Advancing personalised care in atrial fibrillation and stroke : The potential impact of AI from prevention to rehabilitation
Ortega-Martorell, Sandra LU ; Olier, Ivan ; Ohlsson, Mattias LU
and Lip, Gregory Y.H.
(2024)
In Trends in Cardiovascular Medicine
- 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... (More)
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.
(Less)
- author
- Ortega-Martorell, Sandra
LU
; Olier, Ivan
; Ohlsson, Mattias
LU
and Lip, Gregory Y.H.
- author collaboration
- organization
- publishing date
- 2024
- type
- Contribution to journal
- publication status
- epub
- subject
- keywords
- Artificial Intelligence, Atrial Fibrillation, Burden, Digital Twins, Impact, Machine Learning, Personalised care, Significance, Stroke
- in
- Trends in Cardiovascular Medicine
- publisher
- Elsevier
- external identifiers
-
- pmid:39653093
- scopus:85211967182
- ISSN
- 1050-1738
- DOI
- 10.1016/j.tcm.2024.12.003
- language
- English
- LU publication?
- yes
- id
- b3893531-76d7-48c1-83ba-83c4ecaf74b3
- date added to LUP
- 2025-01-30 11:21:05
- date last changed
- 2025-07-04 11:07:20
@article{b3893531-76d7-48c1-83ba-83c4ecaf74b3,
abstract = {{<p>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.</p>}},
author = {{Ortega-Martorell, Sandra and Olier, Ivan and Ohlsson, Mattias and Lip, Gregory Y.H.}},
issn = {{1050-1738}},
keywords = {{Artificial Intelligence; Atrial Fibrillation; Burden; Digital Twins; Impact; Machine Learning; Personalised care; Significance; Stroke}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Trends in Cardiovascular Medicine}},
title = {{Advancing personalised care in atrial fibrillation and stroke : The potential impact of AI from prevention to rehabilitation}},
url = {{http://dx.doi.org/10.1016/j.tcm.2024.12.003}},
doi = {{10.1016/j.tcm.2024.12.003}},
year = {{2024}},
}