ABSTRACT
Introduction: The cause of respiratory distress by the novel corona virus is an acute hyper inflammatory “cytokine storm”. Besides glucocorticoids, tocilizumab, a recombinant monoclonal antibody, directed against the IL-6 receptor, has been used as a treatment modality with variable results [1]. Factors affecting poor response to tocilizumab remain unrecognized. We report a model to predict worse outcomes among patients with severe COVID-19 pneumonia treated with tocilizumab. Methods: In this retrospective study, patients with severe COVID 19 pneumonia admitted to the intensive care unit of our hospital who received Inj. tocilizumab besides the standard treatment between July 2020 to July 2021, were included. Electronic records of such patients were accessed and demographic, biochemical and outcome measures were recorded. Patients were divided into survivor cohort and mortality cohort. To predict mortality as an outcome, a multivariate logistic regression model was constructed. Results: Total of 101 patients were included, 71 in survival cohort and 30 in mortality cohort. Lactate dehydrogenase (LDH), neutrophil to lymphocyte ratio (NL ratio), creatine kinase myocardial band (CKMB) and partial pressure of oxygen to fraction of inspired oxygen ratio (PFR) on day of drug administration differed significantly among the two cohorts after correction for multiple comparison. However, on multivariable logistic regression analysis, a model incorporating LDH, NL ratio, pro-brain natriuretic peptide levels (ProBNP) and PFR best predicted mortality (Fig. 1). A nomogram was also created to estimate probability of mortality using the model parameters. Conclusions: LDH, ProBNP, NL ratio and PFR at Tocilizumab administration are independently associated with mortality. A model incorporating the combination of these parameters at admission can predict mortality among patients with severe COVID-19 pneumonia with good accuracy. (Figure Presented).
ABSTRACT
In this article, we analyze the change in anthropogenic seismic noise level within a frequency range of 4-14 Hz, through a survey of seismic stations in California, United States, New York City, United States, and Mexicali, Baja California, Mexico from early December 2019 to late April 2020. Our analysis shows that some stations recorded a drop in anthropogenic seismic noise during the COVID-19 pandemic, and the timing of the anthropogenic noise decrease typically correlates with the timing of a strict curtailment of personal and economic activity issued by the local government. In other locations, the drop in the anthropogenic seismic noise appears not to follow the lockdown timing perfectly.Duringour analysis,weobservedthatmanystations didnot recordadropduring the early stageofCOVID-19pandemic. Ofthe 19 stationsof the Southern California Seismic Network that were surveyed, we found that only five show a similar extent of drop in anthropogenic seismic noise comparable to the Christmas holiday break in 2019. This suggests that the human activity that caused seismic noise did not significantly reduce during the COVID-19 pandemic near most surveyed stations in southern California. A further analysis implies that the primary seismic noise source in southern California might be traffic, and the continuation of industrial traffic, such as cargo transportation, during the COVID-19 pandemic may be the reason why many stations did not record a noise drop. Our results show that the anthropogenic seismic noise recorded by seismic stations is capable of indicating human activity, and that thismetric is, particularly, powerful inmeasuring how localized communities initially responded to the COVID-19 pandemic. © 2021 Seismological Society of America. All rights reserved.