Ricci, Fabrizio

The advent of high-throughput sequencing technologies has revolutionized cardiovascular genetics, generating vast amounts of data. A major challenge in the genomic era is the efficient and accurate identification of causative genetic variants associated with cardiovascular disease. Artificial intelligence (AI)-driven computational approaches offer a powerful solution by enabling the automation of variant classification, improving consistency and reproducibility, and enhancing predictive accuracy. These methods may guide not only variant classification, variant effect prediction, and clinical prioritization, but also open the door to data-driven precision medicine, enabling more accurate and individualized diagnoses and targeted therapeutic strategies tailored to each patient’s unique genetic and clinical profile. This state-of-the-art review provides a structured overview of AI methodologies applied to cardiovascular genomics, beginning with rule-based expert systems that encode standardized guidelines for consistent variant interpretation. Next, we examine machine learning approaches capable of identifying complex patterns in annotated multimodal clinical and multi-omic datasets. The role of deep learning algorithms is highlighted for their ability to extract features from high-dimensional, unstructured data relevant to cardiovascular disease. In addition, the potential of generative AI is explored, including applications in synthetic data generation, variant impact prediction, and automated summarization of biomedical literature. Despite advances, several challenges remain, including data heterogeneity, the need for explainable AI models to elucidate the decision mechanisms, and the complexity linked to the integration of AI-based tools into clinical workflows. Addressing these issues requires interdisciplinary collaboration among clinicians, geneticists, data scientists, and bioinformaticians to ensure the effective translation of AI-generated insights into clinical practice. This review aims to provide a comprehensive perspective on the evolving role of AI in cardiovascular genomics and its implications for advancing precision medicine.

Arrhythmogenic cardiomyopathies (ACMs) are a phenotypically and etiologically heterogeneous group of myocardial disorders characterized by fibrotic or fibro-fatty replacement of ventricular myocardium, electrical instability, and an elevated risk of sudden cardiac death. Initially identified as a right ventricular disease, ACMs are now recognized to include biventricular and left-dominant forms. Genetic causes account for a substantial proportion of cases and include desmosomal variants, non-desmosomal variants, and familial gene-elusive forms with no identifiable pathogenic mutation. Nongenetic etiologies, including post-inflammatory, autoimmune, and infiltrative mechanisms, may mimic the phenotype. In many patients, the disease remains idiopathic despite comprehensive evaluation. Cardiac magnetic resonance imaging has emerged as a key tool for identifying non-ischemic scar patterns and for distinguishing arrhythmogenic phenotypes from other cardiomyopathies. Emerging classifications propose the unifying concept of scarring cardiomyopathies based on shared structural substrates, although global consensus is evolving. Risk stratification remains challenging, particularly in patients without overt systolic dysfunction or identifiable genetic markers. Advances in tissue phenotyping, multi-omics, and artificial intelligence hold promise for improved prognostic assessment and individualized therapy.

Abstract Aims Syncope remains a diagnostic challenge despite advancements in testing and treatment. Cardiac syncope is an independent predictor of mortality and can be difficult to distinguish from other causes of transient loss of consciousness (TLOC). This paper explores whether artificial intelligence (AI) can improve the evaluation and management of patients with syncope. Methods and results We conducted a literature review and incorporated the opinions of experts in the fields of syncope and AI. The cause of TLOC is often unclear, hospitalization criteria are ambiguous, diagnostic tests are frequently non-informative, and assessments are costly. Patients are left with unanswered questions and limited guidance. Artificial intelligence (AI) has the potential to optimize syncope evaluation by processing large data sets, detecting imperceptible patterns, and assisting clinicians. However, AI has limitations, including errors, lack of human empathy, and uncertain clinical utility. Liability issues further complicate its integration. We present three viewpoints: (i) AI is crucial for advancing syncope management; (ii) AI can enhance the patient experience; and (iii) AI in syncope care is inevitable. Conclusion Artificial intelligence may improve syncope diagnosis and management, particularly through machine learning–based test interpretation and wearable device data. However, it has yet to surpass human clinical judgment in complex decision-making. Current challenges include gaps in understanding syncope mechanisms, AI interpretability, generalizability, and clinical integration. Standardized diagnostic approaches, real-world validation, and curated data sets are essential for progress. Artificial intelligence may enhance efficiency and communication but raises concerns regarding confidentiality, bias, inequities, and legal implications.

