Gene Expression Risk Scores for COVID-19 Illness Severity.
Ontology highlight
ABSTRACT: Background
The correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood.Methods
We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2-infection clinically adjudicated as having mild, moderate or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and non-severe COVID.Results
Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus non-severe illness, we identified >4000 genes differentially expressed (FDR<0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated ROC-AUC=0.98), and need for intensive care in an independent cohort (ROC-AUC=0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort.Conclusion
These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.
SUBMITTER: Peterson DR
PROVIDER: S-EPMC8404885 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundThe correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood.MethodsWe assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2-infection clinically adjudicated as having mild, moderate or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and non-severe COVID.ResultsGene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus non-severe illness, we identified >4000 genes differentially expressed (FDR<0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated ROC-AUC=0.98), and need for intensive care in an independent cohort (ROC-AUC=0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort.ConclusionThese data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.
| S-EPMC8767880 | biostudies-literature
Project description:The causative organism, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), exhibits a wide spectrum of clinical manifestations in disease-ridden patients. Differences in the severity of COVID-19 ranges from asymptomatic infections and mild cases to the severe form, leading to acute respiratory distress syndrome (ARDS) and multiorgan failure with poor survival. MiRNAs can regulate various cellular processes, including proliferation, apoptosis, and differentiation, by binding to the 3′UTR of target mRNAs inducing their degradation, thus serving a fundamental role in post-transcriptional repression. Alterations of miRNA levels in the blood have been described in multiple inflammatory and infectious diseases, including SARS-related coronaviruses. We used microarrays to delineate the miRNAs and snoRNAs signature in the peripheral blood of severe COVID-19 cases (n=9), as compared to mild (n=10) and asymptomatic (n=10) patients, and identified differentially expressed transcripts in severe versus asymptomatic, and others in severe versus mild COVID-19 cases. A cohort of 29 male age-matched patients were selected. All patients were previously diagnosed with COVID-19 using TaqPath COVID-19 Combo Kit (Thermo Fisher Scientific, Waltham, Massachusetts), or Cobas SARS-CoV-2 Test (Roche Diagnostics, Rotkreuz, Switzerland), with a CT value < 30. Additional criterion for selection was age between 35 and 75 years. Participants were grouped into severe, mild and asymptomatic. Classifying severe cases was based on requirement of high-flow oxygen support and ICU admission (n=9). Whereas mild patients were identified based on symptoms and positive radiographic findings with pulmonary involvement (n=10). Patients with no clinical presentation were labelled as asymptomatic cases (n=10).
2022-03-01 | E-MTAB-10970 | biostudies-arrayexpress
Project description:ObjectiveTo characterize the relationship between severity of sleep apnea and coronavirus disease 2019 (COVID-19) hospitalization and severe illness.Study designRetrospective cohort study.SettingMontefiore Health System in the Bronx, New York.MethodsThe data set consisted of adult patients with an active diagnosis of obstructive sleep apnea in the past 2 years and a positive severe acute respiratory syndrome coronavirus 2 quantitative polymerase chain reaction test at our institution between March 16, 2020, and May 26, 2020. Sleep apnea severity and continuous positive airway pressure compliance data were abstracted from the electronic medical record. The International Classification of Diseases, 10th Revision was used to classify comorbidities.ResultsA total of 461 patients with sleep apnea tested positive for COVID-19, of whom 149 were excluded for missing data in the electronic medical record. Patients with moderate and severe sleep apnea had higher rates of COVID-19 hospitalization compared to those with mild sleep apnea (P = .003). This association was reduced when accounting for confounders, most notably the Charlson Comorbidity Index, a measure of comorbid illness burden. Moderate and severe sleep apnea were associated with increased Charlson Comorbidity Indices, compared to mild sleep apnea (P = .01). Sleep apnea severity was not associated with a composite outcome of mechanical ventilation, intensive care unit admission, and death.ConclusionSleep apnea severity was associated with the Charlson Comorbidity Index and may be a risk factor for COVID-19 hospitalization. We found no evidence that sleep apnea severity among hospitalized patients was associated with a composite outcome of mechanical ventilation, intensive care unit admission, and death.
