Related Experiment Video
Updated: Oct 2, 2025
Catheter Ablation in Combination With Left Atrial Appendage Closure for Atrial Fibrillation
Published on: February 26, 2013
Budget impact analysis of a machine learning algorithm to predict high risk of atrial fibrillation among primary care
Tomasz Szymanski1, Rachel Ashton1, Sara Sekelj1,2
1Imperial College Health Partners, London NW1 2FB, UK.
Insights
Using an atrial fibrillation (AF) risk algorithm with standard screening significantly improves AF detection and stroke prevention. This combined approach also substantially reduces healthcare costs for the National Health Service (NHS) and personal social services (PSS).
Area of Science:
- Cardiology
- Public Health
- Health Economics
Background:
- Atrial fibrillation (AF) detection rates in primary care remain suboptimal, leading to a significant gap in diagnosis and stroke prevention.
- Opportunistic screening is the current standard of care, but its effectiveness in identifying all at-risk individuals is limited.
- The economic impact of undiagnosed AF and its associated complications, such as stroke, places a considerable burden on healthcare systems.
Purpose of the Study:
- To evaluate the effectiveness of an atrial fibrillation (AF) risk prediction algorithm in improving AF detection compared to opportunistic screening in UK primary care.
- To assess the budget impact of implementing an AF risk prediction algorithm on National Health Service (NHS) and personal social services (PSS) costs.
- To determine the potential for stroke prevention through enhanced AF detection using a risk prediction algorithm.
Main Methods:
- A comparative analysis of three screening scenarios was conducted: opportunistic screening, algorithm-based screening, and a combined approach.
- The study included UK general practice patients aged 65 years or older with recorded anthropometric and blood pressure data.
- A 3-year time horizon was used to analyze budget impact on NHS and PSS costs, alongside projected stroke prevention rates.
Main Results:
- The combined approach (Scenario 3) identified the highest number of new AF cases (99,267), reducing the detection gap by 27% and preventing an estimated 3299 strokes.
- Algorithm-based screening (Scenario 2) showed a significant reduction in 3-year NHS costs (‒92.0%) compared to standard care, with a 19% reduction in the AF detection gap.
- The combined use of standard care and the algorithm resulted in substantial savings for NHS plus PSS combined costs (‒46.1%).
Conclusions:
- Integrating an AF risk prediction algorithm into primary care significantly enhances AF detection and stroke prevention.
- The algorithm-based approach, particularly when combined with opportunistic screening, offers substantial cost savings for the NHS and PSS.
- Implementing this algorithm represents a cost-effective strategy to improve cardiovascular health outcomes and reduce the burden of stroke.
Aims:
We investigated whether the use of an atrial fibrillation (AF) risk prediction algorithm could improve AF detection compared with opportunistic screening in primary care and assessed the associated budget impact.
Methods And Results:
Eligible patients were registered with a general practice in UK, aged 65 years or older in 2018/19, and had complete data for weight, height, body mass index, and systolic and diastolic blood pressure recorded within 1 year. Three screening scenarios were assessed: (i) opportunistic screening and diagnosis (standard care); (ii) standard care replaced by the use of the algorithm; and (iii) combined use of standard care and the algorithm. The analysis considered a 3-year time horizon, and the budget impact for the National Health Service (NHS) costs alone or with personal social services (PSS) costs. Scenario 1 would identify 79 410 new AF cases (detection gap reduced by 22%). Scenario 2 would identify 70 916 (gap reduced by 19%) and Scenario 3 would identify 99 267 new cases (gap reduction 27%). These rates translate into 2639 strokes being prevented in Scenario 1, 2357 in Scenario 2, and 3299 in Scenario 3. The 3-year NHS budget impact of Scenario 1 would be £45.3 million, £3.6 million (difference ‒92.0%) with Scenario 2, and £46.3 million (difference 2.2%) in Scenario 3, but for NHS plus PSS would be ‒£48.8 million, ‒£80.4 million (64.8%), and ‒£71.3 million (46.1%), respectively.
Conclusion:
Implementation of an AF risk prediction algorithm alongside standard opportunistic screening could close the AF detection gap and prevent strokes while substantially reducing NHS and PSS combined care costs.
