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

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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.

Original languageEnglish (US)
Pages (from-to)1085-1096
Number of pages12
JournalJournal of Molecular Diagnostics
Issue number9
StatePublished - Sep 2021

Bibliographical note

Funding Information:
We thank Robyn Kincaid and Drs. Sophie Arbefeville, Aaron Barnes, and Ryan Langlois for helpful discussion in the preparation of this article; the University of Minnesota Genomics Center (including Benjamin Auch, Ray Watson, Lindsey Gengelbach, Darrell Johnson, Dr. Patrick Grady, Dr. Daryl Gohl, and Shea Anderson) and the M Health Molecular Diagnostic Laboratory and Infectious Disease Diagnostic Laboratory (including Kylene Karnuth, Michaela Leary, Shannon Gascoigne, and Jessica Gunderson) for staffing, support, and testing performance of the SARS-CoV-2 testing.

Publisher Copyright:
© 2021 Association for Molecular Pathology and American Society for Investigative Pathology

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