Background: The effectiveness of interleukin-6 inhibitors (IL6i) in ameliorating Covid-19 disease remains uncertain.
Methods: We analyzed data for patients aged ≥18 years admitted with a positive SARS-CoV-2 PCR test at four safety-net hospital systems with diverse populations and high rates of medical comorbidities in three different regions of the United States. We used inverse probability of treatment weighting via machine learning for confounding adjustment by demographics, comorbidities, and disease severity markers. We estimated the average treatment effect, the odds of IL6i effect on in-hospital mortality from COVID-19, using a logistic marginal structural model.
Results: Of the 516 patients in this study, 104 (20.1%) received IL6i. The estimate of the average treatment effect adjusted for confounders suggested a 37% reduction in the odds of in-hospital mortality in those who received IL-6i, compared with those who did not, though the confidence interval included the null value of 1 (odds ratio = 0.63, 95% CI: 0.29, 1.38). A sensitivity analysis suggested that potential unmeasured confounding would require a minimum odds ratio of 2.55 to nullify our estimated IL-6i effect size.
Conclusions: Despite low precision, our findings suggested a relatively large effect size of IL6i in reducing the odds of COVID-19 related in-hospital mortality.
Keywords: COVID-19; Cytokine release syndrome; Interleukin 6 inhibitors.
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