Evidence map›Paper›PMID 35236299›Full record

Observational studyBMC infectious diseases2022

Secondary infection in COVID-19 critically ill patients: a retrospective single-center evaluation.

Astrid De Bruyn, Stijn Verellen, Liesbeth Bruckers, Laurien Geebelen, Ina Callebaut, Ilse De Pauw, Björn Stessel, Jasperina Dubois

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 1 pooled it
17.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

51 citing papers in PubMed, 1 synthesis or guideline pooled it, 108 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Astrid De Bruyn *Department of Intensive Care and Anesthesiology, Jessa Hospital - Hasselt, 3500, Hasselt, Belgium.
Stijn Verellen *Department of Intensive Care and Anesthesiology, Jessa Hospital - Hasselt, 3500, Hasselt, Belgium.
Liesbeth BruckersI-BioStat, Data Science Institute, Hasselt University, Martelarenlaan 42, 3500, Hasselt, Belgium.
Laurien GeebelenDepartment of Intensive Care and Anesthesiology, Jessa Hospital - Hasselt, 3500, Hasselt, Belgium.
Ina CallebautDepartment of Intensive Care and Anesthesiology, Jessa Hospital - Hasselt, 3500, Hasselt, Belgium.
Ilse De PauwDepartment of Intensive Care and Anesthesiology, Jessa Hospital - Hasselt, 3500, Hasselt, Belgium.
Björn StesselDepartment of Intensive Care and Anesthesiology, Jessa Hospital - Hasselt, 3500, Hasselt, Belgium.
Jasperina DuboisDepartment of Intensive Care and Anesthesiology, Jessa Hospital - Hasselt, 3500, Hasselt, Belgium. jasperina.dubois@jessazh.be.
Jessa Hospital · BEHasselt University · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients infected with severe acute respiratory syndrome coronavirus (SARS-CoV-2) can develop severe illness necessitating intensive care admission. Critically ill patients are susceptible for the development of secondary bacterial infections. Due to a combination of virus- and drug-induced immunosuppression, critically ill patients with corona virus disease 2019 (COVID-19) may even have a higher risk of developing a secondary infection. These secondary infections can aggravate the severity of illness and increase the risk of death. Further research on secondary infections in COVID-19 patients is essential. Therefore, the objective of this study was to investigate the incidence and associated risk factors of secondary bacterial infections and to identify the most common groups of pathogens in critically ill COVID-19 patients.

methodsThis mono-center, retrospective observational cohort study was performed at the intensive care unit (ICU) of the Jessa Hospital, Hasselt, Belgium. All adult COVID-19 patients admitted to the ICU from 13th March 2020 until 17th October 2020, were eligible for inclusion in the study. Data from the resulting 116 patients were prospectively entered into a customized database. The resulting database was retrospectively reviewed to investigate three types of secondary bacterial infections (secondary pneumonia, bloodstream infections of unknown origin, catheter-related sepsis).

resultsOf 94 included patients, 68% acquired at least one of the studied secondary bacterial infections during their ICU stay. Almost two thirds of patients (65.96%, n = 62) acquired a secondary pneumonia, whereas 29.79% (n = 28) acquired a bacteremia of unknown origin and a smaller proportion of patients (14.89%, n = 14) acquired a catheter-related sepsis. Male gender (P = 0.05), diabetes mellitus (P = 0.03) and the cumulative dose of corticosteroids (P = 0.004) were associated with increased risk of secondary bacterial infection. The most common pathogens detected in the cultures of patients with secondary pneumonia were Gram-negative bacilli. Bacteremia of unknown origin and catheter-related sepsis were mostly caused by Gram-positive cocci.

conclusionThis study confirms that the incidence of secondary bacterial infections is very high in critically ill COVID-19 patients. These patients are at highest risk of developing secondary pneumonia. Male gender, a history of diabetes mellitus and the administration of corticosteroids were associated with increased risk of secondary bacterial infection.

Indexed as

CoinfectionCOVID-19AdultCritical IllnessHumansIntensive Care UnitsMaleRetrospective StudiesSARS-CoV-2Bacterial infectionCOVID-19Intensive Care Unit

Identifiers

PMID35236299
PMCPMC8890021
OpenAlexW4214824134

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.