Aims: Long COVID syndrome has had a major impact on million patients’ lives worldwide. The cardiovascular system is an important aspect of this multifaceted disease that may manifest in many ways. We have hereby performed a narrative review in order to identify the extent of the cardiovascular manifestations of the Long COVID syndrome. Methods and results: An in-depth systematic search of the literature has been conducted for this narrative review. The systematic search of PubMed and Cochrane databases yielded 3993 articles, of which 629 underwent full-text screening. A total of 78 studies were included in the final qualitative synthesis and data evaluation. The pathophysiology of the cardiovascular sequelae of Long COVID syndrome and the cardiac manifestations and complications of Long COVID syndrome are critically evaluated. In addition, potential cardiovascular risk factors are assessed, and preventive methods and treatment options are examined in this review. Conclusion: This systematic review poignantly summarizes the evidence from the available literature regarding the cardiovascular manifestations of Long COVID syndrome and reviews potential mechanistic pathways, diagnostic approaches, preventive measures, and treatment options.

Optimal risk assessment for primary prevention remains highly challenging. Recent registries have highlighted major discrepancies between guidelines and daily practice. Although guidelines have improved over time and provide updated risk scores, they still fail to identify a significant proportion of at-risk individuals, who then miss out on effective prevention measures until their initial ischemic events. Cardiovascular imaging is progressively assuming an increasingly pivotal role, playing a crucial part in enhancing the meticulous categorization of individuals according to their risk profiles, thus enabling the customization of precise therapeutic strategies for patients with increased cardiovascular risks. For the most part, the current approach to patients with atherosclerotic cardiovascular disease (ASCVD) is homogeneous. However, data from registries (e.g., REACH, CORONOR) and randomized clinical trials (e.g., COMPASS, FOURIER, and ODYSSEY outcomes) highlight heterogeneity in the risks of recurrent ischemic events, which are especially higher in patients with poly-vascular disease and/or multivessel coronary disease. This indicates the need for a more individualized strategy and further research to improve definitions of individual residual risk, with a view of intensifying treatments in the subgroups with very high residual risk. In this narrative review, we discuss advances in cardiovascular imaging, its current place in the guidelines, the gaps in evidence, and perspectives for primary and secondary prevention to improve risk assessment and therapeutic strategies using cardiovascular imaging.

Coronary artery disease (CAD) is highly prevalent in older adults, yet its management remains challenging. Treatment choices are made complex by the frailty burden of older patients, a high prevalence of comorbidities and body composition abnormalities (e.g., sarcopenia), the complexity of coronary anatomy, and the frequent presence of multivessel disease, as well as the coexistence of major ischemic and bleeding risk factors. Recent randomized clinical trials and epidemiological studies have provided new data on optimal management of complex patients with CAD. However, frail older adults are still underrepresented in the literature. This narrative review aims to highlight the importance of assessing frailty as an aid to guide therapeutic decision-making and tailor CAD management to the specific needs of older adults, taking into account age-related pharmacokinetic and pharmacodynamic changes, polypharmacy, and potential drug interactions. We also discuss gaps in the evidence and offer perspectives on how best in the future to optimize the global strategy of CAD management in older adults.

Chronic inflammation is a pivotal driver in the progression of atherosclerosis, significantly contributing to the burden of cardiovascular disease (CVD). Patients with chronic inflammatory diseases, such as inflammatory bowel diseases (IBDs) (e.g., ulcerative colitis and Crohn’s disease), rheumatological disorders, as well as individuals with auto-immune diseases (such as systemic lupus erythematosus), present a higher risk of major adverse cardiac events (MACEs). Despite their elevated CVD risk, these populations remain underrepresented in cardiovascular research, leading to a critical underestimation of their cardiovascular risk (CVR) in clinical practice. Furthermore, even recent CVR scores poorly predict the risk of events in these specific populations. This narrative review examines the physiopathological mechanisms linking chronic inflammation, immunomodulation, atherosclerosis, thrombosis and cardiovascular events. We review data from epidemiological studies and clinical trials to explore the potential cardiovascular benefits of anti-inflammatory and immunomodulatory therapies. Despite existing evidence, significant gaps in knowledge remain. Future research is mandatory, focusing on innovative strategies for risk stratification and optimization, including lipidomics, proteomics, advanced inflammatory markers, microbiota profiling, and cardiovascular imaging. Addressing these unmet needs will enhance understanding of cardiovascular risk in chronic inflammatory diseases, enabling tailored interventions and better outcomes.

Characterization and management of patients admitted for acute coronary syndromes (ACS) remain challenging, and it is unclear whether currently available clinical and procedural features can suffice to inform adequate decision making. We aimed to explore the presence of specific subsets among patients with ACS. The details on patients discharged after ACS were obtained by querying an extensive multicenter registry and detailing patient features, as well as management details. The clinical outcomes included fatal and nonfatal cardiovascular events at 1-year follow-up. After missing data imputation, 2 unsupervised machine learning approaches (k-means and Clustering Large Applications [CLARA]) were used to generate separate clusters with different features. Bivariate- and multivariable-adjusted analyses were performed to compare the different clusters for clinical outcomes. A total of 23,270 patients were included, with 12,930 cases (56%) of ST-elevation myocardial infarction (STEMI). K-means clustering identified 2 main clusters: a first 1 including 21,998 patients (95%) and a second 1 including 1,282 subjects (5%), with equal distribution for STEMI. CLARA generated 2 main clusters: a first 1 including 11,268 patients (48%) and a second 1 with 12,002 subjects (52%). Notably, the STEMI distribution was significantly different in the CLARA-generated clusters. The clinical outcomes were significantly different across clusters, irrespective of the originating algorithm, including death reinfarction and major bleeding, as well as their composite. In conclusion, unsupervised machine learning can be leveraged to explore the patterns in ACS, potentially highlighting specific patient subsets to improve risk stratification and management.

Background: Artificial Intelligence (AI) is a transformative innovation designed to enable machines to perform tasks typically requiring human intelligence. Among various medical fields, cardiology—and particularly electrophysiology—has seen rapid integration of AI technologies. The ability of AI to analyze large and complex datasets is reshaping diagnostic and therapeutic approaches. Objectives: This review aims to provide a comprehensive overview of AI models and their applications in cardiac electrophysiology. The focus is on understanding how AI contributes to clinical practice through ECG interpretation, arrhythmia detection, atrial mapping, and catheter ablation, while also exploring its limitations and future potential. Methods: The review discusses various AI approaches, including Machine Learning (ML) and Deep Learning (DL), and highlights relevant literature illustrating their implementation in electrophysiological settings. Key clinical applications are examined thematically, with a narrative synthesis of current capabilities, technologies, and outcomes. Results: AI-based tools have demonstrated effectiveness in identifying supraventricular arrhythmias like atrial fibrillation (AF) and atrial flutter (AFL), as well as complex conditions such as ventricular tachycardias (VTs) and long QT syndrome (LQTS). In procedural contexts, AI enhances electro-anatomical mapping, reduces operative time, and supports tailored post-ablation management. Discussion: While AI offers clear advantages in diagnostic accuracy and procedural efficiency, challenges remain regarding data security, ethical transparency, and clinical adoption. Addressing these limitations will be crucial for integrating AI into routine electrophysiology and maximizing its potential in future cardiology practice.