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Published on: February 26, 2013
Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a
Nathan R Hill1, Belinda Sandler1, Ruth Mokgokong2
1Bristol-Myers Squibb, Uxbridge, UK.
Insights
Targeted screening for atrial fibrillation (AF) using a machine learning (ML) algorithm is cost-effective. This approach identifies more undiagnosed AF cases efficiently, improving patient outcomes and healthcare resource use.
Area of Science:
- Cardiology
- Health Economics
- Artificial Intelligence in Medicine
Background:
- Many atrial fibrillation (AF) cases are asymptomatic, leading to delayed diagnosis and potential complications like stroke.
- Risk-prediction algorithms offer a potential solution for efficiently identifying individuals with undiagnosed AF.
- The cost-effectiveness of targeted AF screening strategies remains an area requiring investigation.
Purpose of the Study:
- To assess the cost-effectiveness of targeted screening for atrial fibrillation (AF) using a machine learning (ML) risk prediction algorithm.
- To compare the economic and clinical outcomes of ML-informed targeted screening against traditional systematic and opportunistic AF screening methods.
- To evaluate the efficiency of ML-driven screening in identifying undiagnosed AF in adults aged 50 and above from a UK NHS perspective.
Main Methods:
- Cost-effectiveness analysis was performed using a hybrid screening decision tree and Markov disease progression model.
- The study compared costs and outcomes of traditional systematic and opportunistic AF screening with ML-informed targeted screening.
- Model analyses focused on adults aged 50 years and older, adopting the UK National Health Service (NHS) perspective.
Main Results:
- Targeted ML-informed screening required fewer individuals to be screened per 1,000 patients (61) compared to systematic (534) and opportunistic (687) strategies.
- This targeted approach detected more AF cases (11 per 1,000) versus systematic (6) and opportunistic (8) screening.
- The ML-informed targeted strategy demonstrated cost-effectiveness, with a lower cost per Quality-Adjusted Life Year (QALY) gained (£4,847 vs. £5,544) and yielded additional QALYs (3.40 and 2.05 per 1,000 patients).
Conclusions:
- Targeted screening utilizing a machine learning (ML) risk prediction algorithm presents a clinically and economically effective strategy for identifying atrial fibrillation (AF).
- This method enhances health outcomes by efficiently allocating limited healthcare resources.
- The ML-driven targeted screening approach proved cost-effective across all evaluated scenarios.
Abstract:
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 uncertain. This study aimed to assess the cost-effectiveness of targeted screening, informed by a machine learning (ML) risk prediction algorithm, to identify patients with AF.Methods: Cost-effectiveness analyses were undertaken utilizing a hybrid screening decision tree and Markov disease progression model. Costs and outcomes associated with the detection of AF compared traditional systematic and opportunistic AF screening strategies to targeted screening informed by a ML risk prediction algorithm. Model analyses were based on adults ≥50 years and adopted the UK NHS perspective.Results: Targeted screening using the ML risk prediction algorithm required fewer patients to be screened (61 per 1,000 patients, compared to 534 and 687 patients in the systematic and opportunistic strategies) and detected more AF cases (11 per 1,000 patients, compared to 6 and 8 AF cases in the systematic and opportunistic screening strategies). The targeted approach demonstrated cost-effectiveness under base case settings (cost per QALY gained of £4,847 and £5,544 against systematic and opportunistic screening respectively). The targeted screening strategy was predicted to provide an additional 3.40 and 2.05 QALYs per 1,000 patients screened versus systematic and opportunistic strategies. The targeted screening strategy remained cost-effective in all scenarios evaluated.Limitations: The analysis relied on assumptions that include the extended period of patient life span and the lack of consideration for treatment discontinuations/switching, as well as the assumption that the ML risk-prediction algorithm will identify asymptomatic AF.Conclusions: Targeted screening using a ML risk prediction algorithm has the potential to enhance the clinical and cost-effectiveness of AF screening, improving health outcomes through efficient use of limited healthcare resources.
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