Consistency as a Data Quality Measure for German Corona Consensus items mapped from National Pandemic Cohort Network data collections (preprint)

Background As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from COVID-19 patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO. As mapping from one information model to another is complex, an additional consistency evaluation of the mapped items is recommended to detect possible mapping issues or source data inconsistencies.Objectives The goal of this work is to assure high consistency of research data mapped to the GECCO data model. In particular, it aims at identifying contradictions within interdependent GECCO data items of the German national COVID-19 cohorts to allow investigation of possible reasons for identified contradictions. We furthermore aim at enabling other researchers to easily perform data quality evaluation on GECCO-based datasets and adapt to similar data models.Methods All suitable data items from each of the three NAPKON cohorts are mapped to the GECCO items. A consistency assessment tool (dqGecco) is implemented, following the design of an existing quality assessment framework, retaining their-defined consistency taxonomies, including logical and empirical contradictions. Results of the assessment are verified independently on the primary data source.Results Our consistency assessment tool helped in correcting the mapping procedure and reveals remaining contradictory value combinations within COVID-19 symptoms, vital-signs, and COVID-19 severity. Consistency rates differ between the different indicators and cohorts ranging from 95.84% up to 100%.Conclusion An efficient and portable tool capable to discover inconsistencies in the COVID-19 domain has been developed and applied to three different cohorts. As the GECCO dataset is employed in different platforms and studies, the tool can be directly applied there or adapted to similar information models.

Proteome reveals antiviral host response and NETosis during acute COVID-19 in high-risk patients (preprint)

SARS-CoV-2 remains an acute threat to human health, endangering hospital capacities worldwide. Many studies have aimed at informing pathophysiologic understanding and identification of disease indicators for risk assessment, monitoring, and therapeutic guidance. While findings start to emerge in the general population, observations in high-risk patients with complex pre-existing conditions are limited. To this end, we biomedically characterized quantitative proteomics in a hospitalized cohort of COVID-19 patients with mild to severe symptoms suffering from different (co)-morbidities in comparison to both healthy individuals and patients with non-COVID related inflammation. Deep clinical phenotyping enabled the identification of individual disease trajectories in COVID-19 patients. By the use of this specific disease phase assignment, proteome analysis revealed a severity dependent general type-2 centered host response side-by-side with a disease specific antiviral immune reaction in early disease. The identification of phenomena such as neutrophil extracellular trap (NET) formation and a pro-coagulatory response together with the regulation of proteins related to SARS-CoV-2-specific symptoms by unbiased proteome screening both confirms results from targeted approaches and provides novel information for biomarker and therapy development. Graphical AbstractSars-CoV-2 remains a challenging threat to our health care system with many pathophysiological mechanisms not fully understood, especially in high-risk patients. Therefore, we characterized a cohort of hospitalized COVID-19 patients with multiple comorbidities by quantitative plasma proteomics and deep clinical phenotyping. The individual patients disease progression was determined and the subsequently assigned proteome profiles compared with a healthy and a chronically inflamed control cohort. The identified disease phase and severity specific protein profiles revealed an antiviral immune response together with coagulation activation indicating the formation of NETosis side-by-side with tissue remodeling related to the inflammatory signature. O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/22271106v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@bab525org.highwire.dtl.DTLVardef@1cac7e7org.highwire.dtl.DTLVardef@a3ab1org.highwire.dtl.DTLVardef@19375fb_HPS_FORMAT_FIGEXP M_FIG C_FIG

Anti-SARS-CoV-2 Spike Protein and Anti-Platelet Factor 4 Antibody Responses Induced by COVID-19 Disease and ChAdOx1 nCov-19 vaccination (preprint)

Background: Some recipients of ChAdOx1 nCoV-19 COVID-19 Vaccine AstraZeneca develop antibody-mediated vaccine-induced thrombotic thrombocytopenia (VITT), associated with cerebral venous and other unusual thrombosis resembling autoimmune heparin-induced thrombocytopenia. A prothrombotic predisposition is also observed in Covid-19. We explored whether antibodies against the SARS-CoV-2 spike protein induced by Covid-19 cross-react with platelet factor 4 (PF4/CXLC4), the protein targeted in both VITT and autoimmune heparin-induced thrombocytopenia.Methods: Immunogenic epitopes of PF4 and SARS-CoV-2 spike protein were compared via prediction tools and 3D modelling software (IMED, SIM, MacMYPOL). Sera from 222 PCR-confirmed Covid-19 patients from five European centers were tested by PF4/heparin ELISA, heparin-dependent and PF4-dependent platelet activation assays. Immunogenic reactivity of purified anti-PF4 and anti-PF4/heparin antibodies from patients with VITT were tested against recombinant SARS-CoV-2 spike protein. Results: Three motifs within the spike protein sequence share a potential immunogenic epitope with PF4. Nineteen of 222 (8.6%) Covid-19 patient sera tested positive in the IgG-specific PF4/heparin ELISA, none of which showed platelet activation in the heparin-dependent activation assay, including 10 (4.5%) of the 222 Covid-19 patients who developed thromboembolic complications. Purified anti-PF4 and anti-PF4/heparin antibodies from two VITT patients did not show cross-reactivity to recombinant SARS-CoV-2 spike protein. Conclusions: The antibody responses to PF4 in SARS-CoV-2 infection and after vaccination with COVID-19 Vaccine AstraZeneca differ. Antibodies against SARS-CoV-2 spike protein do not cross-react with PF4 or PF4/heparin complexes through molecular mimicry. These findings make it very unlikely that the intended vaccine-induced immune response against SARS-CoV-2 spike protein would itself induce VITT. 

Level of IL-6 predicts respiratory failure in hospitalized symptomatic COVID-19 patients (preprint)

The pandemic Coronavirus-disease 19 (COVID-19) is characterized by a heterogeneous clinical course. While most patients experience only mild symptoms, a relevant proportion develop severe disease progression with increasing hypoxia up to acute respiratory distress syndrome. The substantial number of patients with severe disease have strained intensive care capacities to an unprecedented level. Owing to the highly variable course and lack of reliable predictors for deterioration, we aimed to identify variables that allow the prediction of patients with a high risk of respiratory failure and need of mechanical ventilation Patients with PCR proven symptomatic COVID-19 infection hospitalized at our institution from 29th February to 27th March 2020 (n=40) were analyzed for baseline clinical and laboratory findings. Patients requiring mechanical ventilation 13/40 (32.5%) did not differ in age, comorbidities, radiological findings, respiratory rate or qSofa score. However, elevated interleukin-6 (IL-6) was strongly associated with the need for mechanical ventilation (p=1.2.10-5). In addition, the maximal IL-6 level (cutoff 80 pg/ml) for each patient during disease predicted respiratory failure with high accuracy (p=1.7.10-8, AUC=0.98). The risk of respiratory failure for patients with IL-6 levels of [≥] 80 pg/ml was 22 times higher compared to patients with lower IL-6 levels. In the current situation with overwhelmed intensive care units and overcrowded emergency rooms, correct triage of patients in need of intensive care is crucial. Our study shows that IL-6 is an effective marker that might be able to predict upcoming respiratory failure with high accuracy and help physicians correctly allocate patients at an early stage.