Evidence map›Paper›PMID 24631369›Full record

ArticleJournal of infection and public health

Prevalence and risk factors for antibiotic-resistant community-associated bloodstream infections.

Caitlin M Wolfe, Bevin Cohen, Elaine Larson

Abstract read
In one paragraph

Article in Journal of infection and public health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Multidrug-Resistant Bacteria in the Community: An Update.Infectious disease clinics of North America · 2020
    Review
  10. Article
  11. Observational
  12. Article
  13. Multidrug-Resistant Bacteria in the Community: Trends and Lessons Learned.Infectious disease clinics of North America · 2016
    Review
  14. Bacteremia in Early Infancy: Etiology and Management.Current infectious disease reports · 2016
    Article
  15. Observational
  16. Review
  17. 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.

Caitlin M WolfeDepartment of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, USA. Electronic address: cmw2188@columbia.edu.
Bevin CohenDepartment of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, USA; Columbia University School of Nursing, 630 West 168th Street, New York, NY 10032, USA. Electronic address: bac2116@columbia.edu.
Elaine LarsonDepartment of Epidemiology, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, USA; Columbia University School of Nursing, 630 West 168th Street, New York, NY 10032, USA. Electronic address: ell23@columbia.edu.

Funding

Health Information Technology to Reduce Healthcare-Associated Infections: HIT-HAIR01NR010822 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI LARSON, ELAINE LUCILLE · 2007 to 2015
$3.0M
Impact of Hand Hygiene Guideline on Infections and CostsR01NR008242 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI LARSON, ELAINE LUCILLE · 2004 to 2005
$664k
NINR NIH HHS R01 NR008242NINR NIH HHS R01 NR010822
6 · The paper itself

Abstract

backgroundAntibiotic resistance is increasing in many community settings. The purpose of this study was to determine the proportion of antibiotic resistant community-associated bloodstream infections (CA-BSIs) present on hospital admissions to identify risk factors for acquiring resistant versus susceptible CA-BSIs and to describe the incidence of concurrent infections with CA-BSIs.

methodsWe conducted a retrospective cohort study of patients discharged from one community, one pediatric, and two tertiary/quaternary care hospitals within an academically affiliated network in the borough of Manhattan in New York, NY, from 2006 to 2008. The CA-BSIs present at hospital admission were defined as BSIs occurring within the first 48h of hospitalization. Infections and patient characteristics were identified using data available from patients' electronic medical records and discharge records.

resultsIn total, 1677 CA-BSIs were identified. Staphylococcus aureus had the largest proportion of resistance (41.2%), followed by enterococcal species (24.3%), Pseudomonas aeruginosa (20.2%), Streptococcus pneumoniae (16.6%), Acinetobacter baumannii (10.0%), and Klebsiella pneumoniae (9.9%). Significant predictors of resistance were prior residence in a skilled nursing facility (OR, 2.55; 95% CI, 1.39-4.70), advanced age (1.01; 1.002-1.02), presence of malignancy (0.58; 0.37-0.91), prior hospitalization (1.62; 1.17-2.23), a weighted Charlson score (1.09; 1.02-1.17) for S. aureus, presence of malignancy (1.82; 1.004-3.30), prior hospitalizations (2.03; 1.12-3.38) for enterococcal species, and younger age for S. pneumoniae (p=0.02). Urinary tract infections were the most common concurrent infection (n=45/87, 51.7%).

conclusionOver 27% of the CA-BSIs present on admission were antibiotic resistant. Understanding the prevalence and risk factors for CA-BSIs may help improve empiric antibiotic therapy and outcomes for patients with community-onset infections.

Indexed as

Drug Resistance, BacterialAdolescentAdultAgedAged, 80 and overAnti-Bacterial AgentsBacteremiaBacteriaChildChild, PreschoolCohort StudiesCommunity-Acquired InfectionsFemaleHumansInfantInfant, NewbornAnti-Bacterial AgentsAntibiotic resistanceBloodstream infectionsCommunity-associated bloodstream infections

Identifiers

PMID24631369
PMCPMC4096851

What OpenQuestion holds

Textmetadata
LicenceTDM
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.