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Prediction of False-Positive Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Molecular Results in a High-Throughput Open-Platform System

Widespread high-throughput testing for identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by RT-PCR has been a foundation in the response to the coronavirus disease 2019 (COVID-19) pandemic. Quality assurance metrics for these RT-PCR tests are still evolving as...

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Autores principales: Martinez, Ryan J., Pankratz, Nathan, Schomaker, Matthew, Daniel, Jerry, Beckman, Kenneth, Karger, Amy B., Thyagarajan, Bharat, Ferreri, Patricia, Yohe, Sophia L., Nelson, Andrew C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Association for Molecular Pathology and American Society for Investigative Pathology. Published by Elsevier Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8186061/
https://www.ncbi.nlm.nih.gov/pubmed/34116245
http://dx.doi.org/10.1016/j.jmoldx.2021.05.015
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author Martinez, Ryan J.
Pankratz, Nathan
Schomaker, Matthew
Daniel, Jerry
Beckman, Kenneth
Karger, Amy B.
Thyagarajan, Bharat
Ferreri, Patricia
Yohe, Sophia L.
Nelson, Andrew C.
author_facet Martinez, Ryan J.
Pankratz, Nathan
Schomaker, Matthew
Daniel, Jerry
Beckman, Kenneth
Karger, Amy B.
Thyagarajan, Bharat
Ferreri, Patricia
Yohe, Sophia L.
Nelson, Andrew C.
author_sort Martinez, Ryan J.
collection PubMed
description Widespread high-throughput testing for identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by RT-PCR has been a foundation in the response to the coronavirus disease 2019 (COVID-19) pandemic. Quality assurance metrics for these RT-PCR tests are still evolving as testing is widely implemented. As testing increases, it is important to understand performance characteristics and the errors associated with these tests. Herein, we investigate a high-throughput, laboratory-developed SARS-CoV-2 RT-PCR assay to determine whether modeling can generate quality control metrics that identify false-positive (FP) results due to contamination. This study reviewed repeated clinical samples focusing on positive samples that test negative on re-extraction and PCR, likely representing false positives. To identify and predict false-positive samples, we constructed machine learning–derived models based on the extraction method used. These models identified variables associated with false-positive results across all methods, with sensitivities for predicting FP results ranging between 67% and 100%. Application of the models to all results predicted a total FP rate of 0.08% across all samples, or 2.3% of positive results, similar to reports for other RT-PCR tests for RNA viruses. These models can predict quality control parameters, enabling laboratories to generate decision trees that reduce interpretation errors, allow for automated reflex testing of samples with a high FP probability, improve workflow efficiency, and increase diagnostic accuracy for patient care.
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spelling pubmed-81860612021-06-08 Prediction of False-Positive Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Molecular Results in a High-Throughput Open-Platform System Martinez, Ryan J. Pankratz, Nathan Schomaker, Matthew Daniel, Jerry Beckman, Kenneth Karger, Amy B. Thyagarajan, Bharat Ferreri, Patricia Yohe, Sophia L. Nelson, Andrew C. J Mol Diagn Regular Article Widespread high-throughput testing for identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by RT-PCR has been a foundation in the response to the coronavirus disease 2019 (COVID-19) pandemic. Quality assurance metrics for these RT-PCR tests are still evolving as testing is widely implemented. As testing increases, it is important to understand performance characteristics and the errors associated with these tests. Herein, we investigate a high-throughput, laboratory-developed SARS-CoV-2 RT-PCR assay to determine whether modeling can generate quality control metrics that identify false-positive (FP) results due to contamination. This study reviewed repeated clinical samples focusing on positive samples that test negative on re-extraction and PCR, likely representing false positives. To identify and predict false-positive samples, we constructed machine learning–derived models based on the extraction method used. These models identified variables associated with false-positive results across all methods, with sensitivities for predicting FP results ranging between 67% and 100%. Application of the models to all results predicted a total FP rate of 0.08% across all samples, or 2.3% of positive results, similar to reports for other RT-PCR tests for RNA viruses. These models can predict quality control parameters, enabling laboratories to generate decision trees that reduce interpretation errors, allow for automated reflex testing of samples with a high FP probability, improve workflow efficiency, and increase diagnostic accuracy for patient care. Association for Molecular Pathology and American Society for Investigative Pathology. Published by Elsevier Inc. 2021-09 2021-06-08 /pmc/articles/PMC8186061/ /pubmed/34116245 http://dx.doi.org/10.1016/j.jmoldx.2021.05.015 Text en © 2021 Association for Molecular Pathology and American Society for Investigative Pathology. Published by Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Regular Article
Martinez, Ryan J.
Pankratz, Nathan
Schomaker, Matthew
Daniel, Jerry
Beckman, Kenneth
Karger, Amy B.
Thyagarajan, Bharat
Ferreri, Patricia
Yohe, Sophia L.
Nelson, Andrew C.
Prediction of False-Positive Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Molecular Results in a High-Throughput Open-Platform System
title Prediction of False-Positive Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Molecular Results in a High-Throughput Open-Platform System
title_full Prediction of False-Positive Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Molecular Results in a High-Throughput Open-Platform System
title_fullStr Prediction of False-Positive Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Molecular Results in a High-Throughput Open-Platform System
title_full_unstemmed Prediction of False-Positive Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Molecular Results in a High-Throughput Open-Platform System
title_short Prediction of False-Positive Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Molecular Results in a High-Throughput Open-Platform System
title_sort prediction of false-positive severe acute respiratory syndrome coronavirus 2 (sars-cov-2) molecular results in a high-throughput open-platform system
topic Regular Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8186061/
https://www.ncbi.nlm.nih.gov/pubmed/34116245
http://dx.doi.org/10.1016/j.jmoldx.2021.05.015
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