TY - JOUR
T1 - Prediction of False-Positive Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Molecular Results in a High-Throughput Open-Platform System
AU - Martinez, Ryan J.
AU - Pankratz, Nathan
AU - Schomaker, Matthew
AU - Daniel, Jerry
AU - Beckman, Kenneth
AU - Karger, Amy B.
AU - Thyagarajan, Bharat
AU - Ferreri, Patricia
AU - Yohe, Sophia L.
AU - Nelson, Andrew C.
N1 - Publisher Copyright:
© 2021 Association for Molecular Pathology and American Society for Investigative Pathology
PY - 2021/9
Y1 - 2021/9
N2 - 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.
AB - 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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U2 - 10.1016/j.jmoldx.2021.05.015
DO - 10.1016/j.jmoldx.2021.05.015
M3 - Article
C2 - 34116245
AN - SCOPUS:85113330240
SN - 1525-1578
VL - 23
SP - 1085
EP - 1096
JO - Journal of Molecular Diagnostics
JF - Journal of Molecular Diagnostics
IS - 9
ER -