1737P - Use of the predictive risk model LungFlagTM for lung cancer screening in screening in a Spanish reference center: A cost-effectiveness analysis

Background

Lung cancer screening programs using low dose computed tomography (LDCT) reduce mortality by early detection. LungFlag is an artificial intelligence-based predictive model of non-small cell lung cancer (NSCLC) risk, evaluating routine clinical and laboratory data to improve selection of individuals at-risk for enrollment in screening programs. The aim of this analysis was to assess the cost-effectiveness of LungFlag in the area of a Spanish reference center.

Methods

A combination of a decision tree and a Markov model was adapted to the Spanish setting. A multidisciplinary group of experts validated all the parameters and assumptions used in the model. Demographic characteristics were in line with those reported by Gould et al. (2021) . We defined 2 hypothetical cohorts, a broader cohort of 3000 ever-smokers aged 55-80 years, and another cohort of 1000 individuals meeting 2013 USPSTF criteria. A lifetime horizon was used, and only direct medical costs were considered. Sensitivity and specificity values were obtained from Gould et al. (2021) , and unit costs were obtained from local databases (€ 2023). Results were expressed in life-years (LYs), quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (ICER). Sensitivity analyses were performed to assess the consistency of the model results.

Results

LungFlag provides a higher number of LYs and QALYs with a lower cost (dominant) compared to non-screening, as shown in the table. Sensitivity analyses confirmed the robustness of base case results.

Table: 1737P

Results for the hypothetical cohorts

Conclusions

The implementation of LungFlag as a risk model for NSCLC screening in a Spanish reference center would be cost-effective compared to no screening for the 2 hypothetical cohorts analyzed, providing savings and a higher clinical benefit.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

Roche Pharma Spain.

Funding

Roche Pharma.

Disclosure

N. Arrabal: Other, Institutional, Advisory Board, Roche Employee: Roche Pharma. A. Flores, F. Garcia Verdes-Montenegro: Other, Institutional, Other, Roche Employee: Roche Pharma. M. Crespo, D. Carcedo: Other, Institutional, Other, Consultant: Hygeia Consulting. C. Heuser, N. Olghi: Financial Interests, Institutional, Other, Roche Employee: Roche Pharma. All other authors have declared no conflicts of interest.