COVID GEAS: COVID-19 National Survey in Patients With Systemic Autoimmune Diseases.

OBJECTIVES: COVID-19 outcomes in population with systemic autoimmune diseases (SAD) remain poorly understood. The aim was to examine demographic and clinical factors associated with COVID-19 infection in people with rheumatic disease. METHODS: Two phases cross-sectional survey of individuals with rheumatic disease in April 2020 and October 2020. COVID infection, severity of disease, age, sex, smoking status, underlying rheumatic disease diagnosis, comorbidities and rheumatic disease medications taken immediately prior to infection were analyzed. RESULTS: A total of 1,529 individuals with autoimmunity disease diagnosis were included. Out of 50 positive patients, 21 required telephone medical assistance, 16 received assessment by primary care physician, 9 were evaluated in Emergency Department and 4 patient required hospitalization. Multivariate analysis was performed without obtaining differences in any of the systemic autoimmune diseases. Regarding the treatments, significant differences were found (p 0.011) in the treatment with anti-TNF-alpha agents with OR 3.422 (1.322-8.858) and a trend to significance (p 0.094) was observed in patients receiving mycophenolate treatment [OR 2.016 (0.996-4-081)]. CONCLUSIONS: Anti-TNF-alpha treatment was associated with more than 3-fold risk of suffering from SARS-CoV-2 infection, although in all cases infection was mild. Cumulative incidence in patients with SAD was up to 5 times higher than general population but with great differences between autoimmune diseases.

Integrated analysis of circulating immune cellular and soluble mediators reveals specific COVID19 signatures at hospital admission with utility for prediction of clinical outcomes.

Coronavirus disease 2019 (COVID19), caused by SARS-CoV-2, is a complex disease, with a variety of clinical manifestations ranging from asymptomatic infection or mild cold-like symptoms to more severe cases requiring hospitalization and critical care. The most severe presentations seem to be related with a delayed, deregulated immune response leading to exacerbated inflammation and organ damage with close similarities to sepsis. Methods: In order to improve the understanding on the relation between host immune response and disease course, we have studied the differences in the cellular (monocytes, CD8+ T and NK cells) and soluble (cytokines, chemokines and immunoregulatory ligands) immune response in blood between Healthy Donors (HD), COVID19 and a group of patients with non-COVID19 respiratory tract infections (NON-COV-RTI). In addition, the immune response profile has been analyzed in COVID19 patients according to disease severity. Results: In comparison to HDs and patients with NON-COV-RTI, COVID19 patients show a heterogeneous immune response with the presence of both activated and exhausted CD8+ T and NK cells characterised by the expression of the immune checkpoint LAG3 and the presence of the adaptive NK cell subset. An increased frequency of adaptive NK cells and a reduction of NK cells expressing the activating receptors NKp30 and NKp46 correlated with disease severity. Although both activated and exhausted NK cells expressing LAG3 were increased in moderate/severe cases, unsupervised cell clustering analyses revealed a more complex scenario with single NK cells expressing more than one immune checkpoint (PD1, TIM3 and/or LAG3). A general increased level of inflammatory cytokines and chemokines was found in COVID19 patients, some of which like IL18, IL1RA, IL36B and IL31, IL2, IFNα and TNFα, CXCL10, CCL2 and CCL8 were able to differentiate between COVID19 and NON-COV-RTI and correlated with bad prognosis (IL2, TNFα, IL1RA, CCL2, CXCL10 and CXCL9). Notably, we found that soluble NKG2D ligands from the MIC and ULBPs families were increased in COVID19 compared to NON-COV-RTI and correlated with disease severity. Conclusions: Our results provide a detailed comprehensive analysis of the presence of activated and exhausted CD8+T, NK and monocyte cell subsets as well as extracellular inflammatory factors beyond cytokines/chemokines, specifically associated to COVID19. Importantly, multivariate analysis including clinical, demographical and immunological experimental variables have allowed us to reveal specific immune signatures to i) differentiate COVID19 from other infections and ii) predict disease severity and the risk of death.

Multiple Approaches at Admission Based on Lung Ultrasound and Biomarkers Improves Risk Identification in COVID-19 Patients.

