Evidence map›Paper›PMID 37379564›Full record

ArticlePloS one2023

Increasing SARS-CoV-2 testing capacity through specimen pooling: An acute care center experience.

Ana Cabrera, Fatimah Al Mutawah, Mike Kadour, Shannon Schofield, Beverley Conkey, Jeffrey Fuller, Michael Payne, Sameer Elsayed, Johan Delport

Abstract read
In one paragraph

Article in PloS one, 2023. 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

9 authors.

Ana CabreraPathology and Laboratory Medicine Department, London Health Sciences Centre, London, Ontario, Canada.ORCID 0000-0003-2246-3471
Fatimah Al MutawahPathology and Laboratory Medicine Department, London Health Sciences Centre, London, Ontario, Canada.
Mike KadourPathology and Laboratory Medicine Department, London Health Sciences Centre, London, Ontario, Canada.
Shannon SchofieldPathology and Laboratory Medicine Department, London Health Sciences Centre, London, Ontario, Canada.
Beverley ConkeyPathology and Laboratory Medicine Department, London Health Sciences Centre, London, Ontario, Canada.
Jeffrey FullerPathology and Laboratory Medicine Department, London Health Sciences Centre, London, Ontario, Canada.
Michael PaynePathology and Laboratory Medicine Department, London Health Sciences Centre, London, Ontario, Canada.
Sameer ElsayedPathology and Laboratory Medicine Department, London Health Sciences Centre, London, Ontario, Canada.
Johan DelportPathology and Laboratory Medicine Department, London Health Sciences Centre, London, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Innovation in laboratory testing algorithms to address seemingly uncontrollable global supply chain shortages in plastics and other consumables during emergencies such as the current COVID-19 pandemic have been urgently needed. We report our experience with specimen pooling on SARS-CoV-2 testing in an acute care hospital microbiology laboratory during a high testing demand period that exceeded available processing capacity. A fully automated four-in-one pooling algorithm was designed and validated. Correlation and agreement were calculated. A custom Microsoft Excel tool was designed for use by the technologists to aid interpretation, verification and result entry. Cost-per-test impact for pooling was measured in reference to the consumable cost and was denoted as the percentage reduction of cost versus the baseline cost-per-test of testing specimens individually. Validation showed a strong correlation between the signals observed when testing specimens individually versus those that were pooled. Average crossing point difference was 1.352 cycles (95% confidence interval of -0.235 and 2.940). Overall agreement observed between individually and pooled tested specimens was 96.8%. Stratified agreement showed an expected decreased performance of pooling for weakly positive specimens dropping below 60% after a crossing point of 35. Post-implementation data showed the consumable cost-savings achieved through this algorithm was 85.5% after 8 months, creating both testing and resource capacity. Pooling is an effective method to be used for SARS-CoV-2 testing during the current pandemic to address resource shortages and provide quick turnaround times for high test volumes without compromising performance.

Indexed as

COVID-19SARS-CoV-2COVID-19 TestingHumansLaboratoriesPandemicsSensitivity and SpecificitySpecimen Handling

Identifiers

PMID37379564
PMCPMC10306409

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.