Author:
Buturovic Ljubomir,Zheng Hong,Tang Benjamin,Lai Kevin,Kuan Win Sen,Gillett Mark,Santram Rahul,Shojaei Maryam,Almansa Raquel,Nieto Jose Ángel,Muñoz Sonsoles,Herrero Carmen,Antonakos Nikolaos,Koufargyris Panayiotis,Kontogiorgi Marina,Damoraki Georgia,Liesenfeld Oliver,Wacker James,Midic Uros,Luethy Roland,Rawling David,Remmel Melissa,Coyle Sabrina,Liu Yiran E.,Rao Aditya M.,Dermadi Denis,Toh Jiaying,Jones Lara Murphy,Donato Michele,Khatri Purvesh,Giamarellos-Bourboulis Evangelos J.,Sweeney Timothy E.
Abstract
AbstractPredicting the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. We developed a logistic regression-based classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N = 705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. We selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.94 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N = 97) and retrospectively (N = 100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. With further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.
Publisher
Springer Science and Business Media LLC
Reference46 articles.
1. https://coronavirus.jhu.edu/map.html. (Johns Hopkins University, 2020).
2. Zhou, F. et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 395, 1054–1062 (2020).
3. Wang, D. et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 323, 1061–1069 (2020).
4. Cevik, M., Bamford, C. & Ho, A. COVID-19 pandemic—A focused review for clinicians. Clin. Microbiol. Infect. https://doi.org/10.1016/j.cmi.2020.04.023 (2020).
5. Epidemiology Working Group for NCIP Epidemic Response. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China. Zhonghua Liu Xing Bing Xue Za Zhi 41, 145–151 (2020).