Model for estimating the healthcare costs and capacity of intensive care units in Italy in the treatment of patients with COVID-19: remdesivir impact assessment
DOI:
https://doi.org/10.33393/abtpn.2020.2213Keywords:
COVID-19, Intensive Care Units, RemdesivirAbstract
Introduction: Coronavirus disease 2019 (COVID-19) is caused by a severe acute respiratory syndrome coronavirus 2, which is a human coronavirus responsible for a pandemic. Direct interventions, i.e. physical distancing and use of protective devices, can prevent or limit contagions, however, it is also required to evaluate the optimization of limited resources, such as the Intensive Care Unit (ICU). For this purpose, it is relevant to estimate the impact of therapeutic solutions that reduce the probability that the patient transits to ICU in symptomatic subjects and in need of hospitalization. The therapeutic solutions allow a more rapid recovery of the patient and save scarce resources that can be used in the treatment of other patients.
Methods: A forecasting model is designed to estimate the impact of one therapeutic solution, i.e. the antiretroviral Remdesivir, on both the capacity of intensive care and the healthcare costs for hospitals when managing the current emergency. A base case is presented as well as a best and a worst case scenario deriving from the sensitivity analyses.
Results: The introduction of Remdesivir in patients receiving low-flow oxygen therapy with the purpose of reducing ICU accesses and deaths leads to 431 million euros cost savings and avoids 17,150 hospitalizations in intensive care and 6,923 deaths. In the best case, 294 million euros savings are estimated, whilst in the worst case the model estimates a saving of 512 million euros.
Conclusions: Remdesivir has the potential to reduce the negative effects of the Coronavirus disease, improving patient conditions and reducing death tolls, and can also save scarce healthcare resources during this pandemic, resulting in a shorter hospital stay and fewer ICU admissions. (Market Access)
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Accepted 2020-12-04
Published 2020-12-17