Budget impact analysis of a machine learning algorithm to predict high risk of atrial fibrillation among primary care patients

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 pound million, 3.6 pound million (difference -92.0%) with Scenario 2, and 46.3 pound million (difference 2.2%) in Scenario 3, but for NHS plus PSS would be -48.8 pound million, -80.4 pound million (64.8%), and -71.3 pound 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.

Automated summary

This study investigated whether the use of an AF risk prediction algorithm could improve AF detection in primary care and assessed the associated budget impact. The results showed that implementing the algorithm alongside standard opportunistic screening could close the AF detection gap, prevent strokes, and substantially reduce healthcare costs.