Evidence map›Paper›PMID 36636302›Full record

ArticleJournal of global infectious diseases

High Prevalence of Fungal and NDM-OXA Producing Gram-Negative Bacterial Superinfections in the Second Wave of Coronavirus Disease 2019 in India: Experience from a Dedicated Coronavirus Disease 2019 Hospital in North India.

Omika Katoch, Neha Sharad, Parul Singh, Sharad Srivastav, Richa Aggrawal, Rajesh Malhotra, Purva Mathur

Open access · goldAbstract read
In one paragraph

Article in Journal of global infectious diseases. 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
0.2field-weighted citation impact, top 40% 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  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

7 authors at 3 institutions in 1 country.

Omika KatochDepartment of Laboratory Medicine, AIIMS, New Delhi, India.
Neha SharadDepartment of Microbiology, AIIMS, New Delhi, India.
Parul SinghDepartment of Microbiology, AIIMS, New Delhi, India.
Sharad SrivastavDepartment of Laboratory Medicine, AIIMS, New Delhi, India.
Richa AggrawalDepartment of Anesthesia and Critical Care, AIIMS, New Delhi, India.
Rajesh MalhotraDepartment of Orthopedics, JPNATC, AIIMS, New Delhi, India.
Purva MathurDepartment of Laboratory Medicine, AIIMS, New Delhi, India.
All India Institute of Medical Sciences · INAll India Institute of Medical Sciences Bhopal · INAll India Institute of Medical Sciences Raipur · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: During the second wave of coronavirus disease 2019 (COVID-19), superinfection caused by fungus and multidrug-resistant bacteria worsened the severity of illness in COVID-19 patients. Limited studies from India reported the antimicrobial resistance pattern of secondary infections. In this study, we aim to study the epidemiology of pathogens causing superinfections and genotyping of Gram-negative isolates in COVID-19 patients. Methods: This retrospective study was conducted at a dedicated COVID-19 center, India. The identification of bacteria/fungi was done by Vitek2 Results: Of all the clinical samples, 45.4% of samples were cultured positive for secondary infections. Conclusions: The mortality rate among COVID-19 patients with secondary infection was significantly higher compared to the overall mortality rate in COVID-19 patients.

Indexed as

Antimicrobial resistancecoronavirus disease 2019MucorNDM

Identifiers

PMID36636302
PMCPMC9831212
OpenAlexW4312452427

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.