Giornale italiano di cardiologia最新文献

Infective endocarditis is a pathology that still presents a high burden of morbidity and mortality. Several observational studies and national and international registries showed that patients undergoing invasive cardiovascular procedures (surgical or percutaneous) are at higher risk of developing infective endocarditis, resulting in increased morbidity, length of hospital stay and mortality related to these procedures. The role of antibiotic prophylaxis is therefore crucial in the prevention of postoperative infections. This is accompanied by new emerging strategies in the pre- and postoperative nursing management of patients.

Background: Heart failure (HF) significantly impacts on morbidity, mortality, and healthcare use, particularly in elderly populations and underserved areas. Remote monitoring can anticipate clinical deterioration and improve care. The aim of this study was to describe the ASL Nuoro's integrated model for proactive HF management via telemonitoring.

Methods: A prospective observational study enrolled HF patients stratified by the 3C-HF score and monitored using non-invasive home devices linked to the CARE MAP platform.

Results: From April 2023 to March 2025, 499 patients were enrolled; 354 were active at follow-up (mean age 80 ± 11 years, 61% men). Therapeutic adherence was high (sodium-glucose cotransporter 2 inhibitors 85%, angiotensin receptor-neprilysin inhibitors 56%). Monthly clinical alerts averaged 1740 (60% clinical relevant), leading to 413 contacts/month. HF hospitalizations dropped by 71% (June-December 2023 vs. 2022).

Conclusions: The integrated model proves to be feasible, effective, and sustainable, even in settings characterized by high organizational complexity. From a social perspective, it ensures equitable access to care and is applicable in geographically disadvantaged areas. The inherent limitations of the 3C-HF score highlight the need for more advanced and dynamic prognostic tools. The intervention also demonstrates economic advantage through the reduction of hospitalizations and the optimization of resource utilization.

This review article describes two controversial aspects of secondary tricuspid regurgitation: the still poorly understood anatomy of the tricuspid valve and the advantages and limitations of color Doppler echocardiography in evaluating tricuspid regurgitation. The tricuspid apparatus is characterized by a complex anatomical structure that includes a saddle-shaped ring, three leaflets (or more), and a subvalvular apparatus. An in-depth knowledge of the anatomy is essential for understanding the various pathophysiological mechanisms of secondary (atriogenic and ventriculogenic) tricuspid regurgitation. Two-dimensional and three-dimensional echocardiography and color Doppler analysis play a key role in correct morphological and functional evaluation and in defining the etiology and extent of tricuspid regurgitation. A multiparametric approach that considers qualitative, semiquantitative, and quantitative parameters is recommended.

Atrial secondary tricuspid regurgitation (A-STR) is a complex and increasingly recognized form of valvular heart disease that arises primarily due to right atrial and tricuspid annular dilation in the absence of intrinsic leaflet pathology. Unlike ventricular secondary tricuspid regurgitation, which is driven by right ventricular remodeling, A-STR is predominantly associated with atrial fibrillation, heart failure with preserved ejection fraction, and other conditions that lead to chronic right atrial remodeling. This condition has been underappreciated despite its significant prevalence and impact on patient morbidity and mortality. Echocardiography is the primary diagnostic tool for diagnosing and assessing patients with A-STR. The natural history of A-STR is unfavorable, with potential worsening over time, particularly if the underlying conditions are not properly treated. Treatment options include cardioversion of atrial fibrillation and medical treatment of heart failure with preserved ejection fraction, which may promote reverse remodeling of the right heart structures and reduce STR severity in some cases. Surgical tricuspid valve annuloplasty remains the gold standard for severe cases, but transcatheter interventions are emerging as potential alternatives. This review provides a comprehensive overview of A-STR, encompassing its epidemiology, pathophysiology, diagnostic approaches, and treatment strategies. By synthesizing current evidence and highlighting gaps in knowledge, this paper aims to guide clinicians in the management of this challenging condition and to inspire future research.

