Targeted plasma metabolomics combined with machine learning for the diagnosis of severe acute respiratory syndrome virus type 2 - Le, Anthony T.; Wu, Manhong; Khan, Afraz; Phillips, Nicholas; Rajpurkar, Pranav; Garland, Megan; Magid, Kayla; Sibai, Mamdouh; Huang, ChunHong; Sahoo, Malaya K.; Bowen, Raffick; Cowan, Tina M.; et al. (Frontiers in Microbiology, 2023)
The routine clinical diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is largely restricted to real-time reverse transcription polymerase chain reaction (RT-qPCR), and tests that detect SARS-CoV-2 nucleocapsid antigen. Given the diagnostic delay and suboptimal sensitivity associated with these respective methods, alternative diagnostic strategies are needed for acute infection. We studied the use of a clinically validated liquid chromatography triple quadrupole method (LC/MS-MS) for detection of amino acids from plasma specimens. We applied machine learning models to distinguish between SARS-CoV-2-positive and negative samples and analyzed amino acid feature importance. A total of 200 samples were tested, including 70 from individuals with COVID-19, and 130 from negative controls. The top performing model overall allowed discrimination between SARS-CoV-2-positive and negative control samples with an area under the receiver operating characteristic curve (AUC) of 0.96 (95% confidence interval [CI] 0.91, 1.00), overall sensitivity of 0.99 (95%CI 0.92, 1.00), and specificity of 0.92 (95%CI 0.85, 0.95). This approach holds potential as an alternative to existing methods for the rapid and accurate diagnosis of acute SARS-CoV-2 infection.