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1.
Sci Rep ; 12(1): 889, 2022 01 18.
Article in English | MEDLINE | ID: covidwho-1630723

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.


Subject(s)
COVID-19 , Gene Expression Regulation , RNA, Messenger/blood , SARS-CoV-2/metabolism , Acute Disease , COVID-19/blood , COVID-19/mortality , Female , Humans , Male , Middle Aged , Predictive Value of Tests , Retrospective Studies
3.
PLoS One ; 15(9): e0238970, 2020.
Article in English | MEDLINE | ID: covidwho-760706

ABSTRACT

The expansion of Covid-19 has severely hit the community's health all over the world, killing hundreds of thousands of people, subjecting health systems to an enormous stress (besides derailing economic activities and altering personal and social behavior). Two elements are essential to monitor the evolution of the pandemic as well as to analyze the effectiveness of the response measures: reliable data and useful indicators. We present an indicator that helps to assess the impact of Covid-19 on the community's health, combining two different components: the extent of the pandemics (i.e. the share of the population affected) and its severity (the intensity of the disease on those affected). The severity measure derives from the application of an evaluation protocol that allows comparing population distributions based on the proportions of those affected with different health conditions. We illustrate the functioning of this indicator over a case study regarding the situation of the Italian regions on March 9 (the beginning of the confinement) and April 8, 2020, one month later.


Subject(s)
Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Public Health/statistics & numerical data , COVID-19 , Coronavirus Infections/prevention & control , Humans , Italy , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Quarantine/statistics & numerical data
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