Sickle cell disease (SCD) is a prevalent genetic disorder in the United States, with a significant economic burden due to the high costs of care, especially from chronic red blood cell (RBC) transfusions. These transfusions carry risks such as alloimmunization, which can lead to complications like delayed hemolytic transfusion reactions (DHTRs), further increasing healthcare costs in addition to increasing suGering. This study aims to aid in the evaluation of cost-eGective strategies for SCD management using Markov models, currently within TreeAge Pro software, which is proprietary. A decision model was adapted from the Kacker et al. (2013) study, which analyzed the cost-eGectiveness of antigen-matching strategies to prevent alloimmunization. The model was exported as a .trex file for analysis and then restructured using Amua software, enabling the integration of sickle cell data for further cost analysis using free software. Amua can also export to R, enabling more thorough simulations and analyses. Our work allows researchers to more easily compare various transfusion management strategies, such as prospective antigen matching and history-based antigen matching, assessing their long-term financial impact on healthcare systems. It can also ease the conversion of other models from TreeAge Pro to Amua and thus to R.
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