ABSTRACT
Background: The use of dexamethasone improved the outcome among hospitalized patients with Covid-19 since mid-2020 significantly (Horby et al.). However, there remain concerns about an increase of bacterial and fungal growth especially in critical ill patients. Aims and objectives: Aim of our study was to identify the bacterial and fungal pathogens dependent on the use of dexamethasone as a treatment of COVID-19 patients admitted to the intensive care unit (ICU). Studies show that bacterial and fungal co-infections raised during the pandemic due to increased empirical antibiotic use for COVID-19 pneumonia. The effect of dexamethasone has not been in focus. Method(s): For this study, we included all patients who had a lab-confirmed SARS-CoV-2 infection treated on the ICU. The clinical data was collected onsite in 20 hospitals of the German Helios-network between February 2020 and March 2021 and aligned with corresponding claims data. We performed a univariate and multivariate analyses to identify the impact of dexamethasone versus no dexamethasone on the pathogen profile in bloodstream and respiratory infections. Result(s): Out of 1 776 patients included, 1070 were treated with dexamethasone. Those patients were more likely to have an infection with Staphyloccus spp., Candida spp., E. coli, Klebsiella spp. and multidrug resistant bacteria (MRE) compared to patients without dexamethasone treatment. However, no change in the absolute number of detected pathogens was observed. Conclusion(s): Dexamethasone leads to a shift of bacterial and fungal pathogens in ICU-treated COVID-19 patients. This shift should be considered when empirical antibiotic therapy is administered.
ABSTRACT
Background. Numerous predictive clinical scores with varying discriminatory performance have been developed in the context of the current coronavirus disease 2019 (COVID-19) pandemic. To support clinical application, we test the transferability of the frequently applied 4C mortality score (4C score) to the German prospective Cross-Sectoral Platform (SUEP) of the National Pandemic Cohort Network (NAPKON) compared to the non COVID-19 specific quick sequential organ failure assessment score (qSOFA). Our project aims to externally validate these two scores, stratified for the most prevalent variants of concerns (VOCs) of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) in Germany. Methods. A total of 685 adults with polymerase chain reaction (PCR)-detected SARS-CoV-2 infection were included from NAPKON-SUEP. Patients were recruited from 11/2020 to 03/2022 at 34 university and non-university hospitals across Germany. Missing values were complemented using multiple imputation. Predictive performance for in-hospital mortality at day of baseline visit was determined by area under the curve (AUC) with 95%-confidence interval (CI) stratified by VOCs of SARS-CoV-2 (alpha, delta, omicron) (Figure 1). Figure 1: Study flow chart with inclusion criteria and methodological workflow. Results. Preliminary results suggest a high predictive performance of the 4C score for in-hospital mortality (Table 1). This applies for the overall cohort (AUC 0.813 (95%CI 0.738-0.888)) as well as the VOC-strata (alpha: AUC 0.859 (95%CI 0.748-0.970);delta: AUC 0.769 (95%CI 0.657-0.882);omicron: AUC 0.866 (95%CI 0.724-1.000)). The overall mortality rates across the defined 4C score risk groups are 0.3% (low), 3.2% (intermediate), 11.6% (high), and 49.5% (very high). The 4C score performs significantly better than the qSOFA (Chi2-test: p=0.001) and the qSOFA does not seem to be a suitable tool in this context. Table 1: Discriminatory performance of the 4C Mortality Score and the qSOFA score within the validation cohort NAPKON-SUEP stratified by the Variant of Concerns of SARS-CoV- 2. Conclusion. Despite its development in the early phase of the pandemic and improved treatment, external validation of the 4C score in NAPKON-SUEP indicates a high predictive performance for in-hospital mortality across all VOCs. However, since the qSOFA was not specifically designed for this predictive issue, it shows low discriminatory performance, as in other validation studies. Any interpretations regarding the omicron stratum are limited due to the sample size.