Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm

• Published in: Journal of medical economics Vol. 23; no. 4; pp. 386 - 393
• Main Authors: Hill, Nathan R., Sandler, Belinda, Mokgokong, Ruth, Lister, Steven, Ward, Thomas, Boyce, Rebecca, Farooqui, Usman, Gordon, Jason
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 02.04.2020
• Subjects: Algorithms; Atrial fibrillation; Atrial Fibrillation - diagnosis; Cost-Benefit Analysis; cost-effectiveness analysis; Decision Trees; Humans; Machine Learning; Markov Chains; Mass Screening - economics; Mass Screening - methods; Quality-Adjusted Life Years; Risk Assessment - statistics & numerical data; screening; Undiagnosed Diseases - diagnosis; United Kingdom; United Kingdom; C52; screening; cost-effectiveness analysis; machine learning; Atrial fibrillation; C51

Aims: As many cases of atrial fibrillation (AF) are asymptomatic, patients often remain undiagnosed until complications (e.g. stroke) manifest. Risk-prediction algorithms may help to efficiently identify people with undiagnosed AF. However, the cost-effectiveness of targeted screening remains uncert...