Precision medicine approaches to biologic initiation in severe asthma: A cost-effectiveness analysis

While oral corticosteroids (OCS) are commonly used in severe asthma, the long-term use is associated with serious comorbidities. Biologics have emerged as advanced therapeutic options that can effectively reduce OCS dependence, but their high costs limited wider adoption and patient access.

To improve patient outcomes while avoiding unnecessary costs, researchers are now exploring precision medicine approaches. In severe asthma management, two precision medicine approaches for initiating biologics have gained attention: biomarker phenotyping and artificial intelligence (AI)-based risk stratification. However, their economic value remains unknown.

Therefore, this project will assess the cost-effectiveness of three approaches for starting biologics in severe asthma in Singapore: (1) treating all eligible patients with a single biologic (anti-TSLP); (2) selecting specific biologics based on a local hospital’s biomarker-guided algorithm; and (3) incorporating AI-based risk prediction to focus biologic therapy on high-risk patients. We will build a health economic model using local healthcare data and systematic literature review to estimate lifetime costs and health outcomes under each treatment strategy. The findings will provide evidence on which approach offers the best value for money and whether the incorporation of AI risk stratification provides added values, supporting policymakers and healthcare providers in improving severe asthma care.