Evidence map›Paper›PMID 37128208›Full record

SynthesisPeerJ2023

Bacterial coinfection and antibiotic resistance in hospitalized COVID-19 patients: a systematic review and meta-analysis.

Ruhana Che Yusof, Mohd Noor Norhayati, Yacob Mohd Azman

Open access · goldAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
2.7field-weighted citation impact, top 8% 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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Observational
  5. Co-Infection of SARS-CoV-2 andDiagnostics (Basel, Switzerland) · 2024
    Review
  6. Microorganisms · 2023
    Article
  7. Article
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

3 authors at 3 institutions in 1 country.

Ruhana Che YusofDepartment of Medicine, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia.ORCID 0000-0002-6535-2969
Mohd Noor NorhayatiDepartment of Family Medicine, Universiti Sains Malaysia, Kubang Kerian, Kelantan, Malaysia.ORCID 0000-0002-6372-1476
Yacob Mohd AzmanMedical Development Division, Ministry of Health, Putrajaya, Malaysia.
Hospital Universiti Sains Malaysia · MYMinistry of Health · MYUniversity of Malaya · MY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: There were a few studies on bacterial coinfection in hospitalized COVID-19 patients worldwide. This systematic review aimed to provide the pooled prevalence of bacterial coinfection from published studies from 2020 to 2022. Methods: Three databases were used to search the studies, and 49 studies from 2,451 identified studies involving 212,605 COVID-19 patients were included in this review. Results: The random-effects inverse-variance model determined that the pooled prevalence of bacterial coinfection in hospitalized COVID-19 patients was 26.84% (95% CI [23.85-29.83]). The pooled prevalence of isolated bacteria for Conclusion: All the prevalences were considered as low. However, effective management and prevention of the infection should be considered since these coinfections have a bad impact on the morbidity and mortality of patients.

Indexed as

CoinfectionCOVID-19Methicillin-Resistant Staphylococcus aureusBacteriaCarbapenemsDrug Resistance, BacterialEscherichia coliHumansCarbapenemsAntibiotic-resistant bacteriaBacterial coinfectionCOVID-19PrevalenceSystematic review

Identifiers

PMID37128208
PMCPMC10148641
OpenAlexW4367054749

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.