Evidence map›Paper›PMID 39315787›Full record

ArticleMicrobiology spectrum2024

Simulation to optimize the laboratory diagnosis of bacteremia.

Alessandro Gerada, Gareth Roberts, Alex Howard, Nada Reza, Anoop Velluva, Conor Rosato, Peter L Green, William Hope

Abstract read
In one paragraph

Article in Microbiology spectrum, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Alessandro GeradaAntimicrobial Pharmacodynamics and Therapeutics Group, Pharmacology Department, Institute of Systems, Molecular & Integrative Biology, University of Liverpool, Liverpool, United Kingdom.ORCID 0000-0002-6743-4271
Gareth RobertsAntimicrobial Pharmacodynamics and Therapeutics Group, Pharmacology Department, Institute of Systems, Molecular & Integrative Biology, University of Liverpool, Liverpool, United Kingdom.
Alex HowardAntimicrobial Pharmacodynamics and Therapeutics Group, Pharmacology Department, Institute of Systems, Molecular & Integrative Biology, University of Liverpool, Liverpool, United Kingdom.
Nada RezaAntimicrobial Pharmacodynamics and Therapeutics Group, Pharmacology Department, Institute of Systems, Molecular & Integrative Biology, University of Liverpool, Liverpool, United Kingdom.ORCID 0000-0003-1183-3262
Anoop VelluvaAntimicrobial Pharmacodynamics and Therapeutics Group, Pharmacology Department, Institute of Systems, Molecular & Integrative Biology, University of Liverpool, Liverpool, United Kingdom.
Conor RosatoAntimicrobial Pharmacodynamics and Therapeutics Group, Pharmacology Department, Institute of Systems, Molecular & Integrative Biology, University of Liverpool, Liverpool, United Kingdom.
Peter L GreenAntimicrobial Pharmacodynamics and Therapeutics Group, Pharmacology Department, Institute of Systems, Molecular & Integrative Biology, University of Liverpool, Liverpool, United Kingdom.
William HopeAntimicrobial Pharmacodynamics and Therapeutics Group, Pharmacology Department, Institute of Systems, Molecular & Integrative Biology, University of Liverpool, Liverpool, United Kingdom.ORCID 0000-0001-6187-878X

Funding

Wellcome TrustWellcome Trust (WT) 226691/Z/22/Z
6 · The paper itself

Abstract

Blood cultures are central to the management of patients with sepsis and bloodstream infection. Clinical decisions depend on the timely availability of laboratory information, which, in turn, depends on the optimal laboratory processing of specimens. Discrete event simulation (DES) offers insights into where optimization efforts can be targeted. Here, we generate a detailed process map of blood culture processing within a laboratory and use it to build a simulator. Direct observation of laboratory staff processing blood cultures was used to generate a flowchart of the blood culture laboratory pathway. Retrospective routinely collected data were combined with direct observations to generate probability distributions over the time taken for each event. These data were used to inform the DES model. A sensitivity analysis explored the impact of staff availability on turnaround times. A flowchart of the blood culture pathway was constructed, spanning labeling, incubation, organism identification, and antimicrobial susceptibility testing. Thirteen processes in earlier stages of the pathway, not otherwise captured by routinely collected data, were timed using direct observations. Observations revealed that specimen processing is predominantly batched. Another eight processes were timed using retrospective data. A simulator was built using DES. Sensitivity analysis revealed that specimen progression through the simulation was especially sensitive to laboratory technician availability. Gram stain reporting time was also sensitive to laboratory scientist availability. Our laboratory simulation model has wide-ranging applications for the optimization of laboratory processes and effective implementation of the changes required for faster and more accurate results. IMPORTANCE: Optimization of laboratory pathways and resource availability has a direct impact on the clinical management of patients with bloodstream infection. This research offers an insight into the laboratory processing of blood cultures at a system level and allows clinical microbiology laboratories to explore the impact of changes to processes and resources.

Indexed as

BacteremiaBlood CultureComputer SimulationHumansRetrospective StudiesSpecimen Handlingblood culturebloodstream infectionsclinical microbiologydiscrete event simulation

Identifiers

PMID39315787
PMCPMC11537109

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.