Artificial intelligence (AI) is redefining ECG interpretation, transforming it from a static diagnostic tool into a dynamic, predictive, and integrative instrument. Although widespread, traditional rule-based ECG analysis has limitations in accuracy and adaptability, especially in complex clinical settings. In contrast, AI-driven models, particularly those employing machine learning and deep learning architectures, have demonstrated improved diagnostic performance across a broad spectrum of cardiovascular diseases, including atrial fibrillation, acute myocardial infarction, hypertrophic cardiomyopathy, and valvular heart disease. Notably, AI-ECG is now able to detect subclinical ventricular dysfunction, stratify long-term risk, and anticipate major adverse events before overt clinical manifestations occur. In addition to diagnosis, AI-ECG is emerging as a decision support tool in scenarios characterized by diagnostic uncertainty, such as syncope and cardio-oncology, and may significantly optimize triage and resource allocation. Multiparametric approaches further extend its utility, enabling simultaneous prediction of structural, functional, and electrical cardiac parameters. Wearable devices integrated with AI improve continuous monitoring and may decentralize arrhythmia detection and sudden cardiac death prevention. Despite these advances, critical challenges remain. Poorly explainable AI models, algorithmic bias, overfitting, data governance, and regulatory uncertainty demand rigorous methodological scrutiny. In this framework, federated learning architectures may enable continuous multicenter model refinement and enhance methodological robustness while safeguarding data privacy. The European AI Act and methodological checklists promoted by scientific societies offer a framework to address these issues, fostering transparency, equity, and clinical validity. If validated and implemented responsibly, AI-enhanced ECG has the potential to enhance - not replace - clinical reasoning, advancing a precision medicine paradigm based on both technological innovation and human expertise.

Cardiovascular diseases continue to be the leading cause of death worldwide, with ischemic heart disease remaining the primary cause among cardiovascular conditions; as is well known, it is closely linked to LDL cholesterol levels, now identified as the "cause" of atherosclerotic plaque development and not just a risk factor. The recent introduction of increasingly powerful lipid-lowering drugs makes it more realistic today to achieve the desired LDL targets, and the evolution of therapeutic strategies allows for more personalized care by tailoring treatments to the specific profile of each patient. There are also high expectations for therapies, currently under evaluation, aimed at other potential targets contributing to atherosclerotic disease, such as lipoprotein(a) and interleukin-6. However, these new and potent therapies also have a significant economic impact, making it essential for scientific societies to thoughtfully consider how to manage resources to ensure equitable care. Likely, the use of digital tools can support a balanced and cost-effective management approach.

Hypertrophic cardiomyopathy is a heterogeneous and complex disease. It is an autosomal dominant genetic disease caused by a missense mutation in one of at least 12 genes that code for cardiac sarcomere proteins. There are various approaches used today for the treatment of this pathology, but the "gold standard" remains the surgical treatment of septal myectomy according to Morrow, in which the hypertrophic septum is surgically excised. However, further less invasive approaches have been proposed, such as alcohol septal ablation and edge-to-edge repair of the systolic anterior motion of the mitral valve in patients who are not candidates for surgery, but the superiority of surgery in resolution of left ventricular outflow tract obstruction and its recurrence has been widely demonstrated. Moreover, the surgical approach is correlated with low mortality and morbidity rates in expert centers for the treatment of this pathology.

The ISCHEMIA study enrolled patients with chronic coronary syndrome, moderate-to-severe myocardial ischemia, and critical coronary stenosis, demonstrating that coronary revascularization is not superior to optimized medical therapy in a population with LDL cholesterol <70 mg/dl in both treatment groups. Despite its bias, ISCHEMIA was able to demonstrate the limitations of a strategy based on coronary revascularization in this clinical setting. Five years after its publication, after even studies based on coronary anatomy have failed to identify a population that benefits from an interventional strategy, coronary revascularization remains the first choice in unstable or worsening angina, in individuals with left main stem disease, complex three-vessel disease, and two-vessel disease with involvement of the proximal anterior interventricular branch or in subjects in whom the objective is to control angina symptoms. In all other cases, medical therapy aimed at achieving lesion stabilization should be preferred. The time has come to promote a critical reflection and question the still dominant practice of performing coronary revascularization directly during coronary angiography based solely on anatomical considerations.

Integrating artificial intelligence (AI) into cardiovascular clinical trials is emerging as a key factor in streamlining patient selection, data collection, endpoint monitoring, and outcome analysis. On the one hand, machine learning and deep learning algorithms facilitate the management and review of ever-increasing volumes of clinical, imaging, and telemonitoring data, identifying predictive patterns and automating repetitive tasks. On the other hand, the high cost and long duration of traditional trials, coupled with the need for adequate population diversity, underscore the urgency of re-engineering trial design. AI can contribute to more adaptive study protocols, minimize interobserver variability, and improve endpoint accuracy. However, technical and ethical challenges remain, including algorithmic bias, privacy, model interpretability, and legal accountability for errors. Looking ahead, the introduction of digital biomarkers, synthetic control arms, and increasingly decentralized trials may redefine experimental paradigms and make cardiovascular trials faster, more inclusive, and more targeted. The aim of this review is to describe the use of AI in randomized controlled trials in cardiology.