Systems-based perspectives on atrial fibrillation: integrating classification and severity

Atrial fibrillation (AF) is the most common persistent arrhythmia encountered in clinical practice and it remains a major contributor to stroke, heart failure, cognitive impairment, and early mortality. Patients rarely follow a single pattern of disease evolution; instead, the condition develops through several interrelated mechanisms, including atrial remodelling, electrical instability, inflammatory activity, and metabolic or systemic comorbidities. This narrative review synthesises evidence published between January 2018 and July 2025 to evaluate current classification systems and approaches to AF severity assessment. Time-based descriptors continue to guide management, yet they do not fully capture early atrial remodelling or the gradual transition toward more persistent forms. Proposed staging models address this issue by incorporating subclinical features. Symptom scores and thromboembolic or bleeding risk indices remain central tools in practice, but they provide only a partial view of AF burden. Evidence suggests that factors such as obesity, sleep-disordered breathing, thyroid and renal dysfunction, and lifestyle exposures shape AF onset and long-term outcomes. Developments in computational modelling, ambulatory monitoring, and machine learning have begun to influence detection and risk stratification. Collectively, these observations support viewing AF as a multisystem condition and highlight the value of frameworks that reflect both cardiac mechanisms and systemic contributors.