Ortega-Martorell Sandra, Olier Ivan, Ohlsson Mattias, Lip Gregory Y H
Data Science Research Centre, Liverpool John Moores University, Liverpool L3 3AF, UK; Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
Data Science Research Centre, Liverpool John Moores University, Liverpool L3 3AF, UK; Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
Trends Cardiovasc Med. 2025 May;35(4):205-211. doi: 10.1016/j.tcm.2024.12.003. Epub 2024 Dec 7.
Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological disorders and is the most common heart arrhythmia worldwide, affecting 2 % of the European population. This prevalence increases with age, imposing significant financial, economic, and human burdens. In Europe, stroke is the second leading cause of death and the primary cause of disability, with numbers expected to rise due to ageing and improved survival rates. Functional recovery from AF-related stroke is often unsatisfactory, leading to prolonged hospital stays, severe disability, and high mortality. Despite advances in AF and stroke research, the full pathophysiological and management issues between AF and stroke increasingly need innovative approaches such as artificial intelligence (AI) and machine learning (ML). Current risk assessment tools focus on static risk factors, neglecting the dynamic nature of risk influenced by acute illness, ageing, and comorbidities. Incorporating biomarkers and automated ECG analysis could enhance pathophysiological understanding. This paper highlights the need for personalised, integrative approaches in AF and stroke management, emphasising the potential of AI and ML to improve risk prediction, treatment personalisation, and rehabilitation outcomes. Further research is essential to optimise care and reduce the burden of AF and stroke on patients and healthcare systems.
心房颤动（AF）是一种由多种潜在病理生理紊乱引起的复杂病症，是全球最常见的心律失常，影响着2%的欧洲人口。这种患病率随年龄增长而增加，带来了巨大的财政、经济和人力负担。在欧洲，中风是第二大死因和残疾的主要原因，由于老龄化和生存率的提高，预计这一数字还会上升。房颤相关性中风后的功能恢复往往不尽人意，导致住院时间延长、严重残疾和高死亡率。尽管在房颤和中风研究方面取得了进展，但房颤和中风之间完整的病理生理和管理问题越来越需要人工智能（AI）和机器学习（ML）等创新方法。目前的风险评估工具侧重于静态风险因素，忽略了受急性疾病、老龄化和合并症影响的风险的动态性质。纳入生物标志物和自动心电图分析可以增强对病理生理的理解。本文强调了房颤和中风管理中个性化、综合方法的必要性，强调了人工智能和机器学习在改善风险预测、治疗个性化和康复结果方面的潜力。进一步的研究对于优化护理以及减轻房颤和中风对患者和医疗系统的负担至关重要。
