Fried et al., Clinical Infectious Disease,
doi:10.1093/cid/ciaa1268 (Peer Reviewed)
Patient Characteristics and Outcomes of 11,721 Patients with COVID19 Hospitalized Across the United States
Database analysis of 11,721 hospitalized patients, 4,232 on HCQ. Strong evidence for confounding by indication and compassionate use of HCQ. 24.9% of HCQ patients were on mechanical ventilation versus 12.2% control. Ventilation mortality was 70.5% versus 11.6%.
This study does not adjust for the differences in comorbid conditions and disease severity, and therefore does not make a conclusion. Unadjusted HCQ mortality was 24.8% versus control 19.6%. Adjusting for ventilation only gives us 17.7% HCQ versus 19.6% control (adjusting the HCQ group to have the same proportion of ventilation patients), RR 0.90. Hopefully authors can do a full adjustment analysis. Comorbidities may favor control, while patients remaining in the hospital (5.3%) may favor HCQ (other studies show faster resolution for HCQ patients).
Data inconsistencies have been found in this study, for example 99.4% of patients treated with HCQ were treated in urban hospitals, compared to 65% of untreated patients (Supplemental Table 3), while patients are distributed in a more balanced manner between teaching or not-teaching hospitals, as well as in the most urbanized (Northeast) and less urbanized (Midwest) regions of the United States [1].
Fried et al., 8/28/2020, retrospective, database analysis, USA, North America, peer-reviewed, 11 authors.
risk of death, 27.0% higher, RR 1.27, p < 0.001, treatment 1048 of 4232 (24.8%), control 1466 of 7489 (19.6%).
This study is excluded in meta analysis: excessive unadjusted differences between groups, substantial unadjusted confounding by indication likely.
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.