedural selection, embolic-risk mitigation, and the complementary roles of carotid endarterectomy, carotid artery stenting, and transcarotid artery revascularization. Optimal outcomes depend less on applying a single preferred revascularization procedure and more on selecting the strategy that most effectively reduces embolic risk and minimizes patient-specific hazards.

Since 2020, artificial intelligence (AI) has been increasingly applied to atherosclerotic cardiovascular disease (ASCVD) risk prediction. This structured narrative review with systematic evidence mapping summarizes literature (2020-2026) examining study design, data sources, model architectures, multimodal fusion, model development and validation, performance evaluation, subgroup applications, and implementation barriers. Overall, 126 studies informed the review; 93 provided sufficient information for structured extraction, including prevention setting, exact input variables, comparator scores, validation strategies, discrimination, calibration, dominant model architecture, endpoint category, foundation-model or pretrained-model status, regulatory status, and implementation features. AI-based models may offer modest but clinically meaningful gains over conventional risk equations, especially with multimodal or longitudinal data. Among the 93 studies, traditional machine learning accounted for 83 (89%), deep learning for 7 (8%), and multimodal fusion for 3 (3%). Endpoint definitions were heterogeneous (23% ASCVD-specific; 63% expanded MACE composites). Among these studies, no large language model or federated learning was used for risk prediction. Appropriate comparators should now include contemporary equations such as PREVENT, rather than only legacy tools. Major barriers remain, including limited external validation, performance attenuation, data and algorithmic bias, limited interpretability, inconsistent reporting of calibration, fairness, and clinical utility, unclear regulatory status, and limited prospective evidence. Future work should prioritize calibration, robustness, transportability, fairness, interpretability, regulatory clarity, workflow integration, and prospective evidence of decision impact or post-deployment benefit. The central question is not only whether AI can detect complex patterns, but whether such models can be trusted, implemented, and shown to advance preventive cardiology in real-world settings.