Collection of seven retrospective database analyses in the USA, showing higher mortality with treatment (not statistically significant).
Time based confounding is very likely because HCQ became highly controversial and usage dramatically declined over the time covered, while overall treatment protocols during this period improved dramatically, i.e., more control patients likely come later in the period when treatment protocols were greatly improved.
This study includes anyone PCR+ during or prior to their visit, and anyone with ICD-10 COVID-19 codes which includes asymptomatic PCR+ patients, therefore some patients in the control groups may be asymptomatic with regards to SARS-CoV-2, but in the hospital for another reason.
Authors do not mention the possibility of any of these likely confounding factors.
This study is excluded in the after exclusion results of meta
analysis:
substantial unadjusted
confounding by indication likely; substantial
confounding by time likely due to declining usage over the early stages of the pandemic when overall treatment protocols improved dramatically; includes PCR+ patients that may be asymptomatic for COVID-19 but in hospital for other reasons.
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risk of death, 18.0% higher, RR 1.18, p = 0.27, treatment 90 of 429 (21.0%), control 141 of 737 (19.1%), adjusted per study, VA, HCQ+AZ.
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risk of death, 1.0% lower, RR 0.99, p = 0.95, treatment 66 of 578 (11.4%), control 188 of 1,243 (15.1%), adjusted per study, TriNetX, HCQ+AZ.
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risk of death, 129.9% higher, RR 2.30, p < 0.001, treatment 32 of 108 (29.6%), control 33 of 256 (12.9%), Synapse, HCQ+AZ.
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risk of death, 9.0% higher, RR 1.09, p = 0.65, treatment 212 of 1,157 (18.3%), control 203 of 1,101 (18.4%), NNT 873, adjusted per study, Health Catalyst, HCQ+AZ.
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risk of death, 90.0% higher, RR 1.90, p = 0.09, treatment 46 of 208 (22.1%), control 47 of 1,334 (3.5%), adjusted per study, Dascena, HCQ+AZ.
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risk of death, 16.0% higher, RR 1.16, p = 0.26, treatment 428 of 1,711 (25.0%), control 123 of 688 (17.9%), adjusted per study, COTA/HMH, HCQ+AZ.
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risk of mechanical ventilation, 29.0% higher, RR 1.29, p = 0.09, treatment 48 of 305 (15.7%), control 95 of 1,302 (7.3%), adjusted per study, Aetion, HCQ.
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Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
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Stewart et al., 17 Mar 2021, retrospective, USA, peer-reviewed, 37 authors.
COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients
Mark Stewart, Carla Rodriguez-Watson, Adem Albayrak, Julius Asubonteng, Andrew Belli, Thomas Brown, Kelly Cho, Ritankar Das, Elizabeth Eldridge, Nicolle Gatto, Alice Gelman, Hanna Gerlovin, Stuart L Goldberg, Eric Hansen, Jonathan Hirsch, Yuk-Lam Ho, Andrew Ip, Monika Izano, Jason Jones, Amy C Justice, Reyna Klesh, Seth Kuranz, Carson Lam, Qingqing Mao, Samson Mataraso, Robertino Mera, Daniel C Posner, Jeremy A Rassen, Anna Siefkas, Andrew Schrag, Georgia Tourassi, Andrew Weckstein, Frank Wolf, Amar Bhat, Susan Winckler, Ellen V Sigal, Jeff Allen
PLOS ONE, doi:10.1371/journal.pone.0248128
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
Across all datasets and treatment groups, the most prominent pre-existing conditions tended to be any cardiovascular disease, hypertension, diabetes and obesity. Overall, obesity was more prevalent among the HCQ treatment groups than in the neither group. For most data partners, the proportion of patients treated with any of these comorbidities was lower or no different in the HCQ groups than in the neither group-with the exception of Syapse, which was a cancer cohort. There are several limitations to this study that must be acknowledged. First, despite our goal of carrying out the same set of analyses on multiple datasets, analyses could not be carried COTA/HMH 516 36 ( 7 ) 0 (0) 0 (0) 12 ( 2 ) 0 (0) 5 ) 53 ( 5 ) 36 ( 4 ) 36 ( 4 ) � Adverse event data from discharge diagnoses.
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