External Validation of the 4C Mortality Score and the qSOFA for Different Variants of Concerns of SARS-CoV-2 Using Data of the NAPKON Cross-Sectoral Cohort Platform (SUEP)

Background. Numerous predictive clinical scores with varying discriminatory performance have been developed in the context of the current coronavirus disease 2019 (COVID-19) pandemic. To support clinical application, we test the transferability of the frequently applied 4C mortality score (4C score) to the German prospective Cross-Sectoral Platform (SUEP) of the National Pandemic Cohort Network (NAPKON) compared to the non COVID-19 specific quick sequential organ failure assessment score (qSOFA). Our project aims to externally validate these two scores, stratified for the most prevalent variants of concerns (VOCs) of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) in Germany. Methods. A total of 685 adults with polymerase chain reaction (PCR)-detected SARS-CoV-2 infection were included from NAPKON-SUEP. Patients were recruited from 11/2020 to 03/2022 at 34 university and non-university hospitals across Germany. Missing values were complemented using multiple imputation. Predictive performance for in-hospital mortality at day of baseline visit was determined by area under the curve (AUC) with 95%-confidence interval (CI) stratified by VOCs of SARS-CoV-2 (alpha, delta, omicron) (Figure 1). Figure 1: Study flow chart with inclusion criteria and methodological workflow. Results. Preliminary results suggest a high predictive performance of the 4C score for in-hospital mortality (Table 1). This applies for the overall cohort (AUC 0.813 (95%CI 0.738-0.888)) as well as the VOC-strata (alpha: AUC 0.859 (95%CI 0.748-0.970);delta: AUC 0.769 (95%CI 0.657-0.882);omicron: AUC 0.866 (95%CI 0.724-1.000)). The overall mortality rates across the defined 4C score risk groups are 0.3% (low), 3.2% (intermediate), 11.6% (high), and 49.5% (very high). The 4C score performs significantly better than the qSOFA (Chi2-test: p=0.001) and the qSOFA does not seem to be a suitable tool in this context. Table 1: Discriminatory performance of the 4C Mortality Score and the qSOFA score within the validation cohort NAPKON-SUEP stratified by the Variant of Concerns of SARS-CoV- 2. Conclusion. Despite its development in the early phase of the pandemic and improved treatment, external validation of the 4C score in NAPKON-SUEP indicates a high predictive performance for in-hospital mortality across all VOCs. However, since the qSOFA was not specifically designed for this predictive issue, it shows low discriminatory performance, as in other validation studies. Any interpretations regarding the omicron stratum are limited due to the sample size.

A proof of concept study for the differentiation of SARS-CoV-2, hCoV-NL63, and IAV-H1N1 in vitro cultures using ion mobility spectrometry.

Rapid, high-throughput diagnostic tests are essential to decelerate the spread of the novel coronavirus disease 2019 (COVID-19) pandemic. While RT-PCR tests performed in centralized laboratories remain the gold standard, rapid point-of-care antigen tests might provide faster results. However, they are associated with markedly reduced sensitivity. Bedside breath gas analysis of volatile organic compounds detected by ion mobility spectrometry (IMS) may enable a quick and sensitive point-of-care testing alternative. In this proof-of-concept study, we investigated whether gas analysis by IMS can discriminate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from other respiratory viruses in an experimental set-up. Repeated gas analyses of air samples collected from the headspace of virus-infected in vitro cultures were performed for 5 days. A three-step decision tree using the intensities of four spectrometry peaks correlating to unidentified volatile organic compounds allowed the correct classification of SARS-CoV-2, human coronavirus-NL63, and influenza A virus H1N1 without misassignment when the calculation was performed with data 3 days post infection. The forward selection assignment model allowed the identification of SARS-CoV-2 with high sensitivity and specificity, with only one of 231 measurements (0.43%) being misclassified. Thus, volatile organic compound analysis by IMS allows highly accurate differentiation of SARS-CoV-2 from other respiratory viruses in an experimental set-up, supporting further research and evaluation in clinical studies.

Neurological implications of COVID-19-results of the LEOSS registry

Background and aims: Register studies and cohort analyses of clinical data are essential to study neurological manifestations of COVID-19 at a large scale. Methods: We analyzed neurological manifestations in COVID-19 patients, diagnosed before Aug 25th 2020, and registered in the European multinational LEOSS registry. Results: Of the 3127 COVID-19 patients, 95.2% were hospitalized. In 54.4% at least one neurological symptom, and in 3.3% a new neurological complication occurred. Preexisting neurological comorbidities were reported in 18.1% of the patients. Neurological symptoms were excessive tiredness (27.6%), headache (15.3%), nausea/emesis (14.0%), muscular weakness (13.2%), smell (6.9%), taste disorder (8.3%) and delirium (6.3%). Intracerebral bleeding occurred in 1.2%, ischemic stroke in 0.5%, and meningitis/ encephalitis in 0.4%. Overall, the death rate was 17.5%. It was higher in patients with the following neurological comorbidities: dementia 38.0%, movement disorders 32.8%, and prior cerebrovascular disease 32.3%. A multivariable logistic regression model found age (OR 1.53), cardiovascular diseases (OR 1.74), muscle weakness (OR 1.40), pulmonary diseases (1.49) and male gender (OR 1.52) to be associated with a significantly increased risk for a critical COVID-19 disease course, failed recovery, and death. Conclusion: The neurological manifestations revealed in COVID-19 patients of this study are mostly in agreement with previously published data. Several neurological conditions, such as prior cerebrovascular diseases or dementia appeared to be associated with a higher risk in unadjusted analyses, which was not confirmed in a multivariable analysis adjusting for confounding variables such as age and sex. These findings contrast previously published studies and stress the importance of considering putative confounds in medical statistics carefully.