| S-EPMC8132102 | biostudies-literature
Project description:IMPORTANCE:Certain individuals, when infected by SARS-CoV-2, tend to develop the more severe forms of Covid-19 illness for reasons that remain unclear. OBJECTIVE:To determine the demographic and clinical characteristics associated with increased severity of Covid-19 infection. DESIGN:Retrospective observational study. We curated data from the electronic health record, and used multivariable logistic regression to examine the association of pre-existing traits with a Covid-19 illness severity defined by level of required care: need for hospital admission, need for intensive care, and need for intubation. SETTING:A large, multihospital healthcare system in Southern California. PARTICIPANTS:All patients with confirmed Covid-19 infection (N = 442). RESULTS:Of all patients studied, 48% required hospitalization, 17% required intensive care, and 12% required intubation. In multivariable-adjusted analyses, patients requiring a higher levels of care were more likely to be older (OR 1.5 per 10 years, P<0.001), male (OR 2.0, P = 0.001), African American (OR 2.1, P = 0.011), obese (OR 2.0, P = 0.021), with diabetes mellitus (OR 1.8, P = 0.037), and with a higher comorbidity index (OR 1.8 per SD, P<0.001). Several clinical associations were more pronounced in younger compared to older patients (Pinteraction<0.05). Of all hospitalized patients, males required higher levels of care (OR 2.5, P = 0.003) irrespective of age, race, or morbidity profile. CONCLUSIONS AND RELEVANCE:In our healthcare system, greater Covid-19 illness severity is seen in patients who are older, male, African American, obese, with diabetes, and with greater overall comorbidity burden. Certain comorbidities paradoxically augment risk to a greater extent in younger patients. In hospitalized patients, male sex is the main determinant of needing more intensive care. Further investigation is needed to understand the mechanisms underlying these findings.
| S-EPMC7377468 | biostudies-literature
Project description:Effective methods for predicting COVID-19 disease trajectories are urgently needed. Here, enzyme-linked immunosorbent assay (ELISA) and coronavirus antigen microarray (COVAM) analysis mapped antibody epitopes in the plasma of COVID-19 patients (n = 86) experiencing a wide range of disease states. The experiments identified antibodies to a 21-residue epitope from nucleocapsid (termed Ep9) associated with severe disease, including admission to the intensive care unit (ICU), requirement for ventilators, or death. Importantly, anti-Ep9 antibodies can be detected within 6 days post-symptom onset and sometimes within 1 day. Furthermore, anti-Ep9 antibodies correlate with various comorbidities and hallmarks of immune hyperactivity. We introduce a simple-to-calculate, disease risk factor score to quantitate each patient’s comorbidities and age. For patients with anti-Ep9 antibodies, scores above 3.0 predict more severe disease outcomes with a 13.42 likelihood ratio (96.7% specificity). The results lay the groundwork for a new type of COVID-19 prognostic to allow early identification and triage of high-risk patients. Such information could guide more effective therapeutic intervention.
2021-04-21 | GSE172471 | GEO
Project description:Severely-afflicted COVID-19 patients can exhibit disease manifestations representative of sepsis, including acute respiratory distress syndrome and multiple organ failure. We hypothesized that diagnostic tools used in managing all-cause sepsis, such as clinical criteria, biomarkers, and gene expression signatures, should extend to COVID-19 patients. Here we analyzed the whole blood transcriptome of 124 early (1-5 days post-hospital admission) and late (6-20 days post-admission) sampled patients with confirmed COVID-19 infections from hospitals in Quebec, Canada. Mechanisms associated with COVID-19 severity were identified between severity groups (ranging from mild disease to the requirement for mechanical ventilation and mortality), and established sepsis signatures were assessed for dysregulation. Specifically, gene expression signatures representing pathophysiological events, namely cellular reprogramming, organ dysfunction, and mortality, were significantly enriched and predictive of severity and lethality in COVID-19 patients. Mechanistic endotypes reflective of distinct sepsis aetiologies and therapeutic opportunities were also identified in subsets of patients, enabling prediction of potentially-effective repurposed drugs. The expression of sepsis gene expression signatures in severely-afflicted COVID-19 patients indicates that these patients should be classified as having severe sepsis. Accordingly, in severe COVID-19 patients, these signatures should be strongly considered for the mechanistic characterization, diagnosis, and guidance of treatment using repurposed drugs.
2022-12-18 | GSE221234 | GEO
Project description:We investigated the effect of IL-13 neutralization on COVID-19 disease progression in mice.
