Evidence map›Paper›PMID 38249419›Full record

ArticleFrontiers in public health2023

Clinical features of omicron SARS-CoV-2 variants infection associated with co-infection and ICU-acquired infection in ICU patients.

Dong-Jie Li, Can-Can Zhou, Fang Huang, Fu-Ming Shen, Ying-Chuan Li

Abstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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

5 authors.

Dong-Jie Li *Department of Pharmacy, Shanghai Tenth People's Hospital, Shanghai, China.
Can-Can Zhou *Department of Pharmacy, Shanghai Tenth People's Hospital, Shanghai, China.
Fang HuangDepartment of Pharmacy, Shanghai Tenth People's Hospital, Shanghai, China.
Fu-Ming ShenDepartment of Pharmacy, Shanghai Tenth People's Hospital, Shanghai, China.
Ying-Chuan LiDepartment of Critical Care Medicine, Shanghai Tenth People's Hospital, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Although the decreasing rate of hospital admission in the omicron wave has led countries to loosen control, still the patients requires ICU admission. It is common for viral respiratory infections to be co-infected with bacteria. However, the difference between co-infection and ICU-acquired infection on their clinical characteristics and outcomes during the Omicron wave was little reported. Methods: Clinical and microbiological data were collected from ICU patients with omicron infection between April 1st, 2022, and May 31th, 2022 and a comprehensive comparative study of the clinical characteristics and endpoint were conducted. Results: The Omicron SARS-CoV-2 variants-infected patients requiring intensive care had high rates of co-infection (42.55%). Additionally, the ICU COVID-19 patients with co-infection showed more severe clinical features compared to those with ICU-acquired infection. Furthermore, Multivariate Cox analysis demonstrated that co-infection (hazard ratio: 4.670, Conclusion: Co-infection is prevalent among patients requiring intensive care and is linked to unfavorable outcomes in the Omicron wave. Consequently, more attention may be needed for the empirical antibacterial treatment in ICU patients within the COVID-19 Omicron variant, especially anti-fungi.

Indexed as

CoinfectionCOVID-19HumansIntensive Care UnitsSARS-CoV-2Candida appco-infectionCOVID-19ICU-acquired infectionOmicron

Identifiers

PMID38249419
PMCPMC10796448

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.