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SARS-CoV-2 antigen rapid diagnostic tests (Ag-RDTs) are increasingly being integrated in testing strategies around the world. Studies of the Ag-RDTs have shown variable performance. In this systematic review and meta-analysis, we assessed the clinical accuracy (sensitivity and specificity) of commercially available Ag-RDTs. We registered the review on PROSPERO (registration number: CRD42020225140). We systematically searched multiple databases (PubMed, Web of Science Core Collection, medRvix, bioRvix, and FIND) for publications evaluating the accuracy of Ag-RDTs for SARS-CoV-2 up until 30 April 2021. Descriptive analyses of all studies were performed, and when more than 4 studies were available, a random-effects meta-analysis was used to estimate pooled sensitivity and specificity in comparison to reverse transcription polymerase chain reaction (RT-PCR) testing. We assessed heterogeneity by subgroup analyses, and rated study quality and risk of bias using the QUADAS-2 assessment tool. From a total of 14,254 articles, we included 133 analytical and clinical studies resulting in 214 clinical accuracy datasets with 112,323 samples. Across all meta-analyzed samples, the pooled Ag-RDT sensitivity and specificity were 71.2% (95% CI 68.2% to 74.0%) and 98.9% (95% CI 98.6% to 99.1%), respectively. Sensitivity increased to 76.3% (95% CI 73.1% to 79.2%) if analysis was restricted to studies that followed the Ag-RDT manufacturers' instructions. LumiraDx showed the highest sensitivity, with 88.2% (95% CI 59.0% to 97.5%). Of instrument-free Ag-RDTs, Standard Q nasal performed best, with 80.2% sensitivity (95% CI 70.3% to 87.4%). Across all Ag-RDTs, sensitivity was markedly better on samples with lower RT-PCR cycle threshold (Ct) values, i.e., <20 (96.5%, 95% CI 92.6% to 98.4%) and <25 (95.8%, 95% CI 92.3% to 97.8%), in comparison to those with Ct ≥ 25 (50.7%, 95% CI 35.6% to 65.8%) and ≥30 (20.9%, 95% CI 12.5% to 32.8%). Testing in the first week from symptom onset resulted in substantially higher sensitivity (83.8%, 95% CI 76.3% to 89.2%) compared to testing after 1 week (61.5%, 95% CI 52.2% to 70.0%). The best Ag-RDT sensitivity was found with anterior nasal sampling (75.5%, 95% CI 70.4% to 79.9%), in comparison to other sample types (e.g., nasopharyngeal, 71.6%, 95% CI 68.1% to 74.9%), although CIs were overlapping. Concerns of bias were raised across all datasets, and financial support from the manufacturer was reported in 24.1% of datasets. Our analysis was limited by the included studies' heterogeneity in design and reporting. In this study we found that Ag-RDTs detect the vast majority of SARS-CoV-2-infected persons within the first week of symptom onset and those with high viral load. Thus, they can have high utility for diagnostic purposes in the early phase of disease, making them a valuable tool to fight the spread of SARS-CoV-2. Standardization in conduct and reporting of clinical accuracy studies would improve comparability and use of data.

COVID-19 has been reported in over 40million people globally with variable clinical outcomes. In this systematic review and meta-analysis, we assessed demographic, laboratory and clinical indicators as predictors for severe courses of COVID-19. This systematic review was registered at PROSPERO under CRD42020177154. We systematically searched multiple databases (PubMed, Web of Science Core Collection, MedRvix and bioRvix) for publications from December 2019 to May 31st 2020. Random-effects meta-analyses were used to calculate pooled odds ratios and differences of medians between (1) patients admitted to ICU versus non-ICU patients and (2) patients who died versus those who survived. We adapted an existing Cochrane risk-of-bias assessment tool for outcome studies. Of 6,702 unique citations, we included 88 articles with 69,762 patients. There was concern for bias across all articles included. Age was strongly associated with mortality with a difference of medians (DoM) of 13.15 years (95% confidence interval (CI) 11.37 to 14.94) between those who died and those who survived. We found a clinically relevant difference between non-survivors and survivors for C-reactive protein (CRP; DoM 69.10 mg/L, CI 50.43 to 87.77), lactate dehydrogenase (LDH; DoM 189.49 U/L, CI 155.00 to 223.98), cardiac troponin I (cTnI; DoM 21.88 pg/mL, CI 9.78 to 33.99) and D-Dimer (DoM 1.29mg/L, CI 0.9 to 1.69). Furthermore, cerebrovascular disease was the co-morbidity most strongly associated with mortality (Odds Ratio 3.45, CI 2.42 to 4.91) and ICU admission (Odds Ratio 5.88, CI 2.35 to 14.73). This comprehensive meta-analysis found age, cerebrovascular disease, CRP, LDH and cTnI to be the most important risk-factors that predict severe COVID-19 outcomes and will inform clinical scores to support early decision-making.

