Cost Effectiveness Analysis of Remdesivir in COVID-19 Patients at a Large Academic Medical Center

Cost-effectiveness analysis (CEA) modeling is one form of health care economic evaluation that can be used to establish the comparative effectiveness between two medical interventions. Chapter one provides a brief overview of CEA modeling and introduces the components of a decision tree model. Chapter two introduces a software technology, TreeAge Pro Healthcare (TreeAge Software, Inc., Williamstown, MA) that is available on the market for building CEA models. A hypothetical decision tree model is provided as an example to showcase the steps to consider when building a new model in TreeAge. Chapter three includes an adaptation of the remdesivir CEA model published by the Institute for Clinical and Economic Review (ICER) to examine the incremental cost required to gain one unit of health benefit, or per quality-adjusted life year (QALY), from the hospital perspective. The adapted model is built in TreeAge, then inputted with two datasets. First, the decision tree is populated with clinical parameters from the Adaptive COVID-19 Treatment Trial-1 (ACTT-1) trial. Second, the decision tree is modified and populated with real-world coronavirus disease 2019 (COVID-19) patient data collected at a large academic medical center. This institutional review board (IRB)-approved study incorporating data from retrospectively reviewed data for hospitalized COVID-19 patient did not find remdesivir to be cost-effective in reducing hospitalization cost. However, this decision tree CEA model combines a structural depiction of COVID-19 severity categories and a comparative effectiveness analysis of remdesivir plus standard of care versus standard of care alone using real-world data. As the COVID-19 pandemic continues to post unforeseeable challenges for pharmacy departments in the health care setting, having a cost-effective treatment and/or prevention is imperative. Chapter four also mentions another powerful alternative for building economic models, Microsoft Excel (Microsoft Corporation, Redmond, WA). In addition, chapter four suggests future studies that may contribute to the body of limited literature analyzing cost-saving implications of treatments and/or preventions for COVID-19 from the hospital perspective.