Evidence map›Paper›PMID 33639862›Full record

ArticleBMC infectious diseases2021

A multicenter analysis of the clinical microbiology and antimicrobial usage in hospitalized patients in the US with or without COVID-19.

Laura Puzniak, Lyn Finelli, Kalvin C Yu, Karri A Bauer, Pamela Moise, Carisa De Anda, Latha Vankeepuram, Aryana Sepassi, Vikas Gupta

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in BMC infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
5.3field-weighted citation impact, top 4% 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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.

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  6. The impact of COVID on bacterial sepsis.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2023
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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

9 authors at 2 institutions in 1 country.

Laura PuzniakMerck & Co., Inc., Kenilworth, NJ, USA. Laura.Puzniak@merck.com.
Lyn FinelliMerck & Co., Inc., Kenilworth, NJ, USA.
Kalvin C YuBecton, Dickinson and Company, Franklin Lakes, NJ, USA.
Karri A BauerMerck & Co., Inc., Kenilworth, NJ, USA.
Pamela MoiseMerck & Co., Inc., Kenilworth, NJ, USA.
Carisa De AndaMerck & Co., Inc., Kenilworth, NJ, USA.
Latha VankeepuramBecton, Dickinson and Company, Franklin Lakes, NJ, USA.
Aryana SepassiBecton, Dickinson and Company, Franklin Lakes, NJ, USA.
Vikas GuptaBecton, Dickinson and Company, Franklin Lakes, NJ, USA.
Merck & Co., Inc., Rahway, NJ, USA (United States) · USBecton Dickinson (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPast respiratory viral epidemics suggest that bacterial infections impact clinical outcomes. There is minimal information on potential co-pathogens in patients with coronavirus disease-2019 (COVID-19) in the US. We analyzed pathogens, antimicrobial use, and healthcare utilization in hospitalized US patients with and without severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2).

methodsThis multicenter retrospective study included patients with > 1 day of inpatient admission and discharge/death between March 1 and May 31, 2020 at 241 US acute care hospitals in the BD Insights Research Database. We assessed microbiological testing data, antimicrobial utilization in admitted patients with ≥24 h of antimicrobial therapy, and length of stay (LOS).

resultsA total of 141,621 patients were tested for SARS-CoV-2 (17,003 [12.0%] positive) and 449,339 patients were not tested. Most (> 90%) patients tested for SARS-CoV-2 had additional microbiologic testing performed compared with 41.9% of SARS-CoV-2-untested patients. Non-SARS-CoV-2 pathogen rates were 20.9% for SARS-CoV-2-positive patients compared with 21.3 and 27.9% for SARS-CoV-2-negative and -untested patients, respectively. Gram-negative bacteria were the most common pathogens (45.5, 44.1, and 43.5% for SARS-CoV-2-positive, -negative, and -untested patients). SARS-CoV-2-positive patients had higher rates of hospital-onset (versus admission-onset) non-SARS-CoV-2 pathogens compared with SARS-CoV-2-negative or -untested patients (42.4, 22.2, and 19.5%, respectively), more antimicrobial usage (68.0, 45.2, and 25.1% of patients), and longer hospital LOS (mean [standard deviation (SD)] of 8.6 [11.4], 5.1 [8.9], and 4.2 [8.0] days) and intensive care unit (ICU) LOS (mean [SD] of 7.8 [8.5], 3.6 [6.2], and 3.6 [5.9] days). For all groups, the presence of a non-SARS-CoV-2 pathogen was associated with increased hospital LOS (mean [SD] days for patients with versus without a non-SARS-CoV-2 pathogen: 13.7 [15.7] vs 7.3 [9.6] days for SARS-CoV-2-positive patients, 8.2 [11.5] vs 4.3 [7.9] days for SARS-CoV-2-negative patients, and 7.1 [11.0] vs 3.9 [7.4] days for SARS-CoV-2-untested patients).

conclusionsDespite similar rates of non-SARS-CoV-2 pathogens in SARS-CoV-2-positive, -negative, and -untested patients, SARS-CoV-2 was associated with higher rates of hospital-onset infections, greater antimicrobial usage, and extended hospital and ICU LOS. This finding highlights the heavy burden of the COVID-19 pandemic on healthcare systems and suggests possible opportunities for diagnostic and antimicrobial stewardship.

Indexed as

AdultAgedAged, 80 and overAnti-Infective AgentsCOVID-19Cross InfectionFemaleGram-Negative BacteriaHospitalizationHumansLength of StayMaleMiddle AgedRetrospective StudiesSARS-CoV-2Anti-Infective AgentsAntibioticsCoinfectionCOVID-19EpidemiologyPathogensSARS-CoV-2

Identifiers

PMID33639862
PMCPMC7910773
OpenAlexW3135686629

What OpenQuestion holds

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LicenceCC BY
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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.