Dataset Information

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.

SUBMITTER: Buturovic L

PROVIDER: S-EPMC8766462 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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

Buturovic Ljubomir L Zheng Hong H Tang Benjamin B Lai Kevin K Kuan Win Sen WS Gillett Mark M Santram Rahul R Shojaei Maryam M Almansa Raquel R Nieto Jose Ángel JÁ Muñoz Sonsoles S Herrero Carmen C Antonakos Nikolaos N Koufargyris Panayiotis P Kontogiorgi Marina M Damoraki Georgia G Liesenfeld Oliver O Wacker James J Midic Uros U Luethy Roland R Rawling David D Remmel Melissa M Coyle Sabrina S Liu Yiran E YE Rao Aditya M AM Dermadi Denis D Toh Jiaying J Jones Lara Murphy LM Donato Michele M Khatri Purvesh P Giamarellos-Bourboulis Evangelos J EJ Sweeney Timothy E TE

Scientific reports 20220118 1

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 an ...[more]

PMID: 35042868

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Project description:BackgroundWhether or not to administer antibiotics is a common and challenging clinical decision in patients with suspected infections presenting to the emergency department (ED). We prospectively validate InSep, a 29-mRNA blood-based host response test for the prediction of bacterial and viral infections.MethodsThe PROMPT trial is a prospective, non-interventional, multi-center clinical study that enrolled 397 adult patients presenting to the ED with signs of acute infection and at least one vital sign change. The infection status was adjudicated using chart review (including a syndromic molecular respiratory panel, procalcitonin and C-reactive protein) by three infectious disease physicians blinded to InSep results. InSep (version BVN-2) was performed using PAXgene Blood RNA processed and quantified on NanoString nCounter SPRINT. InSep results (likelihood of bacterial and viral infection) were compared to the adjudicated infection status.ResultsSubject mean age was 64 years, comorbidities were significant for diabetes (17.1%), chronic obstructive pulmonary disease (13.6%), and severe neurological disease (6.8%); 16.9% of subjects were immunocompromised. Infections were adjudicated as bacterial (14.1%), viral (11.3%) and noninfected (0.25%): 74.1% of subjects were adjudicated as indeterminate. InSep distinguished bacterial vs. viral/noninfected patients and viral vs. bacterial/noninfected patients using consensus adjudication with AUROCs of 0.94 (95% CI 0.90-0.99) and 0.90 (95% CI 0.83-0.96), respectively. AUROCs for bacterial vs. viral/noninfected patients were 0.88 (95% CI 0.79-0.96) for PCT, 0.80 (95% CI 0.72-89) for CRP and 0.78 (95% CI 0.69-0.87) for white blood cell counts (of note, the latter biomarkers were provided as part of clinical adjudication). To enable clinical actionability, InSep incorporates score cutoffs to allocate patients into interpretation bands. The Very Likely (rule in) InSep bacterial band showed a specificity of 98% compared to 94% for the corresponding PCT band (> 0.5 µg/L); the Very Unlikely (rule-out) band showed a sensitivity of 95% for InSep compared to 86% for PCT. For the detection of viral infections, InSep demonstrated a specificity of 93% for the Very Likely band (rule in) and a sensitivity of 96% for the Very Unlikely band (rule out).ConclusionsInSep demonstrated high accuracy for predicting the presence of both bacterial and viral infections in ED patients with suspected acute infections or suspected sepsis. When translated into a rapid, point-of-care test, InSep will provide ED physicians with actionable results supporting early informed treatment decisions to improve patient outcomes while upholding antimicrobial stewardship. Registration number at Clinicaltrials.gov NCT03295825.

Project description:The purpose of the study was to assess the patterns of global gene expression in peripheral blood cells and uncover the complex dynamics of host response to ARIs such as pandemic H1N1. To understand the molecular bases and network orchestration of host responses, we prospectively enrolled 1610 healthy adults in the fall of 2009 and 2010, followed the subjects with influenza-like illness (N=133) for 3 weeks, and examined changes in their peripheral blood gene expression. We discovered distinct phases of the host response spanning 6 days after infection, and identified genes that differentiate influenza from non-influenza virus infection. We examined pre- and post-infection anti-influenza antibody titers defining novel gene expression correlates. Healthy adults were invited to enroll to be followed for acute respiratory illness (ARI) through two consecutive influenza seasons 2009-2010 and 2010-2011. After subjects provided consent, baseline blood specimens were obtained during enrollment. Subjects were given thermometers and instructions to call and report for evaluation within 48 hours of ARI onset. Those persons who had a new ARI were seen within 48 hours of onset (day 0) and 2, 4, and 6 days later for repeat evaluation, blood specimen collections, and medical care (including the antiviral zanamivir if indicated) and 21 days later for collection of convalescent specimens. Nasal wash samples were collected for virus detection by RT-PCR on day 0 and day 2. Surveillance for influenza was terminated after 5.5 months; all subjects were asked to return for specimen collection and to provide a medical and ARI history in spring of next year. Serum specimens obtained at baseline, day 0 and day 21 visits for illnesses, and the terminal visit were tested simultaneously using hemagglutination-inhibition (HAI) antibody assay for Influenza H1N1, H3N2, and Influenza B strains. Peripheral blood RNA (PaxGene) obtained from blood specimens at each visit were analyzed using Illumina Human HT-12 v4. The study was repeated 2010-2011. A total of 880 arrays, corresponding to 133 individuals, passed quality control and are included in this data set.

Project description:Bacterial and viral infections are often clinically indistinguishable, leading to inappropriate patient management and antibiotic misuse. Bacterial-induced host proteins such as procalcitonin, C-reactive protein (CRP), and Interleukin-6, are routinely used to support diagnosis of infection. However, their performance is negatively affected by inter-patient variability, including time from symptom onset, clinical syndrome, and pathogens. Our aim was to identify novel viral-induced host proteins that can complement bacterial-induced proteins to increase diagnostic accuracy. Initially, we conducted a bioinformatic screen to identify putative circulating host immune response proteins. The resulting 600 candidates were then quantitatively screened for diagnostic potential using blood samples from 1002 prospectively recruited patients with suspected acute infectious disease and controls with no apparent infection. For each patient, three independent physicians assigned a diagnosis based on comprehensive clinical and laboratory investigation including PCR for 21 pathogens yielding 319 bacterial, 334 viral, 112 control and 98 indeterminate diagnoses; 139 patients were excluded based on predetermined criteria. The best performing host-protein was TNF-related apoptosis-inducing ligand (TRAIL) (area under the curve [AUC] of 0.89; 95% confidence interval [CI], 0.86 to 0.91), which was consistently up-regulated in viral infected patients. We further developed a multi-protein signature using logistic-regression on half of the patients and validated it on the remaining half. The signature with the highest precision included both viral- and bacterial-induced proteins: TRAIL, Interferon gamma-induced protein-10, and CRP (AUC of 0.94; 95% CI, 0.92 to 0.96). The signature was superior to any of the individual proteins (P<0.001), as well as routinely used clinical parameters and their combinations (P<0.001). It remained robust across different physiological systems, times from symptom onset, and pathogens (AUCs 0.87-1.0). The accurate differential diagnosis provided by this novel combination of viral- and bacterial-induced proteins has the potential to improve management of patients with acute infections and reduce antibiotic misuse.

Dataset Information

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

Publications

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

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