Accuracy and ease-of-use of seven point-of-care SARS-CoV-2 antigen-detecting tests: A multi-centre clinical evaluation.

BACKGROUND: Antigen-detecting rapid diagnostic tests (Ag-RDTs) for SARS-CoV-2 are important diagnostic tools. We assessed clinical performance and ease-of-use of seven Ag-RDTs in a prospective, manufacturer-independent, multi-centre cross-sectional diagnostic accuracy study to inform global decision makers. METHODS: Unvaccinated participants suspected of a first SARS-CoV-2 infection were recruited at six sites (Germany, Brazil). Ag-RDTs were evaluated sequentially, with collection of paired swabs for routine reverse transcription polymerase chain reaction (RT-PCR) testing and Ag-RDT testing. Performance was compared to RT-PCR overall and in sub-group analyses (viral load, symptoms, symptoms duration). To understandusability a System Usability Scale (SUS) questionnaire and ease-of-use (EoU) assessment were performed. FINDINGS: 7471 participants were included in the analysis. Sensitivities across Ag-RDTs ranged from 70·4%-90·1%, specificities were above 97·2% for all Ag-RDTs but one (93·1%).Ag-RDTs, Mologic, Bionote, Standard Q, showed diagnostic accuracy in line with WHO targets (> 80% sensitivity, > 97% specificity). All tests showed high sensitivity in the first three days after symptom onset (≥87·1%) and in individuals with viral loads≥ 6 log10SARS-CoV2 RNA copies/mL (≥ 88·7%). Usability varied, with Rapigen, Bionote and Standard Q reaching very good scores; 90, 88 and 84/100, respectively. INTERPRETATION: Variability in test performance is partially explained by variable viral loads in population evaluated over the course of the pandemic. All Ag-RDTs reach high sensitivity early in the disease and in individuals with high viral loads, supporting their role in identifying transmission relevant infections. For easy-to-use tests, performance shown will likely be maintained in routine implementation. FUNDING: Ministry of Science, Research and Arts, State of Baden-Wuerttemberg, Germany, internal funds from Heidelberg University Hospital, University Hospital Charité - Universitätsmedizin Berlin, UK Department of International Development, WHO, Unitaid.

The Abbott PanBio WHO emergency use listed, rapid, antigen-detecting point-of-care diagnostic test for SARS-CoV-2-Evaluation of the accuracy and ease-of-use.

OBJECTIVES: Diagnostics are essential for controlling the pandemic. Identifying a reliable and fast diagnostic device is needed for effective testing. We assessed performance and ease-of-use of the Abbott PanBio antigen-detecting rapid diagnostic test (Ag-RDT). METHODS: This prospective, multi-centre diagnostic accuracy study enrolled at two sites in Germany. Following routine testing with reverse-transcriptase polymerase chain reaction (RT-PCR), a second study-exclusive swab was performed for Ag-RDT testing. Routine swabs were nasopharyngeal (NP) or combined NP/oropharyngeal (OP) whereas the study-exclusive swabs were NP. To evaluate performance, sensitivity and specificity were assessed overall and in predefined sub-analyses accordingly to cycle-threshold values, days after symptom onset, disease severity and study site. Additionally, an ease-of-use assessment (EoU) and System Usability Scale (SUS) were performed. RESULTS: 1108 participants were enrolled between Sept 28 and Oct 30, 2020. Of these, 106 (9.6%) were PCR-positive. The Abbott PanBio detected 92/106 PCR-positive participants with a sensitivity of 86.8% (95% CI: 79.0% - 92.0%) and a specificity of 99.9% (95% CI: 99.4%-100%). The sub-analyses indicated that sensitivity was 95.8% in Ct-values <25 and within the first seven days from symptom onset. The test was characterized as easy to use (SUS: 86/100) and considered suitable for point-of-care settings. CONCLUSION: The Abbott PanBio Ag-RDT performs well for SARS-CoV-2 testing in this large manufacturer independent study, confirming its WHO recommendation for Emergency Use in settings with limited resources.

Can we predict the severe course of COVID-19 - a systematic review and meta-analysis of indicators of clinical outcome?

BACKGROUND: 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. METHODS: 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. RESULTS: 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). DISCUSSION: 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.

Prediction of local COVID-19 spread in Heidelberg.

Since the first identified case of COVID-19 in Wuhan, China, the disease has developed into a pandemic, imposing a major challenge for health authorities and hospitals worldwide. Mathematical transmission models can help hospitals to anticipate and prepare for an upcoming wave of patients by forecasting the time and severity of infections. Taking the city of Heidelberg as an example, we predict the ongoing spread of the disease for the next months including hospital and ventilator capacity and consider the possible impact of currently imposed countermeasures.