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Tytuł:
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Estimating Clinically Relevant Cut-Off Values for a High-Throughput Quantitative Real-Time PCR Detecting Bacterial Respiratory Pathogens in Cattle.
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Autorzy:
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Klompmaker AF; Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Brydensholt M; Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Michelsen AM; Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Denwood MJ; Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Kirkeby CT; Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Larsen LE; Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Goecke NB; Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.; Centre for Diagnostics, Technical University of Denmark, Kongens Lyngby, Denmark.
Otten ND; Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Nielsen LR; Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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Źródło:
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Frontiers in veterinary science [Front Vet Sci] 2021 May 25; Vol. 8, pp. 674771. Date of Electronic Publication: 2021 May 25 (Print Publication: 2021).
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Typ publikacji:
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Journal Article
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Język:
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English
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Imprint Name(s):
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Original Publication: Lausanne : Frontiers Media S.A., [2014]-
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References:
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Contributed Indexing:
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Keywords: bovine respiratory disease; calf; clinically relevant cut-off; diagnostics; nasal swab; rtPCR
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Entry Date(s):
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Date Created: 20210611 Latest Revision: 20210612
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Update Code:
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20240104
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PubMed Central ID:
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PMC8185137
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DOI:
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10.3389/fvets.2021.674771
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PMID:
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34113678
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Bovine respiratory disease (BRD) results from interactions between pathogens, environmental stressors, and host factors. Obtaining a diagnosis of the causal pathogens is challenging but the use of high-throughput real-time PCR (rtPCR) may help target preventive and therapeutic interventions. The aim of this study was to improve the interpretation of rtPCR results by analysing their associations with clinical observations. The objective was to develop and illustrate a field-data driven statistical method to guide the selection of relevant quantification cycle cut-off values for pathogens associated with BRD for the high-throughput rtPCR system "Fluidigm BioMark HD" based on nasal swabs from calves. We used data from 36 herds enrolled in a Danish field study where 340 calves within pre-determined age-groups were subject to clinical examination and nasal swabs up to four times. The samples were analysed with the rtPCR system. Each of the 1,025 observation units were classified as sick with BRD or healthy, based on clinical scores. The optimal rtPCR results to predict BRD were investigated for Pasteurella multocida, Mycoplasma bovis, Histophilus somni, Mannheimia haemolytica , and Trueperella pyogenes by interpreting scatterplots and results of mixed effects logistic regression models. The clinically relevant rtPCR cut-off suggested for P. multocida and M. bovis was ≤ 21.3. For H. somni it was ≤ 17.4, while no cut-off could be determined for M. haemolytica and T. pyogenes . The demonstrated approach can provide objective support in the choice of clinically relevant cut-offs. However, for robust performance of the regression model sufficient amounts of suitable data are required.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2021 Klompmaker, Brydensholt, Michelsen, Denwood, Kirkeby, Larsen, Goecke, Otten and Nielsen.)
