Evidence map›Paper›PMID 37771748›Full record

ArticleAntimicrobial stewardship & healthcare epidemiology : ASHE2023

Impact of the Severe acute respiratory syndrome coronavirus 2 pandemic on mortality associated with healthcare-associated infections.

Andrew Atkinson, Katelin B Nickel, John M Sahrmann, Dustin Stwalley, Erik R Dubberke, Kathleen McMullen, Jonas Marschall, Margaret A Olsen, Jennie H Kwon, Jason P Burnham

Open access · goldAbstract read
In one paragraph

Article in Antimicrobial stewardship & healthcare epidemiology : ASHE, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 32% 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

2 citing papers in PubMed, 1 citations in OpenAlex.

  1. Article
  2. 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

10 authors at 2 institutions in 1 country.

Andrew AtkinsonDivision of Infectious Diseases, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
Katelin B NickelDivision of Infectious Diseases, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.ORCID https://orcid.org/0000-0003-4116-512X
John M SahrmannDivision of Infectious Diseases, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.ORCID https://orcid.org/0000-0001-6000-0908
Dustin StwalleyInstitute for Informatics, Washington University in St. Louis, St. Louis, MO, USA.ORCID https://orcid.org/0000-0002-6427-0590
Erik R DubberkeDivision of Infectious Diseases, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
Kathleen McMullenInfection Prevention, Mercy Hospital, St. Louis, MO, USA.
Jonas MarschallDivision of Infectious Diseases, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.ORCID https://orcid.org/0000-0002-0052-3210
Margaret A OlsenDivision of Infectious Diseases, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.ORCID https://orcid.org/0000-0001-7070-9320
Jennie H Kwon *Division of Infectious Diseases, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
Jason P Burnham *Division of Infectious Diseases, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.ORCID https://orcid.org/0000-0002-4777-3006
Washington University in St. Louis · USMercy Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To determine the relationship between severe acute respiratory syndrome coronavirus 2 infection, hospital-acquired infections (HAIs), and mortality. Design: Retrospective cohort. Setting: Three St. Louis, MO hospitals. Patients: Adults admitted ≥48 hours from January 1, 2017 to August 31, 2020. Methods: Hospital-acquired infections were defined as those occurring ≥48 hours after admission and were based on positive urine, respiratory, and blood cultures. Poisson interrupted time series compared mortality trajectory before (beginning January 1, 2017) and during the first 6 months of the pandemic. Multivariable logistic regression models were fitted to identify risk factors for mortality in patients with an HAI before and during the pandemic. A time-to-event analysis considered time to death and discharge by fitting Cox proportional hazards models. Results: Among 6,447 admissions with subsequent HAIs, patients were predominantly White (67.9%), with more females (50.9% vs 46.1%, Conclusions: In three Midwestern hospitals, patients with concurrent HAIs and COVID-19 were more likely to die if they were Black, elderly, and had certain chronic comorbidities.

Indexed as

COVID-19healthcare-associated infectionsSARS-CoV-2

Identifiers

PMID37771748
PMCPMC10523544
OpenAlexW4386264450

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.