Prediction of false positive SARS-CoV-2 molecular results in a high-throughput open platform system

Martinez, Ryan John; Pankratz, Nathan; Schomaker, Matthew; Daniel, Jerry; Beckman, Kenneth; Karger, Amy Beth; Thyagarajan, Bharat; Ferreri, Patricia; Yohe, Sophia Louise; Nelson, Andrew Cook

Widespread high-throughput testing for identification of SARS-CoV-2 infection by RT-PCR has been a foundation in the response to the COVID-19 pandemic. Quality assurance metrics for these RT-PCR tests are still evolving as testing becomes widely implemented. As testing increases, it is important to understand performance characteristics and the errors associated with these tests. Here, we investigate a high-throughput, laboratory developed SARS-CoV-2 RT-PCR assay to determine if 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 upon 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 methodology used. These models identified variables associated with false positive results across all methodologies, with sensitivities for predicting FP results ranging between 67-100%. 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.