AUTHOR=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. , Pinsky Benjamin A. , Hogan Catherine A. TITLE=Targeted plasma metabolomics combined with machine learning for the diagnosis of severe acute respiratory syndrome virus type 2 JOURNAL=Frontiers in Microbiology VOLUME=13 YEAR=2023 URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2022.1059289 DOI=10.3389/fmicb.2022.1059289 ISSN=1664-302X ABSTRACT=Introduction

The routine clinical diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is largely restricted to real-time reverse transcription quantitative PCR (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.

Methods

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.

Results

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%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).

Discussion

This approach holds potential as an alternative to existing methods for the rapid and accurate diagnosis of acute SARS-CoV-2 infection.