a 6 mrna host response whole blood classifier trained using patients with non covid 19 CORD-Papers-2021-10-25 (Version 1)

Title: A 6-mRNA host response whole-blood classifier trained using patients with non-COVID-19 viral infections accurately predicts severity of COVID-19
Abstract: Background While major progress has been made to establish diagnostic tools for the identification of SARS-CoV-2 infection, determining the severity of COVID-19 remains an unmet medical need. There is a limited availability of hospital resources in this or any pandemic, and appropriately gauging severity would allow for some patients to safely recover in home quarantine, while ensuring that sicker patients get needed care. Methods We here developed a blood-based generalizable host-gene-expression-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 mRNAs. Results We selected 6 host mRNAs and trained a logistic regression classifier with a training cross-validation AUROC of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1,417 samples across 21 independent retrospective validation cohorts the locked 6-mRNA classifier had an AUROC of 0.91 for discriminating patients with severe vs. non-severe infection. Next, in an independent cohort of prospectively enrolled patients with confirmed COVID-19 (N=97) in Athens, Greece, the 6-mRNA locked classifier had an AUROC of 0.89 for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed an isothermal qRT-LAMP (loop-mediated isothermal gene expression) assay for the 6-mRNA panel to facilitate implementation as a rapid assay. Conclusions With further study, the classifier could assist in the risk assessment of patients with confirmed SARS-CoV-2 infection and COVID-19 to determine severity and level of care, thereby improving patient management and healthcare burden.
Published: 12/8/2020
DOI: 10.1101/2020.12.07.20230235
DOI_URL: http://doi.org/10.1101/2020.12.07.20230235
Author Name: Buturovic, L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/buturovic_l
Author Name: Zheng, H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zheng_h
Author Name: Tang, B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/tang_b
Author Name: Lai, K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lai_k
Author Name: Kuan, W S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/kuan_w_s
Author Name: Gillett, M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gillett_m
Author Name: Santram, R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/santram_r
Author Name: Shojaei, M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/shojaei_m
Author Name: Almansa, R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/almansa_r
Author Name: Nieto, J A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/nieto_j_a
Author Name: Munoz, S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/munoz_s
Author Name: Herrero, C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/herrero_c
Author Name: Antonakos, N
Author link: https://covid19-data.nist.gov/pid/rest/local/author/antonakos_n
Author Name: Koufargyris, P
Author link: https://covid19-data.nist.gov/pid/rest/local/author/koufargyris_p
Author Name: Kontogiorgi, M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/kontogiorgi_m
Author Name: Damoraki, G
Author link: https://covid19-data.nist.gov/pid/rest/local/author/damoraki_g
Author Name: Liesenfeld, O
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liesenfeld_o
Author Name: Wacker, J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wacker_j
Author Name: Midic, U
Author link: https://covid19-data.nist.gov/pid/rest/local/author/midic_u
Author Name: Luethy, R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/luethy_r
Author Name: Rawling, D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/rawling_d
Author Name: Remmel, M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/remmel_m
Author Name: Coyle, S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/coyle_s
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Rao, A M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/rao_a_m
Author Name: Dermadi, D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/dermadi_d
Author Name: Toh, J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/toh_j
Author Name: Jones, L M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jones_l_m
Author Name: Donato, M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/donato_m
Author Name: Khatri, P
Author link: https://covid19-data.nist.gov/pid/rest/local/author/khatri_p
Author Name: Giamarellos Bourboulis, E J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/giamarellos_bourboulis_e_j
Author Name: Sweeney, T E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/sweeney_t_e
sha: 3433d57dc2b16c14d02802a488ba82bfe296d2f8
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: http://medrxiv.org/cgi/content/short/2020.12.07.20230235v1?rss=1 https://doi.org/10.1101/2020.12.07.20230235
has_full_text: TRUE
