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Identification of undiagnosed atrial fibrillation using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI) in primary care: cost-effectiveness of a screening strategy evaluated in a randomized controlled trial in England

Hill, Nathan R.; Groves, Lara; Dickerson, Carissa; Boyce, Rebecca; Lawton, Sarah; Hurst, Michael; Pollock, Kevin G.; Sugrue, Daniel M.; Lister, Steven; Arden, Chris; Davies, D. Wyn; Martin, Anne-Celine; Sandler, Belinda; Gordon, Jason; Farooqui, Usman; Clifton, David; Mallen, Christian; Rogers, Jennifer; Camm, A. John; Cohen, Alexander T.

Identification of undiagnosed atrial fibrillation using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI) in primary care: cost-effectiveness of a screening strategy evaluated in a randomized controlled trial in England Thumbnail


Authors

Nathan R. Hill

Lara Groves

Carissa Dickerson

Rebecca Boyce

Michael Hurst

Kevin G. Pollock

Daniel M. Sugrue

Steven Lister

Chris Arden

D. Wyn Davies

Anne-Celine Martin

Belinda Sandler

Jason Gordon

Usman Farooqui

David Clifton

Jennifer Rogers

A. John Camm

Alexander T. Cohen



Abstract

OBJECTIVE: The PULsE-AI trial sought to determine the effectiveness of a screening strategy that included a machine learning risk prediction algorithm in conjunction with diagnostic testing for identification of undiagnosed atrial fibrillation (AF) in primary care. This study aimed to evaluate the cost-effectiveness of implementing the screening strategy in a real-world setting. METHODS: Data from the PULsE-AI trial - a prospective, randomized, controlled trial conducted across six general practices in England from June 2019 to February 2021 - were used to inform a cost-effectiveness analysis that included a hybrid screening decision tree and Markov AF disease progression model. Model outcomes were reported at both individual- and population-level (estimated UK population =30?years of age at high-risk of undiagnosed AF) and included number of patients screened, number of AF cases identified, mean total and incremental costs (screening, events, treatment), quality-adjusted-life-years (QALYs), and incremental cost-effectiveness ratio (ICER). RESULTS: The screening strategy was estimated to result in 45,493 new diagnoses of AF across the high-risk population in the UK (3.3 million), and an estimated additional 14,004 lifetime diagnoses compared with routine care only. Per-patient costs for high-risk individuals who underwent the screening strategy were estimated at £1,985 (vs £1,888 for individuals receiving routine care only). At a population-level, the screening strategy was associated with a cost increase of approximately £322 million and an increase of 81,000 QALYs. The screening strategy demonstrated cost-effectiveness versus routine care only at an accepted ICER threshold of £20,000 per QALY-gained, with an ICER of £3,994/QALY. CONCLUSIONS: Compared with routine care only, it is cost-effective to target individuals at high risk of undiagnosed AF, through an AF risk prediction algorithm, who should then undergo diagnostic testing. This AF risk prediction algorithm can reduce the number of patients needed to be screened to identify undiagnosed AF, thus alleviating primary care burden.

Journal Article Type Article
Acceptance Date Jul 13, 2022
Online Publication Date Aug 3, 2022
Publication Date Aug 3, 2022
Publicly Available Date May 30, 2023
Journal Journal of Medical Economics
Print ISSN 1369-698X
Volume 25
Issue 1
Pages 974 - 983
DOI https://doi.org/10.1080/13696998.2022.2102355
Keywords Health Policy
Publisher URL https://www.tandfonline.com/doi/full/10.1080/13696998.2022.2102355

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