Cerebrovascular accident is the most ominous complication observed after cardiac surgery, carrying an increased risk of morbidity and mortality. Analysis of the problem shows its multidimensional nature. In this study, we aimed to identify major determinants among classic variables, either demographic, clinical or type of surgical procedure, based on the analysis of a large dataset of 580,117 patients from the UK National Adult Cardiac Surgical Audit (NACSA). For this purpose, univariate and multivariate logistic regression models were utilized to determine associations between predictors and dependent variable (Stroke after cardiac surgery). Odds ratios (ORs) and 95% confidence intervals (CIs) were constructed for each independent variable. Statistical analysis allows us to confirm with greater certainty the predictive value of some variables such as age, gender, diabetes mellitus (diabetes treated with insulin OR = 1.37, 95%CI = 1.23–1.53), and systemic arterial hypertension (OR = 1.11, 95%CI = 1.05–1.16);, to emphasize the role of preoperative atrial fibrillation (OR = 1.10, 95%CI = 1.03–1.16) extracardiac arteriopathy (OR = 1.70, 95%CI = 1.58–1.82), and previous cerebral vascular accident (OR 1.71, 95%CI = 1.6–1.9), and to reappraise others like smoking status (crude OR = 1.00, 95%CI = 0.93–1.07 for current smokers) or BMI (OR = 0.98, 95%CI = 0.97–0.98). This could allow for better preoperative risk stratification. In addition, identifying those surgical procedures (for example thoracic aortic surgery associated with a crude OR of 3.72 and 95%CI = 3.53–3.93) burdened by a high risk of neurological complications may help broaden the field of preventive and protective techniques.

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.