Evidence map›Paper›PMID 31340780›Full record

ArticleBMC pediatrics2019

Development, evaluation and validation of a screening tool for late onset bacteremia in neonates - a pilot study.

Sandra A N Walker, Melanie Cormier, Marion Elligsen, Julie Choudhury, Asaph Rolnitsky, Carla Findlater, Dolores Iaboni

Open access · goldAbstract readValidation Study
In one paragraph

Article in BMC pediatrics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.1field-weighted citation impact, top 21% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Observational
  2. Article
  3. Article
  4. Article
  5. Can we improve early identification of neonatal late-onset sepsis? A validated prediction model.Journal of perinatology : official journal of the California Perinatal Association · 2020
    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

7 authors at 1 institution in 1 country.

Sandra A N WalkerDepartment of Pharmacy E-302, Sunnybrook Health Sciences Centre (SHSC), 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada. sandra.walker@sunnybrook.ca.ORCID 0000-0002-5908-446X
Melanie CormierDepartment of Pharmacy E-302, Sunnybrook Health Sciences Centre (SHSC), 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Marion ElligsenDepartment of Pharmacy E-302, Sunnybrook Health Sciences Centre (SHSC), 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Julie ChoudhurySHSC, Women and Babies Program, Toronto, Ontario, Canada.
Asaph RolnitskySHSC, Women and Babies Program, Toronto, Ontario, Canada.
Carla FindlaterSHSC, Women and Babies Program, Toronto, Ontario, Canada.
Dolores IaboniSHSC, Women and Babies Program, Toronto, Ontario, Canada.
Sunnybrook Health Science Centre · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClinical and laboratory parameters can aid in the early identification of neonates at risk for bacteremia before clinical deterioration occurs. However, current prediction models have poor diagnostic capabilities. The objective of this study was to develop, evaluate and validate a screening tool for late onset (> 72 h post admission) neonatal bacteremia using common laboratory and clinical parameters; and determine its predictive value in the identification of bacteremia.

methodsA retrospective chart review of neonates admitted to a neonatal intensive care unit (NICU) between March 1, 2012 and January 14, 2015 and a prospective evaluation of all neonates admitted between January 15, 2015 and March 30, 2015 were completed. Neonates with late-onset bacteremia (> 72 h after NICU admission) were eligible for inclusion in the bacteremic cohort. Bacteremic patients were matched to non-infected controls on several demographic parameters. A Pearson's Correlation matrix was completed to identify independent variables significantly associated with infection (p < 0.05, univariate analysis). Significant parameters were analyzed using iterative binary logistic regression to identify the simplest significant model (p < 0.05). The predictive value of the model was assessed and the optimal probability cut-off for bacteremia was determined using a Receiver Operating Characteristic curve.

resultsMaximum blood glucose, heart rate, neutrophils and bands were identified as the best predictors of bacteremia in a significant binary logistic regression model. The model's sensitivity, specificity and accuracy were 90, 80 and 85%, respectively, with a false positive rate of 20% and a false negative rate of 9.7%. At the study bacteremia prevalence rate of 51%, the positive predictive value, negative predictive value and negative post-test probability were 82, 89 and 11%, respectively.

conclusionThe model developed in the current study is superior to currently published neonatal bacteremia screening tools. Validation of the tool in a historic data set of neonates from our institution will be completed.

Indexed as

Analysis of VarianceBacteremiaCase-Control StudiesFemaleHumansInfant, NewbornIntensive Care Units, NeonatalLogistic ModelsMaleNeonatal ScreeningNeonatal SepsisPilot ProjectsPredictive Value of TestsPrevalenceRetrospective StudiesROC CurveLate onset bacteremiaNeonatesScreening tool

Identifiers

PMID31340780
PMCPMC6651932
OpenAlexW2962695623

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.