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

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.

Keywords:

• Atrial fibrillation
• screening
• machine learning
• neural network
• risk prediction
• cost-effectiveness

JEL codes:

• H51
• H5
• H
• I00
• I

Transparency

Declaration of funding

This work was funded by Bristol Myers Squibb Pharmaceutical Ltd & Pfizer Inc. The funder of the study was involved in the study design, data interpretation, and review of the report but had no role in data collection or data analysis. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Declaration of financial/other relationships

AJC has received institutional grants and personal fees from Bayer, Boehringer Ingelheim, Bristol Myers Squibb/Pfizer Alliance and Daiichi Sankyo.

ACM has received consulting fees and a research grant from Bristol Myers Squibb/Pfizer Alliance, and consulting fees from Bayer-Healthcare and Boehringer-Ingelheim.

DAC declares fees from Oxford University Innovation, Biobeats, Bristol Myers Squibb, Sensyne Health and academic research grants from GlaxoSmithKline, AstraZeneca, Apple, RCUK, Wellcome Trust, EU Horizon2020, and NIHR.

ATC receives consulting fees from AbbVie, ACI Clinical, Alexion, Aspen, Astra Zeneca, Bayer, Boehringer-Ingelheim, Bristol Myers Squibb, Boston Scientific, CSL Behring, Daiichi-Sankyo, GlaxoSmithKline, GLG, Guidepoint Global, Johnson and Johnson, Leo Pharma, Medscape, McKinsey, Navigant, ONO, Pfizer, Portola, Sanofi, TRN; advisory board membership with Bayer, Bristol Myers Squibb, Daiichi-Sankyo, Johnson and Johnson, Pfizer, Portola, Sanofi; payments for lectures including speakers bureau services, payments for preparation of reports and payment for development of educational presentations from, Aspen, Bayer, Boehringer-Ingelheim, Bristol Myers Squibb, Daiichi, GlaxoSmithKline, Johnson and Johnson, Medscape, Pfizer and Portola. He has advised the UK Government Health Select Committee, the all-party working group on thrombosis, the Department of Health, and the NHS, on the prevention of VTE. He is also an advisor to Lifeblood: the thrombosis charity and is the founder of the European educational charity the Coalition to Prevent Venous Thromboembolism.

CA and DWD have received consulting fees from Bristol Myers Squibb Pharmaceutical Ltd & Pfizer in relation to this study.

NRH, UF, MH, KGP, BS, and SL (Steven Lister) are employees of Bristol Myers Squibb UK. MH was previously employed by Health Economics and Outcomes Research Ltd (HEOR).

JG, LG, CD, RB, and DMS are current or former employees of HEOR. HEOR were a paid consultant to Bristol Myers Squibb Pharmaceutical Ltd & Pfizer in connection with the development of this manuscript.

JR is an employee of PHASTAR. PHASTAR were a paid consultant to Bristol Myers Squibb Pharmaceutical Ltd & Pfizer in connection with the development of this manuscript.

CM and SL (Sarah Lawton) are employees of Keele University. Keele University were a paid consultant to Bristol Myers Squibb Pharmaceutical Ltd & Pfizer in connection with the development of this manuscript. CM is also funded by the National Institute for Health Research (NIHR) Applied Research Collaboration West Midlands and the NIHR School for Primary Care Research.

Author contributions

ATC was the Chief Investigator for the trial and the chair of the Trial Steering Committee (TSC). NRH, MH, KGP, BS, UF, JG, and DMS were responsible for study design, trial management, and interpretation of results. LG was responsible for day-to-day trial and site management, assisted by SL. CD wrote the first draft of the manuscript with input from DMS. RB was responsible for the modeling and cost-effectiveness analyses, with input from MH and SL. CA, DC, CM, and JR were members of the TSC, responsible for study design and trial oversight. ACM, DWD, and AJC were the trial cardiologists responsible for the review of all trial ECGs; they were also members of the TSC, responsible for study design and trial oversight. All authors revised and approved the final version of the manuscript.

Acknowledgements

The authors wish to thank the following principal investigators and research nurses for their significant contributions to data collection: Dr Andrew Wallis and Terri-Anne Thomson (Alcester Health Centre); Dr Manjit Kainth and Jacqueline Woodward (Primrose Lane Surgery); Dr Paul Ainsworth and Oliva Neely (Sherbourne Medical Centre); Dr Caron Morton (Station Drive Surgery); Dr Claire Jones (Spring Gardens Group Medical Practice); Dr Stefan Lachowicz and Deborah Williams (The Caxton Surgery); and Sian Jones and Jonathan Davies (National Institute for Health Research).

The authors also wish to thank Isaac Mackenzie and Aris Skandamis of Health Economics and Outcomes Research (HEOR) Ltd for their contributions to the cost-effectiveness analysis. HEOR Ltd were paid consultants to Bristol Myers Squibb Pharmaceutical Ltd & Pfizer Inc. in connection with the development of this manuscript.

Data availability statement

Deidentified individual participant data that underlie the results reported in this article and the study protocol and statistical analysis plan will be made available upon reasonable request to the corresponding author.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.