دورية أكاديمية
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.
| العنوان: | 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. |
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| المؤلفون: | Hill NR; Bristol Myers Squibb Pharmaceuticals Ltd., Uxbridge, UK., Groves L; HEOR, Unit A, Health Economics and Outcomes Research Ltd., Cardiff, UK., Dickerson C; HEOR, Unit A, Health Economics and Outcomes Research Ltd., Cardiff, UK., Boyce R; HEOR, Unit A, Health Economics and Outcomes Research Ltd., Cardiff, UK., Lawton S; School of Medicine, Keele University, Staffordshire, UK., Hurst M; Bristol Myers Squibb Pharmaceuticals Ltd., Uxbridge, UK., Pollock KG; Bristol Myers Squibb Pharmaceuticals Ltd., Uxbridge, UK., Sugrue DM; HEOR, Unit A, Health Economics and Outcomes Research Ltd., Cardiff, UK., Lister S; Bristol Myers Squibb Pharmaceuticals Ltd., Uxbridge, UK., Arden C; NHS Foundation Trust, University Hospital Southampton, Southampton, UK., Davies DW; London Heart Practice, London, UK., Martin AC; Université de Paris, Innovative Therapies in Haemostasis, INSERM, Hôpital Européen Georges Pompidou, Service de Cardiologie, Paris, France., Sandler B; Bristol Myers Squibb Pharmaceuticals Ltd., Uxbridge, UK., Gordon J; HEOR, Unit A, Health Economics and Outcomes Research Ltd., Cardiff, UK., Farooqui U; Bristol Myers Squibb Pharmaceuticals Ltd., Uxbridge, UK., Clifton D; Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK., Mallen C; School of Medicine, Keele University, Staffordshire, UK., Rogers J; Statistical Research and Consultancy, Unit 2, PHASTAR, London, UK., Camm AJ; Cardiology Clinical Academic Group, Molecular & Clinical Sciences Research Institute, St. George's University of London, London, UK., Cohen AT; Department of Haematological Medicine, Guys and St Thomas' NHS Foundation Trust, King's College London, London, UK. |
| المصدر: | Journal of medical economics [J Med Econ] 2022 Jan-Dec; Vol. 25 (1), pp. 974-983. |
| نوع المنشور: | Journal Article; Randomized Controlled Trial |
| اللغة: | English |
| بيانات الدورية: | Publisher: Taylor & Francis Country of Publication: England NLM ID: 9892255 Publication Model: Print Cited Medium: Internet ISSN: 1941-837X (Electronic) Linking ISSN: 13696998 NLM ISO Abbreviation: J Med Econ Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: 2015- : Abingdon, Oxford : Taylor & Francis Original Publication: Richmond, Surrey : Brookwood Medical, 1998- |
| مواضيع طبية MeSH: | Atrial Fibrillation*/complications, Algorithms ; Artificial Intelligence ; Cost-Benefit Analysis ; Electrocardiography ; Humans ; Machine Learning ; Mass Screening ; Primary Health Care ; Prospective Studies ; Quality-Adjusted Life Years |
| مستخلص: | 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. |
| فهرسة مساهمة: | Keywords: Atrial fibrillation; H; H5; H51; I; I00; cost-effectiveness; machine learning; neural network; risk prediction; screening |
| تواريخ الأحداث: | Date Created: 20220714 Date Completed: 20220805 Latest Revision: 20220805 |
| رمز التحديث: | 20240628 |
| DOI: | 10.1080/13696998.2022.2102355 |
| PMID: | 35834373 |
| قاعدة البيانات: | MEDLINE |
PDF full text from Publisher | تدمد: | 1941-837X |
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| DOI: | 10.1080/13696998.2022.2102355 |