Pharmacoepidemiology, Machine Learning and COVID-19: An intent-to-treat analysis of hydroxychloroquine, with or without azithromycin, and COVID-19 outcomes amongst hospitalized US Veterans

6/24 Late treatment study

Gerlovin et al., American Journal of Epidemiology, doi:10.1093/aje/kwab183 (Peer Reviewed)
Pharmacoepidemiology, Machine Learning and COVID-19: An intent-to-treat analysis of hydroxychloroquine, with or without azithromycin, and COVID-19 outcomes amongst hospitalized US Veterans

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Retrospective 1,769 hospitalized patients in the USA showing no significant differences for HCQ, and higher intubation for HCQ+AZ.

Gerlovin et al., 6/24/2021, retrospective, USA, North America, peer-reviewed, 21 authors.

risk of death, 22.0% higher, RR 1.22, p = 0.18, treatment 90 of 429 (21.0%), control 141 of 770 (18.3%), adjusted per study, HCQ+AZ.

risk of death, 21.0% higher, RR 1.21, p = 0.33, treatment 49 of 228 (21.5%), control 141 of 770 (18.3%), adjusted per study, HCQ.

risk of mechanical ventilation, 55.0% higher, RR 1.55, p = 0.02, treatment 64 of 429 (14.9%), control 69 of 770 (9.0%), adjusted per study, HCQ+AZ.

risk of mechanical ventilation, 33.0% higher, RR 1.33, p = 0.25, treatment 32 of 228 (14.0%), control 69 of 770 (9.0%), adjusted per study, HCQ.

Effect extraction follows pre-specified rules prioritizing more serious outcomes. For an individual study the most serious outcome may have a smaller number of events and lower statistical signficance, however this provides the strongest evidence for the most serious outcomes when combining the results of many trials.

All 344 studies Meta Analysis

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