ArticleBMC nephrology2025
Health-economic evaluation of an AI-powered decision support system for anemia management in in-center hemodialysis patients.
Article in BMC nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Assessing the Value for Money of AI-Assisted Technologies for Older Adults: Scoping Review of Economic Evaluations.Journal of medical Internet research · 2026Article
- Budget impact of expanded hemodialysis versus high-flux hemodialysis using real-world evidence in Colombia.Cost effectiveness and resource allocation : C/E · 2026Article
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- Health-economic evaluation of a novel personalized digital avatar-based anemia management software in hemodialysis patients.NPJ digital medicine · 2026Article
- Anemia in young women: determinants and artificial intelligence-based management approaches.Frontiers in artificial intelligence · 2026Review
- A deterministic large language model (LLM) framework for safe, protocol-adherent clinical decision support: application in hemodialysis anemia management (AnemiaCare HDs).Frontiers in artificial intelligence · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsThis 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.
resultsThis 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.
conclusionsThis modelling study demonstrates that ACM is cost-effective for managing anemia in German in-center HD patients.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.