Abstract Dilated cardiomyopathy (DCM) is a clinical diagnosis characterized by the presence of left ventricular dilatation and systolic dysfunction unexplained by abnormal loading conditions or coronary artery disease. However, a broad range of phenotypic manifestations, encompassing isolated scar, DCM with preserved ejection fraction, and overt DCM, should be regarded as a diagnostic classification representing a broad spectrum of underlying aetiologies, including both inherited and acquired heart muscle disorders. A multimodal non-invasive imaging approach is essential for accurate morpho-functional assessment of cardiac chambers and is key to establish the cardiac phenotype and to rule out an underlying ischaemic aetiology. Furthermore, advanced imaging techniques enable deep cardiovascular phenotyping and non-invasive tissue characterization. The aim of this review is to propose a systematic approach to the diagnosis of DCM, emphasizing the importance of genetics and clinical findings for a precise and practical clinical approach. Also, we strive to qualify the role of cardiac imaging in the diagnosis of DCM, particularly on the relevance of novel techniques and clinical utility of actionable parameters to improve current diagnostic schemes and risk stratification algorithms. We further elaborate on the role of cardiac imaging to deliver optimal guidance to aetiology-based therapeutic approaches, verification of treatment response and disease progression monitoring.

Abstract Dilated cardiomyopathy (DCM) is extensively discussed in numerous expert consensus documents and international guidelines, with differing recommendations. To support clinicians in daily practice and decision-making, we conducted a systematic review of key guidelines and recommendations concerning the diagnosis and clinical management of DCM. Our research encompassed MEDLINE and EMBASE databases for relevant articles published, as well as the websites of relevant scientific societies. We identified two guidelines and one scientific statement that met stringent criteria, thereby qualifying them for detailed systematic analysis. Our review revealed consensus on several key aspects: the definition of DCM, the use of B-type natriuretic peptides and high-sensitivity troponin in laboratory testing, the essential role of multimodality cardiovascular imaging for initial diagnosis, genetic counselling, and the management of advanced disease. Nonetheless, notable areas of variation included the formation of multidisciplinary management teams, the role of cascade genetic testing, pathways for arrhythmic risk stratification, and the criteria for prophylactic defibrillator implantation. Significant evidence gaps persist, particularly regarding the clinical trajectory of genetic, non-genetic and gene-elusive forms of DCM, the use of cardiovascular magnetic resonance in phenotype-negative family members with genotype-positive probands, and the development of potential aetiology-oriented therapies. Addressing these gaps could enhance clinical outcomes and inform future research directions and guideline development.

Sinus tachycardia (ST) is ubiquitous, but its presence outside of normal physiological triggers in otherwise healthy individuals remains a commonly encountered phenomenon in medical practice. In many cases, ST can be readily explained by a current medical condition that precipitates an increase in the sinus rate, but ST at rest without physiological triggers may also represent a spectrum of normal. In other cases, ST may not have an easily explainable cause but may represent serious underlying pathology and can be associated with intolerable symptoms. The classification of ST, consideration of possible etiologies, as well as the decisions of when and how to intervene can be difficult. ST can be classified as secondary to a specific, usually treatable, medical condition (eg, pulmonary embolism, anemia, infection, or hyperthyroidism) or be related to several incompletely defined conditions (eg, inappropriate ST, postural tachycardia syndrome, mast cell disorder, or post-COVID syndrome). While cardiologists and cardiac electrophysiologists often evaluate patients with symptoms associated with persistent or paroxysmal ST, an optimal approach remains uncertain. Due to the many possible conditions associated with ST, and an overlap in medical specialists who see these patients, the inclusion of experts in different fields is essential for a more comprehensive understanding. This article is unique in that it was composed by international experts in Neurology, Psychology, Autonomic Medicine, Allergy and Immunology, Exercise Physiology, Pulmonology and Critical Care Medicine, Endocrinology, Cardiology, and Cardiac Electrophysiology in the hope that it will facilitate a more complete understanding and thereby result in the better care of patients with ST.

Post-acute sequelae of SARS-CoV-2 (PASC), or long COVID syndrome, is emerging as a major health issue in patients with previous SARS-CoV-2 infection. Symptoms commonly experienced by patients include fatigue, palpitations, chest pain, dyspnea, reduced exercise tolerance, and “brain fog”. Additionally, symptoms of orthostatic intolerance and syncope suggest the involvement of the autonomic nervous system. Signs of cardiovascular autonomic dysfunction appear to be common in PASC and are similar to those observed in postural orthostatic tachycardia syndrome and inappropriate sinus tachycardia. In this review, we report on the epidemiology of PASC, discuss current evidence and possible mechanisms underpinning the dysregulation of the autonomic nervous system, and suggest nonpharmacological and pharmacological interventions to treat and relieve symptoms of PASC-associated dysautonomia.