Corticosteroids and RCTs against the supposed undervaluation of real data evidence

Abstract

© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Dear Editor The alleged supremacy of the Randomized control trials (RCTs) is based on the possibility of causal inference. But, is this true? In order to define what we mean by a causal effect, for each subject we assume the existence of the potential outcomes Yα = 0 and Yα = 1 corresponding to what value the outcome would take if we do not apply the intervention (α = 0) or we apply the intervention (α = 1), respectively. For an individual, it is assumed that the intervention has a causal effect whenever Yα = 0 different to Yα = 1, that is, the outcome would take a different value depending on whether the individual is given the intervention or not. To calculate the causal effect of the intervention we would need to somehow obtain or discover the values Yα = 0 and Yα = 1. Consider that “A” denote a random variable indicating whether an individual receives the intervention (A = 1) or not (A = 0), and Y a random variable for the observed outcome. If a particular individual received the intervention, the observed value is Y = Yα = 1, but for the potential outcome value Yα = 0 is unknown. The unobserved outcome is called the “counterfactual” outcome and a causal inference is not possible to obtain [1]. According to the RECOVERY trial results, an adaptive RCT [2], during the second pandemic wave, corticosteroid use was generalized in all critical COVID-19 patients. However, the role of corticosteroids in the treatment of COVID-19 remains controversial. A recent study [3] comparing first versus second wave reported that, despite of the systematic and early administration of glucocorticoids in the second wave, the ICU mortality (50% vs. 52%, p = 0.96) and of ICU length of stay did not differ between the two waves. Late complications, as well as medium-term evolution, were not evaluated in the RECOVERY study [2]. A recent research letter [4] found that corticosteroid use, and tocilizumab treatment were associated with ventilator-associated pneumonia (VAP). Should physicians continue to ignore the results of well-adjusted observational studies in favor of the results of an adaptive RCT with many inconsistencies? We are convinced that steroid treatment can be effective but there are different patients’ phenotypes and their use should be re-evaluated [5]. Possibly, the future of research and clinical practice is not conceived as a confrontation between RCT and observational studies, but rather as the sum of knowledge between RCTs and its clinical application.