Mark Stewart1, Carla Rodriguez-Watson2, Adem Albayrak3, Julius Asubonteng4, Andrew Belli5, Thomas Brown6, Kelly Cho7,8, Ritankar Das9, Elizabeth Eldridge3, Nicolle Gatto10, Alice Gelman3, Hanna Gerlovin7, Stuart L Goldberg11, Eric Hansen5, Jonathan Hirsch6, Yuk-Lam Ho7, Andrew Ip11, Monika Izano6, Jason Jones3, Amy C Justice12,13, Reyna Klesh14, Seth Kuranz15, Carson Lam9, Qingqing Mao9, Samson Mataraso9, Robertino Mera4, Daniel C Posner7, Jeremy A Rassen10, Anna Siefkas9, Andrew Schrag6, Georgia Tourassi16, Andrew Weckstein10, Frank Wolf6, Amar Bhat2, Susan Winckler2, Ellen V Sigal1,2, Jeff Allen1. 1. Friends of Cancer Research, Washington, District of Columbia, United States of America. 2. Reagan-Udall Foundation for the FDA, Washington, District of Columbia, United States of America. 3. Health Catalyst, Salt Lake City, Utah, United States of America. 4. Gilead Science, Inc. Foster City, California, United States of America. 5. COTA, Inc., Boston, Massachusetts, United States of America. 6. Syapse, San Francisco, California, United States of America. 7. Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, Massachusetts, United States of America. 8. Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America. 9. Dascena, Oakland, California, United States of America. 10. Aetion, New York, New York, United States of America. 11. Division of Outcomes and Value Research, John Theurer Cancer Center at Hackensack University Medical Center, Hackensack, New Jersey, United States of America. 12. VA Connecticut Healthcare System, West Haven, Connecticut, United States of America. 13. Yale University Schools of Medicine and Public Health, New Haven, Connecticut, United States of America. 14. HealthVerity, Philadelphia, Pennsylvania, United States of America. 15. TriNetX, Cambridge, Massachusetts, United States of America. 16. National Center for Computational Sciences Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America.
Abstract
BACKGROUND: The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. METHODS: Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. RESULTS: Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. CONCLUSION: Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.
BACKGROUND: The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. METHODS: Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. RESULTS: Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19patients. No treatment groups appeared to have an elevated risk of adverse events. CONCLUSION: Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.
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