School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Document Type

Article

Publication Date

2-29-2024

Abstract

A cohort of 30,423 Covid-19 patients treated between March 2020 and December 2021 at the IHU-Méditerranée Infection in Marseille (France) was retrospectively analyzed in terms of treatment attempted and disease worsening factors to quantify efficacy with respect to the composite endpoint of transfer to intensive care unit or death, within a couple of months (56 days) from admission. Within limitations of the data and of the models, after adjustment for sampling biases, multivariate logistic regression analyses were performed to determine unadjusted and adjusted odds ratios (ORs) for the subset of patients having received the combined treatment hydroxychloroquine plus azithromycin (HCQ-AZ) or no specific treatment (i.e. no HCQ, no AZ and no ivermectin (IVM)) (24,943 patients). An efficacy of 58% in reducing the risk of ICU transfer and death was measured (HCQ-AZ unadjusted OR = 0.499; 95%CI = [0.343; 0.727], p < 0.001) (HCQ-AZ adjusted OR = 0.419; 95%CI = [0.327; 0.539], p < 0.001). AZ without HCQ but associated with ivermectin in 31.3% of the cases was significantly active as well with respect to no specific treatment, with a measured efficacy of 27% (unadjusted OR = 0.720, 95%CI = [0.574; 0.905] p = 0.005 and adjusted OR = 0.727, 95%CI = [0.608; 0.870] p < 0.001). Interactions between HCQ-AZ and the model covariates were systematically explored. No interaction between HCQ-AZ treatment and vaccination was detected. Statistically significant favorable interactions were detected between HCQ-AZ treatment and male sex, age categories ≥ 50 years, the UK variant and when the variant was not determined, obesity, chronic obstructive pulmonary disease (COPD), cancer, immunodeficiency, confirming the high efficacy of this early treatment. No statistically significant unfavorable interaction of HCQ-AZ with any covariate was detected. Limitations of the models and their implications for the results are discussed extensively.

Comments

Creative Commons Attribution License version 4.0

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

DOI

10.26502/ami.936500153

034-Raoult-data-analysis-I-supp.pdf (1334 kB)
Supplementary material

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