AI-based derivation of atrial fibrillation phenotypes in the general and critical care populations.

Journal: EBioMedicine
PMID: 39153412

Abstract

BACKGROUND: Atrial fibrillation (AF) is the most common heart arrhythmia worldwide and is linked to a higher risk of mortality and morbidity. To predict AF and AF-related complications, clinical risk scores are commonly employed, but their predictive accuracy is generally limited, given the inherent complexity and heterogeneity of patients with AF. By classifying different presentations of AF into coherent and manageable clinical phenotypes, the development of tailored prevention and treatment strategies can be facilitated. In this study, we propose an artificial intelligence (AI)-based methodology to derive meaningful clinical phenotypes of AF in the general and critical care populations.

Authors

  • Ryan A A Bellfield
    Data Science Research Centre, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK; Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
  • Ivan Olier
    1Manchester Metropolitan University, Manchester, UK.
  • Robyn Lotto
    Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK; School of Nursing and Advanced Practice, Liverpool John Moores University, Liverpool L2 2ER, UK.
  • Ian Jones
    Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK; School of Nursing and Advanced Practice, Liverpool John Moores University, Liverpool L2 2ER, UK.
  • Ellen A Dawson
    Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK; Research Institute for Sport and Exercise Science, Liverpool John Moores University, Liverpool L3 3AF, UK.
  • Guowei Li
    Center for Clinical Epidemiology and Methodology, Guangdong Second Provincial General Hospital, Guangzhou, China.
  • Anil M Tuladhar
    Department of Neurology, Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands.
  • Gregory Y H Lip
    Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, L69 3BX Liverpool, UK.
  • Sandra Ortega-Martorell
    School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.

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