December 7, 2024Trends in Cardiovascular Medicine15 citationsOpen Access

Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation

Q: What is the clinical evidence from this study?

Study design: Review. Population: Atrial Fibrillation and Stroke. Intervention: Artificial Intelligence and Machine Learning.

SOSandra Ortega‐MartorellCross-Cutting Cardiology IOIván OlierCross-Cutting Cardiology MOMattias OhlssonLund University

Key Result

Artificial intelligence and machine learning approaches have the potential to improve personalized risk prediction, treatment, and rehabilitation outcomes for patients with atrial fibrillation and stroke.

Structured PICO

Population

Patients with atrial fibrillation and stroke

Intervention

Artificial intelligence (AI) and machine learning (ML) approaches, including digital twins

Comparator

Current standard of care using static risk assessment tools (e.g., CHA2DS2-VASc) and conventional rehabilitation

Outcome

Improvement in risk prediction, treatment personalization, and rehabilitation outcomes

AI and machine learning offer promising avenues to transition from static risk assessment to dynamic, personalized care pathways in atrial fibrillation and stroke management.

Limitations

Many AI studies rely on private datasets, posing significant challenges for reproducibility.
Rehabilitation studies have been conducted on very small sample sizes, raising concerns about generalizability.
Implementation of digital twins in healthcare faces several challenges that limit their adoption and integration into clinical practice.
Many AI-based decision support tools are proprietary and not personalized.

Abstract

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.

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