A 6-mRNA host response classifier in whole blood predicts outcomes in COVID-19 and other acute viral infections

Abstract

Predicting 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.

A 9-mRNA signature measured from whole blood by a prototype PCR panel predicts 28-day mortality upon admission of critically ill COVID-19 patients

“…Transcriptomic approaches have shown that they could discriminate between distinct physio-pathological states of the COVID-19 (e.g paucisymptomatic, mild/moderate and severe) ( 22 ). Recently, a 6-gene signature was identified to predict COVID-19 mortality based on cohort explorative approaches ( 23 ).…”

“…However, we propose the use of a tool that captures the immune profile of a patient directly through processing blood samples without added laborious hands-on time and resource. In regard, none of the elegant transcriptomic-based machine learning models which were previously described to predict mortality in COVID-19 ( 21 , 23 ) can be easily implemented at the patient´s bedside due to constraints with RNAs processing and standardization of the measures. Similarly, while demographic parameters such as age or clinical parameters such as the SOFA score can readily be obtained at patient admission, some other variables used to build models in the literature are not so easily accessed.…”

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

“…The deep neural network (DNN) model earned the highest accuracy values in all three datasets. Buturovic et al [46] sought to build a blood-based host gene expression classifier for the severity of viral infections, including COVID-19. They created a logistic regression-based classifier for viral infection severity and validated it in a variety of viral infection situations, including COVID-19.…”

Alterations in Immune-Related Defensin Alpha 4 (DEFA4) Gene Expression in Health and Disease

“…Moreover, the hub genes may represent candidate biomarkers for diagnosis as well as be targeted in the therapeutic procedure. For example, DEFA4 was one of 6 genes constructed as a classifier that can predict the severity of different viral infections including the SARS-CoV 2 virus (COVID-19) [ 40 ]. All studies are summarized in Figure 2 .…”