| Abstract [eng] |
The aim of this narrative review is to enhance awareness of the revised ICD-11 classification of supraventricular arrhythmias, synthesise contemporary evidence-based research, and highlight existing knowledge gaps for clinicians, researchers, and healthcare decision-makers by integrating the ICD-11 classification (BC80/BC81) with the evidence-based framework of the 2019 and 2024 ESC guidelines, with particular emphasis on diagnostic reasoning and acute-care relevance. The objectives were to outline the ICD-11-based nosological structure of SVAs and to systematise the current knowledge on the mechanisms and pathophysiology of SVAs; to summarise the current data on epidemiology of SVAs relevant for acute heart disease; to review general diagnostic principles of SVAs relevant for acute heart disease; to outline the distinguishing clinical features of SVAs relevant for acute heart disease; to discuss treatment strategies for SVAs relevant for acute heart disease; and to identify future directions for SVA research relevant for acute clinical settings. The research methods comprised a narrative synthesis of current ESC guideline recommendations and contemporary scientific concepts related to SVA classification, pathophysiology, diagnostics, and acute clinical management. The particular emphasis has been made on integrating the ICD-11 framework with ESC guideline-based approaches. The results demonstrated that transitioning from traditional clinical nomenclature to a more rigorous ICD-11-driven nosological structure facilitates clearer differentiation between atrial fibrillation and non-AF tachyarrhythmias. Such differentiation improve coherence across epidemiological and diagnostic domains, and further confirms that atrial arrhythmogenesis may be characterized as a continuum in which acute heart disease frequently serves as a physiological trigger unmasking underlying substrate vulnerabilities. While the 12-lead ECG remains the foundational diagnostic tool for identifying characteristic electrophysiological features the review also identified a clinically relevant shift toward AI-augmented analysis and next-generation monitoring. The latter may enable increasingly accurate non-invasive prediction of tachycardia mechanisms and contribute to improved diagnostic efficiency and triage in acute settings. The findings of the review confirm that a classification-driven and diagnostically rigorous approach prioritizing early rhythm recognition and mechanistic clarity provides a robust foundation for managing the hemodynamic and thromboembolic risks in acute cardiovascular presentations and supports a more coherent integration of guideline-based strategies into daily clinical practice. |
