Comparing variability in diagnosis of upper respiratory tract infections in patients using syndromic, next generation sequencing, and PCR-based methods

Andrew W Bartlow; Zachary R Stromberg; Cheryl D Gleasner; Bin Hu; Karen W Davenport; Shailja Jakhar; Po-E Li; Molly Vosburg; Madhavi Garimella; Patrick S G Chain; Tracy H Erkkila; Jeanne M Fair; Harshini Mukundan

doi:10.1371/journal.pgph.0000811

Comparing variability in diagnosis of upper respiratory tract infections in patients using syndromic, next generation sequencing, and PCR-based methods

PLOS Glob Public Health. 2022 Jul 20;2(7):e0000811. doi: 10.1371/journal.pgph.0000811. eCollection 2022.

Affiliations

¹ Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
² Physical Chemistry and Applied Spectroscopy, Chemistry Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
³ Medical Associates of Northern New Mexico, Los Alamos, New Mexico, United States of America.

Abstract

Early and accurate diagnosis of respiratory pathogens and associated outbreaks can allow for the control of spread, epidemiological modeling, targeted treatment, and decision making-as is evident with the current COVID-19 pandemic. Many respiratory infections share common symptoms, making them difficult to diagnose using only syndromic presentation. Yet, with delays in getting reference laboratory tests and limited availability and poor sensitivity of point-of-care tests, syndromic diagnosis is the most-relied upon method in clinical practice today. Here, we examine the variability in diagnostic identification of respiratory infections during the annual infection cycle in northern New Mexico, by comparing syndromic diagnostics with polymerase chain reaction (PCR) and sequencing-based methods, with the goal of assessing gaps in our current ability to identify respiratory pathogens. Of 97 individuals that presented with symptoms of respiratory infection, only 23 were positive for at least one RNA virus, as confirmed by sequencing. Whereas influenza virus (n = 7) was expected during this infection cycle, we also observed coronavirus (n = 7), respiratory syncytial virus (n = 8), parainfluenza virus (n = 4), and human metapneumovirus (n = 1) in individuals with respiratory infection symptoms. Four patients were coinfected with two viruses. In 21 individuals that tested positive using PCR, RNA sequencing completely matched in only 12 (57%) of these individuals. Few individuals (37.1%) were diagnosed to have an upper respiratory tract infection or viral syndrome by syndromic diagnostics, and the type of virus could only be distinguished in one patient. Thus, current syndromic diagnostic approaches fail to accurately identify respiratory pathogens associated with infection and are not suited to capture emerging threats in an accurate fashion. We conclude there is a critical and urgent need for layered agnostic diagnostics to track known and unknown pathogens at the point of care to control future outbreaks.

Copyright: © 2022 Bartlow et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Grants and funding

This article was funded by U.S. Department of Homeland Security, Science and Technology Directorate (DHS CBD OBAA 14-003, PM: Mr. David Shepherd, R-00603-17-0, Predictive Analytics for Respiratory Pathogens) to ZRS, CDG, BH, KD, SJ, PL, PSGC, TE, JMF, HM. AWB received funding from a Director's Funded Postdoctoral Fellowship through the Laboratory Directed Research and Development program (#3B100A-XWVP) at Los Alamos National Laboratory. MG and MV did not receive funding for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.