We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI ArrayExpress to create a comprehensive meta-analysis microarray expression set (meta-expression set). Measurements had to be obtained via microarray-technique from whole blood samples of adult or pediatric patients with sepsis diagnosed based on international consensus definition immediately after admission to the intensive care unit. We aggregate trauma patients, systemic inflammatory response syndrome (SIRS) patients, and healthy controls in a non-septic entity. Differential expression (DE) analysis is compared with machine-learning-based solutions like decision tree (DT), random forest (RF), support vector machine (SVM), and deep-learning neural networks (DNNs). We evaluated classifier training and discrimination performance in 100 independent iterations. To test diagnostic resilience, we gradually degraded expression data in multiple levels. Clustering of expression values based on DE genes results in partial identification of sepsis samples. In contrast, RF, SVM, and DNN provide excellent diagnostic performance measured in terms of accuracy and area under the curve (>0.96 and >0.99, respectively). We prove DNNs as the most resilient methodology, virtually unaffected by targeted removal of DE genes. By surpassing most other published solutions, the presented approach substantially augments current diagnostic capability in intensive care medicine.

Increasing evidence supports a central role of the immune system in sepsis, but the current view of how sepsis affects immunity, and vice versa, is still rudimentary. The European Group on Immunology of Sepsis has identified major gaps that should be addressed with high priority, such as understanding how immunological alterations predispose to sepsis, key aspects of the immunopathological events during sepsis, and the long-term consequences of sepsis on patient's immunity. We discuss major unmet topics in those three categories, including the role of key immune cells, the cause of lymphopenia, organ-specific immunology, the dynamics of sepsis-associated immunological alterations, the role of the microbiome, the standardisation of immunological tests, the development of better animal models, and the opportunities offered by immunotherapy. Addressing these gaps should help us to better understand sepsis physiopathology, offering translational opportunities to improve its prevention, diagnosis, and care.

The COVID-19 pandemic has exerted great pressure on national health systems, which have aimed to ensure comprehensive healthcare at all times. Healthcare professionals working with COVID-19 patients are on the frontline and thereby confronted with enormous demands. Although early reports exist on the psychological impact of the pandemic on frontline medical staff working in Asia, little is known about its impact on healthcare professionals in other countries and across various work sectors. The present cross-sectional, online survey sought to investigate common work stressors among healthcare professionals, their psychological stress as well as coping resources during the pandemic. A sample of 575 healthcare professionals (57% male) in three different sectors (hospital, prehospital emergency care, and outpatient service) reported their experiences concerning work and private stressors, psychological stress, and coping strategies between April 17, 2020 and June 5, 2020. To capture pandemic-specific answers, most of the items were adapted or newly developed. Exploratory factor analyses (EFA) were conducted to detect underlying latent factors relating to COVID-specific work stressors. In a next step, the effects of these latent stressors across various work sectors on psychological stress (perceived stress, fatigue, and mood) were examined by means of structural equation models (SEM). To add lived experience to the findings, responses to open-ended questions about healthcare professionals' stressors, effective crisis measures and prevention, and individual coping strategies were coded inductively, and emergent themes were identified. The EFA revealed that the examined work stressors can be grouped into four latent factors: \"fear of transmission\", \"interference of workload with private life\", \"uncertainty/lack of knowledge\", and \"concerns about the team\". The SEM results showed that \"interference of workload with private life\" represented the pivotal predictor of psychological stress. \"Concerns about the team\" had stress-reducing effects. The latent stressors had an equal effect on psychological stress across work sectors. On average, psychological stress levels were moderate, yet differed significantly between sectors (all p < .001); the outpatient group experienced reduced calmness and more stress than the other two sectors, while the prehospital group reported lower fatigue than the other two sectors. The prehospital group reported significantly higher concerns about the team than the hospital group (p < .001). In their reports, healthcare professionals highlighted regulations such as social distancing and the use of compulsory masks, training, experience and knowledge exchange, and social support as effective coping strategies during the pandemic. The hospital group mainly mentioned organizational measures such as visiting bans as effective crisis measures, whereas the prehospital sector most frequently named governmental measures such as contact restrictions. The study demonstrated the need for sector-specific crisis measures to effectively address the specific work stressors faced by the outpatient sector in particular. The results on pandemic-specific work stressors reveal that healthcare professionals might benefit from coping strategies that facilitate the utilization of social support. At the workplace, team commitment and knowledge exchange might buffer against adverse psychological stress responses. Schedules during pandemics should give healthcare workers the opportunity to interact with families and friends in ways that facilitate social support outside work. Future studies should investigate cross-sector stressors using a longitudinal design to identify both sector- and time-specific measures. Ultimately, an international comparison of stressors and measures in different sectors of healthcare systems is desirable.