Keywords Extracted from Text Content: patient COVID-19 patients SARS-CoV-2 qRT-LAMP host-gene-expression-based N=97 interleukin-6 GSE77087 medRxiv COVID-19 patients Figure 1 www.sepsis.gr LF/LB Hepatitis B peripheral blood DEFA4 antigen myeloid cells Supplementary Table 1 convalesce PaO2/FiO2 host-response peripheral blood mononuclear cells Supplementary Figure 1 immunoStates database Patients Class II molecules HLA-DR Blood Nile vacutainers myeloid dendritic cells COVID-19 SARS-CoV-2 cell membranes dendritic cells Stanford ICU cells creatinine https://metasignature.khatrilab.stanford.edu 55°C blood low-density lipoprotein lactate blood host-immune B lymphocytes Blood RNA stabilization CD4 T-cell host-gene-expression-based prealbumin AST medRxiv preprint Table 3 Inflammatix non-COVID-19 CRP TNFα BATF granulocytes CD3 IL-6 GSE66099 multiorgan HLA class II beta chain corticosteroids lymphocyte SARS-coronavirus 2 extracellular proteins intravenous immunoglobulin oxygen medRxiv preprint leave-one-study-out N=97 glucose FIP/BIP/F3/B3 HK3 prothrombin monocytes qRT-LAMP GE GC GSE101702 RNAdvance Blood kit cNRI NCBI GEO HLA-DPB1 respiratory secretions -8 lactate dehydrogenase Blood RNA samples Blood samples host-response-signature point-estimates blood samples QuantStudio 6 GSE38900 TGFBI samples PBMCs https://doi.org/10.1101/2020. PCT line HLA-DR monocytes host-response-based Supplementary Figure 2 https://doi.org/10.1101/2020.12.07.20230235 doi serum ferritin patient GSE103842 SAR-CoV-2 patients macrophages LY86 Hepatitis C D-dimer PK Horizon2020 Marie-Curie Project European Sepsis Academy ImmunoSep OL UM RL DR Inflammatix FrameWork 7 InflaRx PREVISE CoVerityTM TES HemoSpec Jesús Bermejo-Martin AxisShield
Extracted Text Content in Record: First 5000 Characters:Background While major progress has been made to establish diagnostic tools for the identification of SARS-CoV-2 infection, determining the severity of COVID-19 remains an unmet medical need. There is a limited availability of hospital resources in this or any pandemic, and appropriately gauging severity would allow for some patients to safely recover in home quarantine, while ensuring that sicker patients get needed care. We here developed a blood-based generalizable host-gene-expression-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 mRNAs. We selected 6 host mRNAs and trained a logistic regression classifier with a training cross-validation AUROC of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1,417 samples across 21 independent retrospective validation cohorts the locked 6-mRNA classifier had an AUROC of 0.91 for discriminating patients with severe vs. non-severe infection. Next, in an independent cohort of prospectively enrolled patients with confirmed COVID-19 (N=97) in Athens, Greece, the 6-mRNA locked classifier had an AUROC of 0.89 for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed an isothermal qRT-LAMP (loop-mediated isothermal gene expression) assay for the 6-mRNA panel to facilitate implementation as a rapid assay. With further study, the classifier could assist in the risk assessment of patients with confirmed SARS-CoV-2 infection and COVID-19 to determine severity and level of care, thereby improving patient management and healthcare burden. Background While major progress has been made to establish diagnostic tools for the identification of SARS-CoV-2 infection, determining the severity of COVID-19 remains an unmet medical need. There is a limited availability of hospital resources in this or any pandemic, and appropriately gauging severity would allow for some patients to safely recover in home quarantine, while ensuring that sicker patients get needed care. We here developed a blood-based generalizable host-gene-expression-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 mRNAs. We selected 6 host mRNAs and trained a logistic regression classifier with a training cross-validation AUROC of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1,417 samples across 21 independent retrospective validation cohorts the locked 6-mRNA classifier had an AUROC of 0.91 for discriminating patients with severe vs. non-severe infection. Next, in an independent cohort of prospectively enrolled patients with confirmed COVID-19 (N=97) in Athens, Greece, the 6-mRNA locked classifier had an AUROC of 0.89 for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed an isothermal qRT-LAMP (loop-mediated isothermal gene expression) assay for the 6-mRNA panel to facilitate implementation as a rapid assay. With further study, the classifier could assist in the risk assessment of patients with confirmed SARS-CoV-2 infection and COVID-19 to determine severity and level of care, thereby improving patient management and healthcare burden. The emergence of the SARS-coronavirus 2 (SARS-CoV-2), causative agent of COVID-19, and its rapid pandemic spread has led to a global health crisis with more than 54 million cases and more than 1 million deaths to date (1). COVID-19 presents with a spectrum of clinical phenotypes, with most patients exhibiting mild-to-moderate symptoms, and 20% progressing to severe or critical disease, typically within a week (2) (3) (4) (5) (6) . Severe cases are often characterized by acute respiratory failure requiring mechanical ventilation and sometimes progressing to ARDS and death (7) . Illness severity and development of ARDS are associated with older age and underlying medical conditions (3) . Yet, despite the rapid progress in developing diagnostics for SARS-CoV-2 infection, existing prognostic markers ranging from clinical data to biomarkers and immunopathological findings have proven unable to identify which patients are likely to progress to severe disease (8) . Poor risk stratification means that front-line providers may be unable to determine which patients might be safe to quarantine and convalesce at home, and which need close monitoring. Early identification of severity along with monitoring of immune status may also prove important for selection of treatments such as corticosteroids, intravenous immunoglobulin, or selective cytokine blockade (9) (10) (11) . A host of lab valu
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