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

J Mol Diagn. 2021 Sep;23(9):1085-1096. doi: 10.1016/j.jmoldx.2021.05.015. Epub 2021 Jun 8.


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.

MeSH terms

  • Automation, Laboratory
  • COVID-19 Nucleic Acid Testing / methods*
  • Carrier State / virology
  • Decision Support Systems, Clinical
  • False Positive Reactions
  • High-Throughput Nucleotide Sequencing / methods
  • Humans
  • Machine Learning
  • RNA, Viral / isolation & purification*
  • Reverse Transcriptase Polymerase Chain Reaction / methods*
  • SARS-CoV-2 / genetics
  • Viral Load
  • Workflow


  • RNA, Viral