Health-economic evaluation of an AI-powered decision support system for anemia management in in-center hemodialysis patients.

Abstract

Background: The Anemia Control Model (ACM) is a decision support system powered by an artificial intelligence core designed to assist nephrologists in managing anemia therapy for in-center hemodialysis (HD) patients. This study aims to evaluate the cost-effectiveness of the ACM compared to standard of care in Germany, defined as the absence of ACM and a hemoglobin (Hb) target achievement rate of less than 70% among in-center HD patients, based on results from matched observational studies.

Methods: This simulation study adopted the perspective of the German statutory health insurance. A Markov (cohort) state-transition model was used to project the effects of the ACM over the remaining lifetime of patients. All costs were expressed in 2024 euros, and both costs and quality-adjusted life years (QALYs) were discounted at a rate of 3% per year. To test the sensitivity of the results, one-way sensitivity analyses and a probabilistic sensitivity analysis were performed.

Results: This study finds that ACM provides more QALYs and incurs lower costs compared to standard of care. The net monetary value of ACM is €38,423 per patient in the base case scenario. In the sensitivity analysis, the annual cost of erythropoiesis-stimulating agents emerged as the variable with the largest impact on the value of ACM. The probabilistic sensitivity analysis shows that 100% of cost-effect pairs fall within the dominant southeast quadrant, indicating cost-effectiveness.

Conclusions: This modelling study demonstrates that ACM is cost-effective for managing anemia in German in-center HD patients.