Data standardization implementation and applications within and among diagnostic laboratories: integrating and monitoring enteric coronaviruses.


Journal

Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc
ISSN: 1943-4936
Titre abrégé: J Vet Diagn Invest
Pays: United States
ID NLM: 9011490

Informations de publication

Date de publication:
May 2021
Historique:
pubmed: 20 3 2021
medline: 2 6 2021
entrez: 19 3 2021
Statut: ppublish

Résumé

Every day, thousands of samples from diverse populations of animals are submitted to veterinary diagnostic laboratories (VDLs) for testing. Each VDL has its own laboratory information management system (LIMS), with processes and procedures to capture submission information, perform laboratory tests, define the boundaries of test results (i.e., positive or negative), and report results, in addition to internal business and accounting applications. Enormous quantities of data are accumulated and stored within VDL LIMSs. There is a need for platforms that allow VDLs to exchange and share portions of laboratory data using standardized, reliable, and sustainable information technology processes. Here we report concepts and applications for standardization and aggregation of data from swine submissions to multiple VDLs to detect and monitor porcine enteric coronaviruses by RT-PCR. Oral fluids, feces, and fecal swabs were the specimens submitted most frequently for enteric coronavirus testing. Statistical algorithms were used successfully to scan and monitor the overall and state-specific percentage of positive submissions. Major findings revealed a consistently recurrent seasonal pattern, with the highest percentage of positive submissions detected during December-February for porcine epidemic diarrhea virus, porcine deltacoronavirus, and transmissible gastroenteritis virus (TGEV). After 2014, very few submissions tested positive for TGEV. Monitoring VDL data proactively has the potential to signal and alert stakeholders early of significant changes from expected detection. We demonstrate the importance of, and applications for, data organized and aggregated by using LOINC and SNOMED CTs, as well as the use of customized messaging to allow inter-VDL exchange of information.

Identifiants

pubmed: 33739188
doi: 10.1177/10406387211002163
pmc: PMC8120076
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

457-468

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Auteurs

Giovani Trevisan (G)

Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA.

Leticia C M Linhares (LCM)

Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA.

Kent J Schwartz (KJ)

Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA.

Eric R Burrough (ER)

Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA.

Edison de S Magalhães (ES)

Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA.

Bret Crim (B)

Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA.

Poonam Dubey (P)

Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA.

Rodger G Main (RG)

Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA.

Phillip Gauger (P)

Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA.

Mary Thurn (M)

Veterinary Population Medicine, University of Minnesota, Saint Paul, MN.

Paulo T F Lages (PTF)

Veterinary Population Medicine, University of Minnesota, Saint Paul, MN.

Cesar A Corzo (CA)

Veterinary Population Medicine, University of Minnesota, Saint Paul, MN.

Jerry Torrison (J)

Veterinary Population Medicine, University of Minnesota, Saint Paul, MN.

Jamie Henningson (J)

College of Veterinary Medicine, Kansas State University, Manhattan, KS.

Eric Herrman (E)

College of Veterinary Medicine, Kansas State University, Manhattan, KS.

Rob McGaughey (R)

College of Veterinary Medicine, Kansas State University, Manhattan, KS.

Giselle Cino (G)

College of Veterinary Medicine, Kansas State University, Manhattan, KS.

Jon Greseth (J)

Veterinary & Biomedical Sciences Department, South Dakota State University, Brookings, SD.

Travis Clement (T)

Veterinary & Biomedical Sciences Department, South Dakota State University, Brookings, SD.

Jane Christopher-Hennings (J)

Veterinary & Biomedical Sciences Department, South Dakota State University, Brookings, SD.

Daniel C L Linhares (DCL)

Veterinary Diagnostic and Production Animal Medicine, Iowa State University, Ames, IA.

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Classifications MeSH