Dataset Information

An integrated clinical and genetic model for predicting risk of severe COVID-19: A population-based case-control study.

ABSTRACT: Up to 30% of people who test positive to SARS-CoV-2 will develop severe COVID-19 and require hospitalisation. Age, gender, and comorbidities are known to be risk factors for severe COVID-19 but are generally considered independently without accurate knowledge of the magnitude of their effect on risk, potentially resulting in incorrect risk estimation. There is an urgent need for accurate prediction of the risk of severe COVID-19 for use in workplaces and healthcare settings, and for individual risk management. Clinical risk factors and a panel of 64 single-nucleotide polymorphisms were identified from published data. We used logistic regression to develop a model for severe COVID-19 in 1,582 UK Biobank participants aged 50 years and over who tested positive for the SARS-CoV-2 virus: 1,018 with severe disease and 564 without severe disease. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC). A model incorporating the SNP score and clinical risk factors (AUC = 0.786; 95% confidence interval = 0.763 to 0.808) had 111% better discrimination of disease severity than a model with just age and gender (AUC = 0.635; 95% confidence interval = 0.607 to 0.662). The effects of age and gender are attenuated by the other risk factors, suggesting that it is those risk factors-not age and gender-that confer risk of severe disease. In the whole UK Biobank, most are at low or only slightly elevated risk, but one-third are at two-fold or more increased risk. We have developed a model that enables accurate prediction of severe COVID-19. Continuing to rely on age and gender alone (or only clinical factors) to determine risk of severe COVID-19 will unnecessarily classify healthy older people as being at high risk and will fail to accurately quantify the increased risk for younger people with comorbidities.

SUBMITTER: Dite GS

PROVIDER: S-EPMC7886160 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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An integrated clinical and genetic model for predicting risk of severe COVID-19: A population-based case-control study.

Dite Gillian S GS Murphy Nicholas M NM Allman Richard R

PloS one 20210216 2

Up to 30% of people who test positive to SARS-CoV-2 will develop severe COVID-19 and require hospitalisation. Age, gender, and comorbidities are known to be risk factors for severe COVID-19 but are generally considered independently without accurate knowledge of the magnitude of their effect on risk, potentially resulting in incorrect risk estimation. There is an urgent need for accurate prediction of the risk of severe COVID-19 for use in workplaces and healthcare settings, and for individual r ...[more]

PMID: 33592063

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Project description:Background: Predicting the risk of progression to severe coronavirus disease 2019 (COVID-19) could facilitate personalized diagnosis and treatment options, thus optimizing the use of medical resources. Methods: In this prospective study, 206 patients with COVID-19 were enrolled from regional medical institutions between December 20, 2019, and April 10, 2020. We collated a range of data to derive and validate a predictive model for COVID-19 progression, including demographics, clinical characteristics, laboratory findings, and cytokine levels. Variation analysis, along with the least absolute shrinkage and selection operator (LASSO) and Boruta algorithms, was used for modeling. The performance of the derived models was evaluated by specificity, sensitivity, area under the receiver operating characteristic (ROC) curve (AUC), Akaike information criterion (AIC), calibration plots, decision curve analysis (DCA), and Hosmer-Lemeshow test. Results: We used the LASSO algorithm and logistic regression to develop a model that can accurately predict the risk of progression to severe COVID-19. The model incorporated alanine aminotransferase (ALT), interleukin (IL)-6, expectoration, fatigue, lymphocyte ratio (LYMR), aspartate transaminase (AST), and creatinine (CREA). The model yielded a satisfactory predictive performance with an AUC of 0.9104 and 0.8792 in the derivation and validation cohorts, respectively. The final model was then used to create a nomogram that was packaged into an open-source and predictive calculator for clinical use. The model is freely available online at https://severeconid-19predction.shinyapps.io/SHINY/. Conclusion: In this study, we developed an open-source and free predictive calculator for COVID-19 progression based on ALT, IL-6, expectoration, fatigue, LYMR, AST, and CREA. The validated model can effectively predict progression to severe COVID-19, thus providing an efficient option for early and personalized management and the allocation of appropriate medical resources.

Project description:For the purpose of establishing a mice model for sever COVID-19 , mice were infected intranasally with SARS-CoV-2 two days after pulmonary application of low-dose ricin. The goal of this study is to perform a comparative transcriptomic analysis by exploring the alterations in gene expression in lungs of mice exposed to low-dose ricin and SARS-CoV-2 (samples OR009-OR012) compared to lungs of mice infected solely with SARS-CoV-2 (48 hr after infection, samples OR021-OR024) or mice exposed to low-dose ricin (96 hr post exposure,samples OR005-OR008)) or mice exposed to lethal dose of ricin (48 hr post exposure, samples OR013-OR016). Methods: Total RNA was extracted from lungs of ricin intoxicated mice (intranasally exposure to 1.7 µg/kg or 10 µg/kg ricin ) or of SARS-CoV-2 infected mice (intranasal administration of 5 x 106 PFU), using the RNeasy mini kit (Qiagen). RNA-seq libraries were constructed and sequencing of 100bp paired-end was performed on the Illumina NovaSeq 6000 system. Sequencing yielded about 22M reads per sample that were mapped to the mouse genome. RNA ratios were clustered using partitioning clustering. We next carried out Ingenuity Pathway Analysis (IPA). Results: Principal component analysis revealed distinct transcriptional signatures between lungs mice exposed to low-dose ricin and infected with SARS-CoV-2 compared to lungs of mice infected solely with SARS-CoV-2 or mice exposed to low-dose ricin (96 hr post exposure) or mice exposed to lethal dose of ricin (48 hr post exposure). Differentially Expressed Genes (DEGs) were defined as such when having false discovery rate (FDR) value (p-adjusted) of ≤ 0.01 and a fold change (FC) of ≥ 1.5 or ≤ 0.66. Analysis of Low-dose-ricin-treated mice transcripts resulted in the detection of 6684 DEGs, 55% upregulated and 45% downregulated. Similarly, in Low-dose-ricin-treated mice infected with SARS-CoV-2, we found a total of 6044 differentially expressed genes, 52% induced and 48% repressed. Considerably fewer (797, 67% upregulated and 33% downregulated) differentially expressed genes were identified in lungs of mice infected only with SARS-CoV-2.

Dataset Information

An integrated clinical and genetic model for predicting risk of severe COVID-19: A population-based case-control study.

Publications

An integrated clinical and genetic model for predicting risk of severe COVID-19: A population-based case-control study.

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