Co-infection and ICU-acquired infection in COIVD-19 ICU patients: a secondary analysis of the UNITE-COVID data set

Andrew Conway Morris, Katharina Kohler, Thomas De Corte, Ari Ercole, Harm-Jan De Grooth, Paul W G Elbers, Pedro Povoa, Rui Morais, Despoina Koulenti, Sameer Jog, Nathan Nielsen, Alasdair Jubb, Maurizio Cecconi, Jan De Waele, ESICM UNITE COVID investigators

Abstract

Background: The COVID-19 pandemic presented major challenges for critical care facilities worldwide. Infections which develop alongside or subsequent to viral pneumonitis are a challenge under sporadic and pandemic conditions; however, data have suggested that patterns of these differ between COVID-19 and other viral pneumonitides. This secondary analysis aimed to explore patterns of co-infection and intensive care unit-acquired infections (ICU-AI) and the relationship to use of corticosteroids in a large, international cohort of critically ill COVID-19 patients.

Methods: This is a multicenter, international, observational study, including adult patients with PCR-confirmed COVID-19 diagnosis admitted to ICUs at the peak of wave one of COVID-19 (February 15th to May 15th, 2020). Data collected included investigator-assessed co-infection at ICU admission, infection acquired in ICU, infection with multi-drug resistant organisms (MDRO) and antibiotic use. Frequencies were compared by Pearson's Chi-squared and continuous variables by Mann-Whitney U test. Propensity score matching for variables associated with ICU-acquired infection was undertaken using R library MatchIT using the "full" matching method.

Results: Data were available from 4994 patients. Bacterial co-infection at admission was detected in 716 patients (14%), whilst 85% of patients received antibiotics at that stage. ICU-AI developed in 2715 (54%). The most common ICU-AI was bacterial pneumonia (44% of infections), whilst 9% of patients developed fungal pneumonia; 25% of infections involved MDRO. Patients developing infections in ICU had greater antimicrobial exposure than those without such infections. Incident density (ICU-AI per 1000 ICU days) was in considerable excess of reports from pre-pandemic surveillance. Corticosteroid use was heterogenous between ICUs. In univariate analysis, 58% of patients receiving corticosteroids and 43% of those not receiving steroids developed ICU-AI. Adjusting for potential confounders in the propensity-matched cohort, 71% of patients receiving corticosteroids developed ICU-AI vs 52% of those not receiving corticosteroids. Duration of corticosteroid therapy was also associated with development of ICU-AI and infection with an MDRO.

Conclusions: In patients with severe COVID-19 in the first wave, co-infection at admission to ICU was relatively rare but antibiotic use was in substantial excess to that indication. ICU-AI were common and were significantly associated with use of corticosteroids. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021).

Conflict of interest statement

ACM sits on the scientific advisory board of Cambridge Infection Diagnostics. PP received fees for consulting and lectures to Gilead, Pfizer, MSD and Sanofi. NN has received consulting honoraria from Johnson & Johnson and serves on the scientific advisory board of Adrenomed. The other authors declare that they have no competing interests.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Distribution of values of inflammatory markers within the first 24 h of admission between patients with and without identified co-infection at ICU admission
Fig. 2
Fig. 2
Forest plot of steroid treatment by centre: Colour and dot size represent the percentage of overall patients that received steroid treatment, the forest plot shows the median and IQR of length of steroid treatment. Centres are ordered by the median length of time patients receive steroids
Fig. 3
Fig. 3
Propensity score (PS) matching for corticosteroid matching parameters compared to the input cohort. A density plots and histograms showing the effect of PS matching on distributions. B Covariance balance ‘love’ plot illustrating the effect of PS on standardised mean difference. C Numeric summary statistics following PS matching. Reporting percentages for categorical variables, mean for non-skewed parameters, and median for skewed parameters. Last two entries below the thick line are outcome measures and were not used for matching

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