Methods
We built a network of six academic hospitals in California (Stanford
University)/USA, Frankfurt (Goethe University), Giessen (Justus Liebig
University) and Marburg (Philipps University)/Germany, and Moscow
(Clinical Hospital 52 in collaboration with Sechenov University)/Russia.
The German and US participating centers collected clinical and
laboratory data for all patients hospitalized due to COVID-19 since the
beginning of the pandemic until end of 2020 and September 2020
respectively. Moscow delivered case-control type of data and included
patients hospitalized during 23.03.-16.05.2020. Reported comorbidities
reported in the present study were based on the 2020 International
Classification of Diseases-10th Revision codes as
described in Supplementary Table 1. Laboratory values were calculated
and expressed in the same units for direct comparison whenever
applicable.
Statistical Analysis
All statistical analyses were conducted in R (R Core Team (2021). R: A
language and environment for statistical computing. R Foundation for
Statistical Computing, Vienna, Austria. URL https://www.R-project.org/;
version 4.1.2).
The prevalence of asthma patients among the hospitalized COVID-19
patients was calculated from the number of hospitalized asthma patients
divided by the reported number of total hospitalized patients. While
Germany and Stanford data correspond to a cohort of all hospitalized
COVID-19 patients at these hospitals in the indicated time interval,
Moscow data corresponds to all asthma cases and a set of control cases
without asthma. The prevalence in Moscow was calculated based on all
hospitalized COVID-19 cases in the Moscow hospitals (4,549). To compare
the prevalence of asthma to the corresponding prevalences in the general
population, a binomial test was used. The 95% confidence interval was
calculated by the method of Clopper-Pearson .
To test whether asthma patients were over-represented among the ICU
admitted patients we used Fisher’s exact test and show the estimated
odds ratios and 95% confidence intervals between Odds
(ICU|asthma) and Odds (ICU|no asthma) reported by the
R-function fisher test.
To test whether any of the additional preconditions are over-represented
in the asthma compared to the non-asthma patients we used Fisher’s exact
test. We corrected for multiple testing using the method of
Benjamin-Hochberg and report significant differences at a false
discovery rate of 10%.
To test whether the number of additional preconditions is different
between patients with asthma and without asthma we used a Wilcoxon rank
sum test. To adjust for confounders explaining the overrepresentation of
asthmatics in Stanford, we performed logistic regression using the
center, age group, sex and the 11 comorbidities to predict whether a
patient was asthmatic. This analysis was restricted to the centers
Moscow and Stanford, for which we had microdata available. From the
so-fitted model, we calculated the Odds to be asthmatic given that the
patient was from Stanford, female and had no comorbidity over the
different age groups.
To identify possible confounders for the laboratory measurements a
linear regression model was fitted with the predictors age group, sex,
preconditions (including asthma). These analyses revealed a strong and
significant effect of the precondition COPD and the eosinophil count at
admission, during the hospital stay, and at discharge. Thus, we removed
patients that had a precondition of COPD and recalculated averages and
standard errors.