Dataset Information

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.

ABSTRACT:

Aims

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.

Methods and results

Eligible participants (aged ≥30 years without AF diagnosis; n = 23 745) from six general practices in England were randomized into intervention and control arms. Intervention arm participants, identified by the algorithm as high risk of undiagnosed AF (n = 944), were invited for diagnostic testing (n = 256 consented); those who did not accept the invitation, and all control arm participants, were managed routinely. The primary endpoint was the proportion of AF, atrial flutter, and fast atrial tachycardia diagnoses during the trial (June 2019-February 2021) in high-risk participants. Atrial fibrillation and related arrhythmias were diagnosed in 5.63% and 4.93% of high-risk participants in intervention and control arms, respectively {odds ratio (OR) [95% confidence interval (CI)]: 1.15 (0.77-1.73), P = 0.486}. Among intervention arm participants who underwent diagnostic testing (28.1%), 9.41% received AF and related arrhythmia diagnoses [vs. 4.93% (control); OR (95% CI): 2.24 (1.31-3.73), P = 0.003].

Conclusion

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.

SUBMITTER: Hill NR

PROVIDER: S-EPMC9707963 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

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.

Hill Nathan R NR Groves Lara L Dickerson Carissa C Ochs Andreas A Pang Dong D Lawton Sarah S Hurst Michael M Pollock Kevin G KG Sugrue Daniel M DM Tsang Carmen C Arden Chris C Wyn Davies David D Martin Anne Celine AC Sandler Belinda B Gordon Jason J Farooqui Usman U Clifton David D Mallen Christian C Rogers Jennifer J Camm Alan John AJ Cohen Alexander T AT

European heart journal. Digital health 20220323 2

<h4>Aims</h4>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.<h4>Methods and results</h4>Eligible participants (aged ≥30 years without AF diagnosis; <i>n</i> = 23 745) from six general practices in England were randomized into intervention and control arms. Intervention arm participants, identified by the algorithm as ...[more]

PMID: 36713002

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Dataset Information

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.

Aims

Methods and results

Conclusion

Publications

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.

Similar Datasets

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