Cost-effectiveness analysis of genetic tools to predict treatment response in patients with cystic fibrosis

Background: Cystic fibrosis (CF) transmembrane conductance regulator (CFTR) modulator therapies show variable efficacy for patients with CF. Patient-derived predictive tools may identify individuals likely to respond to CFTRs, but are not in routine use. We aimed to determine the cost-utility of predictive toolguided treatment with CFTRs as add-on to standard of care (SoC) for individuals with CF. Methods: This economic evaluation compared two strategies using an individual level simulation: (i) Treat All , where all patients received CFTRs plus SoC and (ii) Test -> Treat , where patients who tested positive on predictive tools received CFTRs plus SoC and those who tested negative received SoC only. We simulated 50,0 0 0 individuals over their lifetime, and estimated costs (2020 CAD) per quality-adjusted life year (QALY) from the healthcare payer's perspective, discounted at 1.5% annually. The model was populated using Canadian CF registry data and published literature. Probabilistic and deterministic sensitivity were conducted. Results: The Treat All and Test -> Treat and strategies yielded 22.41 and 21.36 QALYs, and cost $4.21 M and $3.15 M respectively. Results of probabilistic sensitivity analysis showed that Test -> Treat was highly costeffective com pared to Treat All in 100% of simulations at cost-effectiveness thresholds as high as $50 0,0 0 0 per QALY. Test -> Treat may save between $931 K to $1.1 M per QALY lost, depending on sensitivity and specificity of predictive tools. Conclusion: The use of predictive tools could optimize the health benefits of CFTR modulators while reducing costs. Our findings support the use of pre-treatment predictive testing and may help inform coverage and reimbursement policies for individuals with CF. (c) 2023 European Cystic Fibrosis Society. Published by Elsevier B.V. All rights reserved.