Evidence map›Paper›PMID 30390351›Full record

ArticleJournal of clinical laboratory analysis2019

Prospective evaluation of a screening algorithm for carbapenemase-producing Enterobacteriaceae.

Morgane Choquet, Raphaël Guiheneuf, Sandrine Castelain, Emilie Pluquet, Véronique Decroix

Abstract read
In one paragraph

Article in Journal of clinical laboratory analysis, 2019. 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
–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

2 citing papers in PubMed.

  1. Article
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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

5 authors.

Morgane ChoquetDepartment of Bacteriology, Amiens University Hospital, Amiens, France.
Raphaël GuiheneufDepartment of Bacteriology, Amiens University Hospital, Amiens, France.
Sandrine CastelainDepartment of Bacteriology, Amiens University Hospital, Amiens, France.ORCID http://orcid.org/0000-0002-2670-3148
Emilie PluquetDepartment of Bacteriology, Amiens University Hospital, Amiens, France.
Véronique DecroixDepartment of Bacteriology, Amiens University Hospital, Amiens, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCarbapenemase-producing Enterobacteriaceae (CPE) have become a major public health issue. The objective of the present study was to prospectively assess the analytical performance of a CPE detection algorithm based on phenotypic tests (the screening test) and MALDI-ToF hydrolysis (the confirmatory test).

methodsOver a 6-month period and based on a disk diffusion method, 74 carbapenem-resistant strains were included in this study.

resultsOf the collected isolates, 54 turned out to be negative after phenotypic tests. Hence, 20 strains (including all of the CPEs) were checked with the confirmation test. Seven strains were positive. After molecular biology assessments in a reference center, three of the seven were found to be false positives. The algorithm had a negative predictive value and a sensitivity of 100%, a specificity of 77%, and a positive predictive value of 20%.

conclusionThe algorithm has a 24-hour turnaround time and helps to avoid using expensive molecular biology tests; we consider that it can be used on a routine basis for screening clinical strains.

Indexed as

AlgorithmsAnti-Bacterial AgentsCarbapenem-Resistant EnterobacteriaceaeEnterobacteriaceae InfectionsHumansMicrobial Sensitivity TestsMolecular TypingPredictive Value of TestsProspective StudiesSensitivity and SpecificitySpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationAnti-Bacterial Agentscarbapenemcarbapenemase-producing Enterobacteriaceaeprospectiveresistancescreening algorithm

Identifiers

PMID30390351
PMCPMC6818548

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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.