Objective: Prescription trends and patterns of anti-COVID-19 drugs in hospitalized patients were examined based on real world data to understand the use of anti-COVID-19 drugs in clinical practice in Japan.
Design: The longitudinal and cross-sectional study was conducted utilizing data from January 1, 2019 to December 31, 2021 of the MID-NET® medical information database, which stored the electronic medical records, administrative claim data, and diagnosis procedure combination data of patients in Japan.
Participants: Hospitalized patients with a COVID-19-related diagnosis who received at least one anti-COVID-19 drug between April 1, 2020 and December 31, 2021.
Exposures: The following 14 drugs were included in this study: remdesivir, baricitinib, combination product of casirivimab and imdevimab, favipiravir, dexamethasone, ivermectin, azithromycin, nafamostat mesylate, camostat mesylate, ciclesonide, tocilizumab, sarilumab, combination product of lopinavir and ritonavir, and hydroxychloroquine.
Results: We identified 5,717 patients hospitalized with COVID-19 and prescribed at least one anti-COVID-19 drug. The entire cohort generally included patients over 41-50 years and more males. The most common prescription pattern was dexamethasone monotherapy (22.9%), followed by the concomitant use of remdesivir and dexamethasone (15.0%), azithromycin monotherapy (15.0%), remdesivir monotherapy (10.2%), and nafamostat mesylate monotherapy (8.5%). However, an often prescribed anti-COVID-19 drug differed depending on the period.
Conclusions and relevance: This study revealed the real-world situation of anti-COVID-19 drug prescriptions in hospitalized COVID-19 patients in Japan. A prescribed drug would depend on the latest scientific evidence, such as efficacy, safety, and approval status, at the time of prescription. Understanding the prescription of anti-COVID-19 drugs will be important for providing the most up-to-date treatments to patients and evaluating the benefit and/or risk of anti-COVID-19 drugs based on the utilization of an electronic medical record database.
Copyright: © 2024 Shida et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.