The recent pandemic launched an acceleration in adopting telemedicine by cardiovascular health and triggered the flourishing of technological advancements, such as the metaverse, which is a novel interactive mix of digital worlds that leverages augmented reality with virtual reality. The CardioVerse represents a theoretical term for the embracement of the metaverse by cardiovascular medicine, encompassing the endless possibilities as well as the challenges that it holds and introduces new dimensions to disease education, prevention and diagnosis. Its applications are numerous, notably in enhancing medical visits, assisting cardiovascular interventions and reshaping the way medical education is provided. Although obstacles are expected in diverse domains such as security, technical, legislative and regulatory, the utilization of non-fungible tokens as a security asset for patient data appears as potential solution.

Chat Generative Pre-trained Transformer (ChatGPT) is currently a trending topic worldwide triggering extensive debate about its predictive power, its potential uses, and its wider implications. Recent publications have demonstrated that ChatGPT can correctly answer questions from undergraduate exams such as the United States Medical Licensing Examination. We challenged it to answer questions from a more demanding, post-graduate exam—the European Exam in Core Cardiology (EECC), the final exam for the completion of specialty training in Cardiology in many countries. Our results demonstrate that ChatGPT succeeds in the EECC.

Background: Although disopyramide has been widely used to reduce left ventricular outflow obstruction (LVOTO) and to improve symptoms in patients with obstructive hypertrophic cardiomyopathy (oHCM), its use in real world as well as patient characteristics associated with a positive treatment response are still unclear. Methods: From 1980 to 2021, 1527 patients with HCM were evaluated and 372 (23%) had a LVOTO with active follow-up. The efficacy and safety of disopyramide were assessed systematically during 12 months (2-, 6-, and 12-month visits). Responders were patients with a final NYHA = I and a LVOTO < 30 mmHg; incomplete responders were those patients with NYHA > I and a LVOTO < 30 mmHg; and non-responders were symptomatic patients with no change in functional class NYHA and a LVOT gradient > 30 mmHg. Results: Two-hundred-fifty-four (66%) patients were in functional class NYHA I/II and 118 (34%) in NYHA III/IV. A total of 118/372 (32%, 55 ± 16 years) underwent disopyramide therapy. Twenty-eight (24%) patients responded to therapy, 39 (33%) were incomplete responders, and 51 (43%) did not respond. Responder were mainly patients in functional NYHA class I/II (24/28, 86%), whereas incomplete responders and non-responders were more often in functional NYHA class III/IV (50/54 (93%)). An independent predictor of response to disopyramide treatment was the presence of NYHA I/II at the initiation of therapy (HR 1.5 (95% CI 1.1–4.5), p = 0.03). No major life-threatening arrhythmic events or syncope occurred, despite 19 (16%) patients showing reduced QTc from baseline, 19 (16%) having no difference, while 80 (69%) patients had prolonged QTc interval. Thirty-one (26%) patients experienced side effects, in particular, 29 of the anticholinergic type. Conclusions: Disopyramide was underused in oHCM but effective in reducing LVOTO gradients and symptoms in slightly symptomatic patients with less severe disease phenotype with a safe pro-arrhythmic profile.

Background: Hypertension remains the leading global risk factor for cardiovascular morbidity and mortality, with suboptimal control rates despite guideline-directed therapies. Digital health and artificial intelligence (AI) technologies offer novel approaches for improving diagnosis, monitoring, and individualized treatment of hypertension. Objectives: To critically review the current landscape of AI-enabled digital tools for hypertension management, including emerging applications, implementation challenges, and future directions. Methods: A narrative review of recent PubMed-indexed studies (2019–2024) was conducted, focusing on clinical applications of AI and digital health technologies in hypertension. Emphasis was placed on real-world deployment, algorithmic explainability, digital biomarkers, and ethical/regulatory frameworks. Priority was given to high-quality randomized trials, systematic reviews, and expert consensus statements. Results: AI-supported platforms—including remote blood pressure monitoring, machine learning titration algorithms, and digital twins—have demonstrated early promise in improving hypertension control. Explainable AI (XAI) is critical for clinician trust and integration into decision-making. Equity-focused design and regulatory oversight are essential to prevent exacerbation of health disparities. Emerging implementation strategies, such as federated learning and co-design frameworks, may enhance scalability and generalizability across diverse care settings. Conclusions: AI-guided titration and digital twin approaches appear most promising for reducing therapeutic inertia, whereas cuffless blood pressure monitoring remains the least mature. Future work should prioritize pragmatic trials with equity and cost-effectiveness endpoints, supported by safeguards against bias, accountability gaps, and privacy risks.

Background and Aims: The European Society of Cardiology guidelines recommend a systematic search for diagnostic clues or ‘red flags’ (RFs) in patients with hypertrophic cardiomyopathy (HCM) to better tailor disease management. To date, the prevalence and clinical significance of RF associated with HCM phenotypes in different clinical settings are unknown. Methods: The study cohort comprised 818 patients with a clinical diagnosis of HCM [479 (62%) males, mean age 49 ± 21 years] referred to four European centres. Pre-specified RFs were categorized into one of five domains: (i) family history; (ii) physical examination; (iii) electrocardiography; (iv) echocardiography; and (v) laboratory. Results: A total of 318 (39%) patients had a pathogenic or likely pathogenic sarcomere gene variant; 240 (29%) a TTR and GLA variant; 154 (19%) a syndromic cause; and 106 (13%) no identifiable cause. In the overall cohort, 2979 RFs were identified. Of these, 1018 (34%) were identifiable from clinical history and examination alone (generalist setting) and 1961 (66%) by 12-lead electrocardiogram and echocardiography (cardiologist’s office). Non-sarcomeric patients were diagnosed more often aged <20 and after 60 years (476/500, 95%). Syndromic diseases such as Rasopathies, inherited metabolic disorders, and mitochondrial disorders were diagnosed in neonatal/early childhood (mean age 3 ± 2 years), whereas patients with Danon disease, Friedreich’s ataxia, Noonan syndrome, and PRKAG2 cardiomyopathy were identified mostly during adolescence (mean age 16 ± 8 years). Non-cardiac RFs identified by clinical history, physical examination, and routine laboratory testing were most frequent in patients with HCM caused by Rasopathies, inherited metabolic disorders, and mitochondrial disorders (48%, 47%, and 57%, respectively). Physical RFs were almost exclusively observed in patients with a definite non-sarcomeric aetiology [348/350 (99%)]. On the contrary, most RFs associated with geno-positive and geno-negative HCM were derived from electrocardiogram and echocardiography [692/990 (70%) and 332/375 (88%), respectively]. Conclusions: Red flags are a common finding in patients presenting with HCM, most commonly non-cardiac in non-sarcomeric aetiology and cardiac in sarcomeric HCM. Over 34% of RF, most relevant to rare HCM phenocopies, can be potentially detected in a generalist setting. Investing in high-touch-low tech, widespread awareness for HCM-related RF may provide substantial advantage in terms of diagnostic yield and appropriate use of resources.