Using Causal Analysis to Measure Corticosteroids’ or Tociluzimab’s Effect on Mortality in Patients with COVID-19

Abstract

RATIONALE Severe inflammation is thought to drive disease severity in patients with COVID-19. Proposed treatments include corticosteroids and tocilizumab. Corticosteroids have demonstrated outcome benefit in patients with severe ARDS1,2. Tocilizumab (an interleukin-6 receptor antibody) is theorized to block the inflammatory cascade3. Variable steroid administration for COVID-19 illness stemmed from the possibility of increased viral shedding and limited survival benefit noted from other Coronavirus strains4. Lack of standardization for tocilizumab and corticosteroid administration leaves retrospective study prone to confounding and bias;therefore, we proposed using causal analysis to better determine treatment efficacy. In clinical observational studies, it is only possible to observe an individual participant under one treatment scenario, whereas the counterfactual (alternate) outcome is unknown5. Targeted Maximum Likelihood Estimator (TMLE) generates two matched subject populations using assigned weights to create a pseudo-population that models counterfactual outcomes while limiting confounding bias6. The causal analysis objective was to elucidate the Average Treatment Effect (ATE) for steroids and tocilizumab to reduce mortality in adult patients with COVID-19. METHODS A retrospective review of adult patients with COVID-19 admitted to an ICU between March 2020 and June 2020. The primary outcome was ICU mortality. We used TMLE with an ensemble of machine learning algorithms as the primary model7. The machine learning ensemble included Logistic Regression, a Neural Network, Naive Bayes, and XGboost8. The analysis was performed on corticosteroid and tocilizumab administration separately. The covariates included for the corticosteroid group were age, ethnicity, oxygen support level, and tocilizumab treatment. The tocilizumab analysis covariates included age, ethnicity, oxygen support, ECMO, and treatment with corticosteroids. Our primary metric was Average Treatment Effect (ATE). Secondary metrics are Odds Ratio and Risk Ratio. RESULTS Using TMLE, mortality analysis on the corticosteroids group (n=199) demonstrated an ATE (RD) of-0.259, 95% CI [-0.387 ,-0.13], risk ratio (RR) 0.512, 95% CI [0.364, 0.72] and odds ratio (OR) of 0.33 [0.188, 0.581]. The tocilizumab group (n=199) demonstrated an ATE (RD) of 0.104, 95% CI [-0.025, 0.232], RR 1.343, 95% CI [0.916, 1.97] and OR of 1.578 [0.885-2.813]. CONCLUSION Causal analysis is a useful analysis model to evaluate treatment effects. In this cohort of adult patients with COVID-19, tocilizumab did not demonstrate a mortality difference, whereas corticosteroids were associated with decreased mortality.