Life-threatening COVID-19 is associated with strong inflammation, where an IL-6-driven cytokine storm appears to be a cornerstone for enhanced pathology. Nonetheless, the specific inhibition of such pathway has shown mixed outcomes. This could be due to variations in the dose of tocilizumab used, the stage in which the drug is administered or the severity of disease presentation. Thus, we performed a retrospective multicentric study in 140 patients with moderate to critical COVID-19, 79 of which received tocilizumab in variable standard doses (< 400 mg, 400–800 mg or > 800 mg), either at the viral (1–7 days post-symptom onset), early inflammatory (8–15) or late inflammatory (16 or more) stages, and compared it with standard treated patients. Mortality, reduced respiratory support requirements and pathology markers were measured. Tocilizumab significantly reduced the respiratory support requirements (OR 2.71, CI 1.37–4.85 at 95%) and inflammatory markers (OR 4.82, CI 1.4–15.8) of all patients, but mortality was only reduced (4.1% vs 25.7%, p = 0.03) when the drug was administered at the early inflammatory stage and in doses ranging 400–800 mg in severely-ill patients. Despite the apparent inability of Tocilizumab to prevent the progression of COVID-19 into a critical presentation, severely-ill patients may be benefited by its use in the early inflammatory stage and moderate doses.

Prognostic scales may help to optimize the use of hospital resources, which may be of prime interest in the context of a fast spreading pandemics. Nonetheless, such tools are underdeveloped in the context of COVID-19. In the present article we asked whether accurate prognostic scales could be developed to optimize the use of hospital resources. We retrospectively studied 467 files of hospitalized patients after COVID-19. The odds ratios for 16 different biomarkers were calculated, and those that were significantly associated were screened by a Pearson’s correlation, and such index was used to establish the mathematical function for each marker. The scales to predict the need for hospitalization, intensive-care requirement and mortality had enhanced sensitivities (0.91 CI 0.87–0.94; 0.96 CI 0.94–0.98; 0.96 CI 0.94–0.98; all with p < 0.0001) and specificities (0.74 CI 0.62–0.83; 0.92 CI 0.87–0.96 & 0.91 CI 0.86–0.94; all with p < 0.0001). Interestingly, when a different population was assayed, these parameters did not change considerably. These results show a novel approach to establish the mathematical function of a marker in the development of highly sensitive prognostic tools, which in this case, may aid in the optimization of hospital resources. An online version of the three algorithms can be found at: http://benepachuca.no-ip.org/covid/index.php