Background Bloodstream infections remain one of the major challenges in intensive care units, leading to sepsis or even septic shock in many cases. Due to the lack of timely diagnostic approaches with sufficient sensitivity, mortality rates of sepsis are still unacceptably high. However a prompt diagnosis of the causative microorganism is critical to significantly improve outcome of bloodstream infections. Although various targeted molecular tests for blood samples are available, time-consuming blood culture-based approaches still represent the standard of care for the identification of bacteria. Methods Here we describe the establishment of a complete diagnostic workflow for the identification of infectious microorganisms from seven septic patients based on unbiased sequence analyses of free circulating DNA from plasma by next-generation sequencing. Results We found significant levels of DNA fragments derived from pathogenic bacteria in samples from septic patients. Quantitative evaluation of normalized read counts and introduction of a sepsis indicating quantifier (SIQ) score allowed for an unambiguous identification of Gram-positive as well as Gram-negative bacteria that exactly matched with blood cultures from corresponding patient samples. In addition, we also identified species from samples where blood cultures were negative. Reads of non-human origin also comprised fragments derived from antimicrobial resistance genes, showing that, in principle, prediction of specific types of resistance might be possible. Conclusions The complete workflow from sample preparation to species identification report could be accomplished in roughly 30 h, thus making this approach a promising diagnostic platform for critically ill patients suffering from bloodstream infections.

Hypercoagulability and thrombo-inflammation are the main reasons for death in COVID-19 patients. It is unclear whether there is a difference between D-dimer levels in patients without or with COVID-19 acute respiratory distress syndrome (ARDS). We searched PubMed, EMBASE, and ClinicalTrails.gov databases looking for studies reporting D-dimer levels in patients without or with COVID-19 ARDS. Secondary endpoints included length of hospital stay, and mortality data at the longest follow-up available. We included 12 retrospective and 3 prospective studies with overall 2,828 patients, of whom 1,404 (49.6%) had non-COVID-19 ARDS and 1,424 had COVID-19 ARDS. D-dimer levels were not significantly higher in non-COVID-19 ARDS than in COVID-19 ARDS patients (mean 7.65 mg/L vs. mean 6.20 mg/L MD 0.88 [CI: -0.61 to 2.38] p = 0.25; I² = 85%) while the length of hospital stay was shorter (non-COVID-19 mean 37.4 days vs. COVID-19 mean 48.5 days, MD -10.92 [CI: -16.71 to -5.14] p < 0.001; I² = 44%). No difference in mortality was observed: non-COVID-19 ARDS 418/1167 (35.8%) vs. COVID-19 ARDS 467/1201 (38.8%). We found no difference in the mean D-dimer levels between non-COVID-19 ARDS and COVID-19 ARDS patients.

Here we report on five COVID-19 patients with a median age of 67 years who were admitted to the medical intensive care unit of Heidelberg University Hospital due to respiratory failure. [...]all patients had multi-organ failure with acute respiratory distress syndrome (ARDS, 4 severe, 1 moderate) and acute kidney injury of at least KDIGO stage 2. During the PE, striking reduction of inflammatory markers C-reactive protein (− 47%, P = 0.0078) and interleukin 6 (− 74%, P = 0.0078), as well as significant reduction of ferritin (− 49%, P = 0.0078), LDH (− 41%, P = 0.0078), and D-dimer (− 47%, P = 0.016) were observed (Fig. 1a–e).

