Sarcomeric hypertrophic cardiomyopathy (HCM) is a prevalent genetic disorder characterised by left ventricular hypertrophy, myocardial disarray, and an increased risk of heart failure and sudden cardiac death. Despite advances in understanding its pathophysiology, treatment options for HCM remain limited. This narrative review aims to provide a comprehensive overview of current clinical practice and explore emerging therapeutic strategies for sarcomeric HCM, with a focus on cardiac myosin inhibitors. We first discuss the conventional management of HCM, including lifestyle modifications, pharmacological therapies, and invasive interventions, emphasizing their limitations and challenges. Next, we highlight recent advances in molecular genetics and their potential applications in refining HCM diagnosis, risk stratification, and treatment. We delve into emerging therapies, such as gene editing, RNA-based therapies, targeted small molecules, and cardiac myosin modulators like mavacamten and aficamten, which hold promise in modulating the underlying molecular mechanisms of HCM. Mavacamten and aficamten, selective modulators of cardiac myosin, have demonstrated encouraging results in clinical trials by reducing left ventricular outflow tract obstruction and improving symptoms in patients with obstructive HCM. We discuss their mechanisms of action, clinical trial outcomes, and potential implications for the future of HCM management. Furthermore, we examine the role of precision medicine in HCM management, exploring how individualised treatment strategies, including exercise prescription as part of the management plan, may optimise patient outcomes. Finally, we underscore the importance of multidisciplinary care and patient-centred approaches to address the complex needs of HCM patients. This review also aims to encourage further research and collaboration in the field of HCM, promoting the development of novel and more effective therapeutic strategies, such as cardiac myosin modulators, to hopefully improve the quality of life and outcome of patients with sarcomeric HCM.

Introduction: Thrombophilia is a condition that predisposes to a higher incidence of venous thromboembolisms (VTE), some also in atypical sites. Direct oral anticoagulants (DOACs) have proven to be effective in the treatment of deep vein thrombosis (DVT). However, their use can be sometimes challenging in particular settings of patients such as those with major thrombophilia - antithrombin, protein C and protein S deficiency, homozygous mutation of Factor V Leiden, homozygous mutation of Factor II G20210A, combined heterozygous mutation of factor V Leiden and Factor II G20210A – carrying a high thrombotic risk. Patients and methods: At our Center, 45 patients with major thrombophilia were treated with DOACs: 33 after an initial treatment with vitamin K antagonists (VKA) and 12 as first line therapy for VTE. The median follow-up of DOACs treatment was 29 months. Conclusions: No patient presented hemorrhagic or thrombotic complications during DOAC therapy. DOACs have proven to be effective and safe in this real-life series of patients with major thrombophilia.

Antithrombotic therapies (ATT) play a pivotal role in the management of cardiovascular diseases, aiming to prevent ischemic events while maintaining a delicate balance with the patient’s bleeding risk. Typically, ATT can be classified into antiplatelet and anticoagulant therapies. Their application spans a broad spectrum of cardiovascular conditions, ranging from ischemic heart disease to atrial fibrillation, encompassing venous thromboembolisms and innovative structural interventional cardiology procedures. The global burden of cardiovascular diseases is steadily increasing, often giving rise to overlapping clinical presentations. Accordingly, the adoption of combined pharmacological approaches becomes imperative, potentially disrupting the delicate equilibrium between ischemic and bleeding risk, thus leading to nuanced pharmacotherapeutic pathways. In this context, contemporary investigations strive to identify a convergence point that optimizes the duration of medical therapy while addressing the need for antithrombotic effects, especially in the context of ischemic heart disease. This review aims to comprehensively revisit the main antithrombotic strategies in cardiovascular diseases, with the intention of enhancing a systematic approach which is key for the effective clinical management of these patients. Also, the review will examine the most impactful studies that have established the groundwork for current scientific evidence, with acknowledgement of special populations. Finally, we will cast a gaze into the future of this dynamic and evolving research field, exploring forthcoming perspectives and advancements.

Mavacamten is a selective, allosteric, and reversible cardiac myosin inhibitor, representing the first disease-specific treatment for obstructive hypertrophic cardiomyopathy (HCM) that targets the core pathophysiological mechanism of this condition. Clinical evidence supports its efficacy in improving symptoms, cardiac function and remodeling, thereby supplementing established treatment regimes. However, mavacamten is extensively metabolized by hepatic cytochromes and its half-life is contingent upon CYP2C19 phenotype. Consequently, co-administered medications that inhibit or induce these enzymes may significantly alter mavacamten pharmacokinetics, potentially leading to reversible systolic dysfunction or diminished therapeutic efficacy. This paper provides a comprehensive analysis of mavacamten pharmacokinetics and its potential interactions with antithrombotic and antiarrhythmic agents, which are the cornerstones of atrial fibrillation management in HCM population. Our aim is to offer clinicians practical guidance on safely administering mavacamten in conjunction with these medications, discuss the role of pharmacogenomics and outline rigorous patient safety monitoring strategies to ensure effective and individualized treatment.

Background: Artificial intelligence (AI) has shown promise in the interpretation of electrocardiograms (ECGs) using signal-based deep learning models. In parallel, large language models (LLMs) have gained increasing visibility in clinical practice, including exploratory applications in ECG analysis. Whether a general-purpose LLM can meaningfully discriminate cardiovascular disease from athlete ECGs during PPS remains unknown. We aimed to evaluate the diagnostic performance of a general-purpose LLM for this task. Methods: In this multicentre diagnostic accuracy study, we evaluated a commercially available LLM (ChatGPT, version 5) in 2950 competitive athletes undergoing PPS. All athletes underwent resting 12-lead ECG, with second- and third-line investigations performed when clinically indicated. The reference outcome was confirmed cardiovascular disease after full diagnostic work-up (n = 450, 15.3%). For each ECG, the LLM generated a numeric score (0–100) representing the inferred likelihood of underlying disease using a standardized prompt and without task-specific fine-tuning. Discriminative performance was assessed using receiver operating characteristic (ROC) analysis. Misclassification patterns were analysed according to International ECG Criteria. Results: GPT-derived scores demonstrated a marked floor effect, with a median value of 0 (IQR 0–2) in both diseased and non-diseased athletes and substantial overlap between groups. The area under the ROC curve was 0.52 (95% CI 0.49–0.55), indicating performance close to random classification. At the Youden-derived threshold, 79% of athletes with confirmed disease were incorrectly classified as negative. False-negative cases were predominantly characterized by borderline ECG patterns (82%), and a substantial number of red-flag ECG abnormalities were also missed. Conclusions: In this PPS cohort, a general-purpose LLM used in a naïve configuration showed no clinically meaningful ability to discriminate between cardiovascular disease and athlete ECGs. Without task-specific training or domain adaptation, such models should not be used for diagnostic triage in athlete screening.