Utilising genomic data to improve AAA risk prediction in screening: an economic assessment

In recent years there has been substantial investment in genomic research, including exploring the use of polygenic risk scores (PRS) for disease diagnosis and disease prevention strategies. For diseases that do not have a screening programme in place, the patient level benefits are clear, but what is the impact when screening is already offered at the population level and incorporating genomic risk leads to a reduction in the number screened?

The cost-effectiveness of the current UK screening program for abdominal aortic aneurysm (AAA) has been compared here with an intervention where genetic pre-screening determines which patients are invited to the screening programme. Pre-screening is first explored using a published PRS for AAA disease, and subsequently using a meta-genetic risk score (metaGRS), developed to include the genomic risk of traits and diseases found to have evidence of a potential causal association with AAA. An existing discrete event simulation model has been adapted and used for economic evaluation.

Pre-screening offers a cost-effective alternative to population wide screening in a scenario where genomic risk information is freely available. However, this results in a reduction in benefits to patients with aneurysms due to worse outcomes from missed cases. The metaGRS outperformed the PRS as a pre-screening tool due to better discriminative ability.

Pre-screening results in a loss of benefit to AAA patients unless genetic prediction is 100% accurate. Better disease prediction can limit the loss of these benefits and maximise the cost-effectiveness of the programme. Money saved from screening fewer patients could release opportunity cost to improve overall patient benefits in the health service, by freeing up financial resource to target other high-risk individuals not currently included in the NAAASP, or to fund other services that offer better value for money.