BACKGROUND: Risk stratification of COVID-19 patients is fundamental to improving prognosis and selecting the right treatment. We hypothesized that a combination of lung ultrasound (LUZ-score), biomarkers (sST2), and clinical models (PANDEMYC score) could be useful to improve risk stratification. METHODS: This was a prospective cohort study designed to analyze the prognostic value of lung ultrasound, sST2, and PANDEMYC score in COVID-19 patients. The primary endpoint was in-hospital death and/or admission to the intensive care unit. The total length of hospital stay, increase of oxygen flow, or escalated medical treatment during the first 72 h were secondary endpoints. RESULTS: a total of 144 patients were included; the mean age was 57.5 ± 12.78 years. The median PANDEMYC score was 243 (52), the median LUZ-score was 21 (10), and the median sST2 was 53.1 ng/mL (30.9). Soluble ST2 showed the best predictive capacity for the primary endpoint (AUC = 0.764 (0.658-0.871); p = 0.001), towards the PANDEMYC score (AUC = 0.762 (0.655-0.870); p = 0.001) and LUZ-score (AUC = 0.749 (0.596-0.901); p = 0.002). Taken together, these three tools significantly improved the risk capacity (AUC = 0.840 (0.727-0.953); p ≤ 0.001). CONCLUSIONS: The PANDEMYC score, lung ultrasound, and sST2 concentrations upon admission for COVID-19 are independent predictors of intra-hospital death and/or the need for admission to the ICU for mechanical ventilation. The combination of these predictive tools improves the predictive power compared to each one separately. The use of decision trees, based on multivariate models, could be useful in clinical practice.

Point-of-care lung ultrasound assessment for risk stratification and therapy guiding in COVID-19 patients: a prospective noninterventional study.

BACKGROUND: Lung ultrasound is feasible for assessing lung injury caused by coronavirus disease 2019 (COVID-19). However, the prognostic meaning and time-line changes of lung injury assessed by lung ultrasound in COVID-19 hospitalised patients are unknown. METHODS: Prospective cohort study designed to analyse prognostic value of lung ultrasound in COVID-19 patients by using a quantitative scale (lung ultrasound Zaragoza (LUZ)-score) during the first 72 h after admission. The primary end-point was in-hospital death and/or admission to the intensive care unit. Total length of hospital stay, increase of oxygen flow and escalation of medical treatment during the first 72 h were secondary end-points. RESULTS: 130 patients were included in the final analysis; mean±sd age was 56.7±13.5 years. Median (interquartile range) time from the beginning of symptoms to admission was 6 (4-9) days. Lung injury assessed by LUZ-score did not differ during the first 72 h (21 (16-26) points at admission versus 20 (16-27) points at 72 h; p=0.183). In univariable logistic regression analysis, estimated arterial oxygen tension/inspiratory oxygen fraction ratio (PAFI) (hazard ratio 0.99, 95% CI 0.98-0.99; p=0.027) and LUZ-score >22 points (5.45, 1.42-20.90; p=0.013) were predictors for the primary end-point. CONCLUSIONS: LUZ-score is an easy, simple and fast point-of-care ultrasound tool to identify patients with severe lung injury due to COVID-19, upon admission. Baseline score is predictive of severity along the whole period of hospitalisation. The score facilitates early implementation or intensification of treatment for COVID-19 infection. LUZ-score may be combined with clinical variables (as estimated by PAFI) to further refine risk stratification.

Early Measurement of Blood sST2 Is a Good Predictor of Death and Poor Outcomes in Patients Admitted for COVID-19 Infection.

Although several biomarkers have shown correlation to prognosis in COVID-19 patients, their clinical value is limited because of lack of specificity, suboptimal sensibility or poor dynamic behavior. We hypothesized that circulating soluble ST2 (sST2) could be associated to a worse outcome in COVID-19. In total, 152 patients admitted for confirmed COVID-19 were included in a prospective non-interventional, observational study. Blood samples were drawn at admission, 48-72 h later and at discharge. sST2 concentrations and routine blood laboratory were analyzed. Primary endpoints were admission at intensive care unit (ICU) and mortality. Median age was 57.5 years [Standard Deviation (SD: 12.8)], 60.4% males. 10% of patients (n = 15) were derived to ICU and/or died during admission. Median (IQR) sST2 serum concentration (ng/mL) rose to 53.1 (30.9) at admission, peaked at 48-72 h (79.5(64)) and returned to admission levels at discharge (44.9[36.7]). A concentration of sST2 above 58.9 ng/mL was identified patients progressing to ICU admission or death. Results remained significant after multivariable analysis. The area under the receiver operating characteristics curve (AUC) of sST2 for endpoints was 0.776 (p = 0.001). In patients admitted for COVID-19 infection, early measurement of sST2 was able to identify patients at risk of severe complications or death.