2021-07-23 | GSE180557 | GEO
Project description:BackgroundPatients with cardiovascular comorbidities are at high risk of poor outcome from COVID-19. However, how the burden (number) of vascular risk factors influences the risk of severe COVID-19 disease remains unresolved. Our aim was to investigate the association of severe COVID-19 illness with vascular risk factor burden.MethodsWe included 164 (61.8 ± 13.6 years) patients with COVID-19 in this retrospective study. We compared the difference in clinical characteristics, laboratory findings and chest computed tomography (CT) findings between patients with severe and non-severe COVID-19 illness. We evaluated the association between the number of vascular risk factors and the development of severe COVID-19 disease, using a Cox regression model.ResultsSixteen (9.8%) patients had no vascular risk factors; 38 (23.2%) had 1; 58 (35.4%) had 2; 34 (20.7%) had 3; and 18 (10.9%) had ≥4 risk factors. Twenty-nine patients (17.7%) experienced severe COVID-19 disease with a median (14 [7-27] days) duration between onset to developing severe COVID-19 disease, an event rate of 4.47 per 1000-patient days (95%CI 3.10-6.43). Kaplan-Meier curves showed a gradual increase in the risk of severe COVID-19 illness (log-rank P < 0.001) stratified by the number of vascular risk factors. After adjustment for age, sex, and comorbidities as potential confounders, vascular risk factor burden remained associated with an increasing risk of severe COVID-19 illness.ConclusionsPatients with increasing vascular risk factor burden have an increasing risk of severe COVID-19 disease, and this population might benefit from specific COVID-19 prevention (e.g., self-isolation) and early hospital treatment measures.
| S-EPMC7503438 | biostudies-literature
Project description:Purpose To externally validate four previously developed severity scores (i.e., CALL, CHOSEN, HA2T2 and ANDC) in patients with COVID-19 hospitalised in a tertiary care centre in Switzerland. Methods This observational analysis included adult patients with a real-time reverse-transcription polymerase chain reaction or rapid-antigen test confirmed severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection hospitalised consecutively at the Cantonal Hospital Aarau from February to December 2020. The primary endpoint was all-cause in-hospital mortality. The secondary endpoint was disease progression, defined as needing invasive ventilation, ICU admission or death. Results From 399 patients (mean age 66.6 years ± 13.4 SD, 68% males), we had complete data for calculating the CALL, CHOSEN, HA2T2 and ANDC scores in 297, 380, 151 and 124 cases, respectively. Odds ratios for all four scores showed significant associations with mortality. The discriminative power of the HA2T2 score was higher compared to CALL, CHOSEN and ANDC scores [area under the curve (AUC) 0.78 vs. 0.65, 0.69 and 0.66, respectively]. Negative predictive values (NPV) for mortality were high, particularly for the CALL score (≥ 6 points: 100%, ≥ 9 points: 95%). For disease progression, discriminative power was lower, with the CHOSEN score showing the best performance (AUC 0.66). Conclusion In this external validation study, the four analysed scores had a lower performance compared to the original cohorts regarding prediction of mortality and disease progression. However, all scores were significantly associated with mortality and the NPV of the CALL and CHOSEN scores in particular allowed reliable identification of patients at low risk, making them suitable for outpatient management. Supplementary Information The online version contains supplementary material available at 10.1007/s15010-021-01728-0.
| S-EPMC8604199 | biostudies-literature
Project description:Though commonly used for adjustment of risk, severity of illness and mortality risk prediction scores, based on the first 24 h of intensive care unit (ICU) admission, have not been validated in the pediatric extracorporeal membrane oxygenation (ECMO) population. We aimed to determine the association of Pediatric Index of Mortality 2 (PIM2), Pediatric Risk of Mortality Score III (PRISM III) and Pediatric Logistic Organ Dysfunction (PELOD) scores with mortality in pediatric patients on ECMO. This was a retrospective cohort study of children ≤18 years of age included in the Pediatric ECMO Outcomes Registry (PEDECOR) from 2014 to 2018. Logistic regression and Receiver Operating Characteristics (ROC) curves were used to calculate the area under the curve (AUC) to evaluate association of mortality with the scores. Of the 655 cases, 289 (44.1%) did not survive until hospital discharge. AUCs for PIM2, PRISM III, and PELOD predicting mortality were 0.52, 0.52, and 0.51 respectively. PIM2, PRISM III, and PELOD scores are not associated with odds of mortality for pediatric patients receiving ECMO. These scores for a general pediatric ICU population should not be used for prognostication or risk stratification of a select population such as ECMO patients.
| S-EPMC8506160 | biostudies-literature