BackgroundCOVID-19 pneumonia and subsequent respiratory failure is causing an immense strain on intensive care units globally. Early prediction of severe disease enables clinicians to avoid acute respiratory distress syndrome (ARDS) development and improve management of critically ill patients. The soluble receptor of advanced glycation endproducts (sRAGE) is a biomarker shown to predict ARDS. Although sRAGE level varies depending on the type of disease, there is limited information available on changes in sRAGE levels in COVID-19. Therefore, sRAGE was measured in COVID-19 patients to determine sRAGE level variation in COVID-19 severity and to examine its ability to predict the need for mechanical ventilation (MV) and mortality in COVID-19.MethodsIn this single-centre observational cohort study in Germany, serum sRAGE during acute COVID-19, 20 weeks after the start of COVID-19 symptoms, as well as in control groups of non-COVID-19 pneumonia patients and healthy controls were measured using ELISA. The primary endpoint was severe disease (high-flow nasal oxygen therapy (HFNO)/MV and need of organ support). The secondary endpoints were respiratory failure with need of MV and 30-day mortality. The area under the curve (AUC), cut-off based on Youden’s index and odds ratio with 95% CI for sRAGE were calculated with regard to prediction of MV need and mortality.ResultsSerum sRAGE in 164 COVID-19 patients, 101 matched COVID-19 convalescent patients, 23 non-COVID-19 pneumonia patients and 15 healthy volunteers were measured. sRAGE level increased with COVID-19 severity, need for oxygen therapy, HFNO/MV, ARDS severity, need of dialysis and catecholamine support, 30-day mortality, sequential organ failure assessment (SOFA) and quick SOFA (qSOFA) score. sRAGE was found to be a good predictor of MV need in COVID-19 inpatients and mortality with an AUC of 0.871 (0.770–0.973) and 0.903 (0.817–0.990), respectively. When adjusted for male gender, age, comorbidity and SOFA score ≥ 3, sRAGE was independently associated with risk of need for HFNO/MV. When adjusted for SOFA score ≥ 3, sRAGE was independently associated with risk of need for MV.ConclusionsSerum sRAGE concentrations are elevated in COVID-19 patients as disease severity increases. sRAGE should be considered as a biomarker for predicting the need for MV and mortality in COVID-19.

With more than 18 million annual new cases, cancer belongs to the major challenges of modern healthcare. Surgical resection of solid tumours under general anaesthesia is the prime therapy. Different aspects of anaesthesia are under discussion to independently influence the long-term outcome of cancer patients. Most recently, the commonly used volatile anaesthetics like sevoflurane have entered the spotlight, as retrospective studies suggest a detrimental outcome in certain cancer aetiologies with sparse mechanistic understanding. Our objective was to investigate this concept in a murine melanoma model, herein comparing the consequence of inhalative and injection anesthesia on tumour composition and growth. We used a murine model of malignant melanoma in male, adult C57BL/6 mice (n = 92), induced by the subcutaneous injection of B16-F10 cells. We either exposed the melanoma cells to sevoflurane before implantation or subjected the animals to single or double anaesthesia with either volatile or injection drugs. After a maximum follow-up of 4 weeks, leucocytes within the tumour microenvironment (TME) were comprehensively analysed by flow cytometry with focus on tumor-associated macrophages (TAM). We found that exposure of melanoma cells to sevoflurane before implantation induced long-lasting transcriptome changes and aggravated tumour growth, without extensive changes of the TME. Contrastingly, both a single and double anaesthesia with sevoflurane led to a significant reduction of TAMs (injection vs. sevoflurane: 2,0 vs. 0.3% and 1.2 vs. 0.6%, respectively), whilst increasing PD-L1 expression on the remaining cells (mean fluorescent intensity injection vs. sevoflurane: 3,804 vs. 7,143 and 9,090 vs. 32,228, respectively). No changes in tumour growth were observed in these groups. In sharp contrast to the detrimental impact of sevoflurane on patients' outcome reported in retrospective clinical studies, we propose here that sevoflurane might actually exert a beneficial effect by decreasing TAMs within the TME, rendering the tumour again susceptible for cytotoxic T cells and immunotherapies. Further research is warranted to delineate, how these results translate into the clinic.