Identification of undiagnosed atrial fibrillation using a machine learning risk-prediction algorithm and diagnostic testing (PULsE-AI) in primary care: a multi-centre randomized controlled trial in England.
Atrial fibrillation
Machine learning
Primary care
Risk prediction
Screening
Journal
European heart journal. Digital health
ISSN: 2634-3916
Titre abrégé: Eur Heart J Digit Health
Pays: England
ID NLM: 101778323
Informations de publication
Date de publication:
Jun 2022
Jun 2022
Historique:
received:
13
01
2022
revised:
24
02
2022
accepted:
22
03
2022
entrez:
30
1
2023
pubmed:
31
1
2023
medline:
31
1
2023
Statut:
epublish
Résumé
The aim of the PULsE-AI trial was to assess the effectiveness of a machine learning risk-prediction algorithm in conjunction with diagnostic testing for identifying undiagnosed atrial fibrillation (AF) in primary care in England. Eligible participants (aged ≥30 years without AF diagnosis; The AF risk-prediction algorithm accurately identified high-risk participants in both arms. While the proportions of AF and related arrhythmia diagnoses were not significantly different between high-risk arms, intervention arm participants who underwent diagnostic testing were twice as likely to receive arrhythmia diagnoses compared with routine care. The algorithm could be a valuable tool to select primary care groups at high risk of undiagnosed AF who may benefit from diagnostic testing.
Identifiants
pubmed: 36713002
doi: 10.1093/ehjdh/ztac009
pii: ztac009
pmc: PMC9707963
doi:
Types de publication
Journal Article
Langues
eng
Pagination
195-204Informations de copyright
© The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology.
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