Project description:Patients with coronavirus disease-19 (COVID-19) are at high risk for thrombotic arterial and venous occlusions. However, bleeding complications have also been observed in some patients. Understanding the balance between coagulation and fibrinolysis will help inform optimal approaches to thrombosis prophylaxis and potential utility of fibrinolytic-targeted therapies. 118 hospitalized COVID-19 patients and 30 healthy controls were included in the study. We measured plasma antigen levels of tissue-type plasminogen activator (tPA) and plasminogen activator inhibitor-1 (PAI-1) and performed spontaneous clot-lysis assays. We found markedly elevated tPA and PAI-1 levels in patients hospitalized with COVID-19. Both factors demonstrated strong correlations with neutrophil counts and markers of neutrophil activation. High levels of tPA and PAI-1 were associated with worse respiratory status. High levels of tPA, in particular, were strongly correlated with mortality and a significant enhancement in spontaneous ex vivo clot-lysis. While both tPA and PAI-1 are elevated among COVID-19 patients, extremely high levels of tPA enhance spontaneous fibrinolysis and are significantly associated with mortality in some patients. These data indicate that fibrinolytic homeostasis in COVID-19 is complex with a subset of patients expressing a balance of factors that may favor fibrinolysis. Further study of tPA as a biomarker is warranted.
Project description:Infections caused by SARS-CoV-2 may cause a severe disease, termed COVID-19, with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms, and their modulation is the only therapeutic strategy that has shown a mortality benefit. Herein, we used peripheral blood transcriptomes of critically-ill COVID-19 patients obtained at admission in an Intensive Care Unit, to identify two clusters that, in spite of no major clinical differences, have different gene expression profiles that reveal different underlying pathogenetic mechanisms and ultimately have different ICU outcome. A transcriptomic signature was used to identify these clusters in an external validation cohort, yielding a similar result. These results illustrate the potential of transcriptomic profiles to identify patient endotypes and point to relevant pathogenetic mechanisms in COVID-19.
Project description:Infections caused by SARS-CoV-2 may cause a severe disease, termed COVID-19, with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms, and their modulation is the only therapeutic strategy that has shown a mortality benefit. Herein, we used peripheral blood transcriptomes of critically-ill COVID-19 patients obtained at admission in an Intensive Care Unit, to identify two clusters that, in spite of no major clinical differences, have different gene expression profiles that reveal different underlying pathogenetic mechanisms and ultimately have different ICU outcome. A transcriptomic signature was used to identify these clusters in an external validation cohort, yielding a similar result. These results illustrate the potential of transcriptomic profiles to identify patient endotypes and point to relevant pathogenetic mechanisms in COVID-19.
Project description:Infections caused by SARS-CoV-2 may cause a severe disease, termed COVID-19, with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms, and their modulation is the only therapeutic strategy that has shown a mortality benefit. Herein, we used peripheral blood transcriptomes of critically-ill COVID-19 patients obtained at admission in an Intensive Care Unit, to identify two clusters that, in spite of no major clinical differences, have different gene expression profiles that reveal different underlying pathogenetic mechanisms and ultimately have different ICU outcome. A transcriptomic signature was used to identify these clusters in an external validation cohort, yielding a similar result. These results illustrate the potential of transcriptomic profiles to identify patient endotypes and point to relevant pathogenetic mechanisms in COVID-19.
Project description:Total plasma IgA glycosylation was compared between healthy volunteers and volunteers suffering fromo infections with either the influenza A virus or the severe acute respiratory syndrome corona virus 2. Data from functional assays of the same plasma samples, such as neutrophil extracellular trap formation is also available.
Project description:Single-cell RNA-sequencing reveals a shift from focused IFN alpha-driven signals in COVID-19 ICU patients who survive to broad pro-inflammatory responses in fatal COVID-19 – a feature not observed in severe influenza. We conclude that fatal COVID-19 infection is driven by uncoordinated inflammatory responses that drive a hierarchy of T cell activation, elements of which can serve as prognostic indicators and potential targets for immune intervention.
Project description:Background: Systemic inflammation is a whole body reaction that can have an infection-positive (i.e. sepsis) or infection-negative origin. It is important to distinguish between septic and non-septic presentations early and reliably, because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on a small number of RNAs expressed in peripheral blood could be discovered that would: 1) determine which patients with systemic inflammation had sepsis; 2) be robust across independent patient cohorts; 3) be insensitive to disease severity; and 4) provide diagnostic utility. The overall goal of this study was to identify and validate such a molecular classifier. Methods and Findings: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICU). Biomarker discovery was conducted with an Australian cohort (n = 105) consisting of sepsis patients and post -surgical patients with infection-negative systemic inflammation. Using this cohort, a four-gene classifier consisting of a combination of CEACAM4, LAMP1, PLA2G7 and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyte® Lab, was externally validated using RT-qPCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Cohort 1 (n=59) consisted of unambiguous septic cases and infection-negative systemic inflammation controls; SeptiCyte® Lab gave an area under curve (AUC) of 0.96 (95% CI: 0.91-1.00). ROC analysis of a more heterogeneous group of patients (Cohorts 2-5; 249 patients after excluding 37 patients with infection likelihood possible) gave an AUC of 0.89 (95% CI: 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or the Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility o f SeptiCyte® Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyte® Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyte® Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyte® Lab is a rapid molecular assay that may be clinically useful in the management of ICU patients with systemic inflammation. SIRS and Sepsis ICU patients, admission samples Retrospective, mutli-site sutdy using retrospective physician adjudication as a comparator
Project description:We report preliminary data from a cohort of adults admitted to COVID-designated intensive care units from March 6 through April 17, 2020 across an academic healthcare system. Among 217 critically ill patients, mortality for those who required mechanical ventilation was 29.7% (49/165), with 8.5% (14/165) of patients still on the ventilator at the time of this report. Overall mortality to date in this critically ill cohort is 25.8% (56/217), and 40.1% (87/217) patients have survived to hospital discharge. Despite multiple reports of mortality rates exceeding 50% among critically ill adults with COVID-19, particularly among those requiring mechanical ventilation, our early experience indicates that many patients survive their critical illness.