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BackgroundComprehensive information about the accuracy of antigen rapid diagnostic tests (Ag-RDTs) for SARS-CoV-2 is essential to guide public health decision makers in choosing the best tests and testing policies. In August 2021, we published a systematic review and meta-analysis about the accuracy of Ag-RDTs. We now update this work and analyze the factors influencing test sensitivity in further detail. Methods and findingsWe 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 until August 31, 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 with reverse transcription polymerase chain reaction (RT-PCR) testing as a reference. To evaluate factors influencing test sensitivity, we performed 3 different analyses using multivariate mixed-effects meta-regression models. We included 194 studies with 221,878 Ag-RDTs performed. Overall, the pooled estimates of Ag-RDT sensitivity and specificity were 72.0% (95% confidence interval [CI] 69.8 to 74.2) and 98.9% (95% CI 98.6 to 99.1), respectively. When manufacturer instructions were followed, sensitivity increased to 76.4% (95%CI 73.8 to 78.8). Sensitivity was markedly better on samples with lower RT-PCR cycle threshold (Ct) values (sensitivity of 97.9% [95% CI 96.9 to 98.9] and 90.6% [95% CI 88.3 to 93.0] for Ct-values <20 and <25, compared to 54.4% [95% CI 47.3 to 61.5] and 18.7% [95% CI 13.9 to 23.4] for Ct-values [≥]25 and [≥]30) and was estimated to increase by 2.9 percentage points (95% CI 1.7 to 4.0) for every unit decrease in mean Ct-value when adjusting for testing procedure and patients symptom status. Concordantly, we found the mean Ct-value to be lower for true positive (22.2 [95% CI 21.5 to 22.8]) compared to false negative (30.4 [95% CI 29.7 to 31.1]) results. Testing in the first week from symptom onset resulted in substantially higher sensitivity (81.9% [95% CI 77.7 to 85.5]) compared to testing after 1 week (51.8%, 95% CI 41.5 to 61.9). Similarly, sensitivity was higher in symptomatic (76.2% [95% CI 73.3 to 78.9]) compared to asymptomatic (56.8% [95% CI 50.9 to 62.4]) persons. However, both effects were mainly driven by the Ct-value of the sample. With regards to sample type, highest sensitivity was found for nasopharyngeal (NP) and combined NP/oropharyngeal samples (70.8% [95% CI 68.3 to 73.2]), as well as in anterior nasal/mid-turbinate samples (77.3% [95% CI 73.0 to 81.0]). ConclusionAg-RDTs detect most of the individuals infected with SARS-CoV-2, and almost all when high viral loads are present (>90%). With viral load, as estimated by Ct-value, being the most influential factor on their sensitivity, they are especially useful to detect persons with high viral load who are most likely to transmit the virus. To further quantify the effects of other factors influencing test sensitivity, standardization of clinical accuracy studies and access to patient level Ct-values and duration of symptoms are needed.
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ABSTRACT Background 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. Methods and Results We registered the review on PROSPERO (Registration number: CRD42020225140). We systematically searched multiple databases (PubMed, Web of Science Core Collection, medRvix and bioRvix, FIND) for publications evaluating the accuracy of Ag-RDTs for SARS-CoV-2 up until April 30 th , 2021. Descriptive analyses of all studies were performed and when more than four studies were available, a random-effects meta-analysis was used to estimate pooled sensitivity and specificity in comparison to reverse transcriptase polymerase chain reaction 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 data sets with 112,323 samples. Across all meta-analyzed samples, the pooled Ag-RDT sensitivity was 71.2% (95% confidence interval [CI] 68.2 to 74.0) and increased to 76.3% (CI 73.1 to 79.2) if analysis was restricted to studies that followed the Ag-RDT manufacturers’ instructions. The LumiraDx showed the highest sensitivity with 88.2% (CI 59.0 to 97.5). Of instrument-free Ag-RDTs, Standard Q nasal performed best with 80.2% sensitivity (CI 70.3 to 87.4). Across all Ag-RDTs sensitivity was markedly better on samples with lower Ct-values, i.e., <20 (96.5%, CI 92.6 to 98.4) and <25 (95.8%, CI 92.3 to 97.8), in comparison to those with Ct ≥25 (50.7%, CI 35.6 to 65.8) and ≥30 (20.9%, CI 12.5 to 32.8). Testing in the first week from symptom onset resulted in substantially higher sensitivity (83.8%, CI 76.3 to 89.2) compared to testing after one week (61.5%, CI 52.2 to 70.0). The best Ag-RDT sensitivity was found with anterior nasal sampling (75.5%, CI 70.4 to 79.9) in comparison to other sample types (e.g., nasopharyngeal 71.6%, CI 68.1 to 74.9) although CIs were overlapping. Concerns of bias were raised across all data sets, and financial support from the manufacturer was reported in 24.1% of data sets. Our analysis was limited by the included studies’ heterogeneity in design and reporting, making it difficult to draw conclusions from. Conclusion In this study we found that Ag-RDTs detect the vast majority of cases 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. AUTHOR SUMMARY Why was this study done? – Antigen rapid diagnostic tests (Ag-RDTs) are considered an important diagnostic tool to fight the spread of SARS-CoV-2 – An increasing number of Ag-RDTs is offered on the market, and a constantly growing body of literature evaluating their performance is available – To inform decision makers about the best test to choose, an up to date summary of their performance is needed What did the researchers do and find? – On a weekly basis, we search multiple data bases for evaluations of Ag-RDTs detecting SARS-CoV-2 and post the results on www.diagnosticsglobalhealth.org – Based on the search results up until April 30 th , 2021, we conducted a systematic review and meta-analysis, including a total of 133 clinical and analytical accuracy studies – Across all meta-analyzed studies, when Ag-RDTs were performed according to manufacturers’ recommendations, they showed a sensitivity of 76.3% (CI 73.1 to 79.2), with the LumiraDx (sensitivity 88.2%, CI 59.0 to 97.5) and of the instrument-free Ag-RDT Standard Q (74.9% sensitivity, CI 69.3 to 79.7) performing best. – Across all Ag-RDTs, sensitivity increased to 95.8% (CI 92.3 to 97.8) when restricting the analysis to samples with high viral loads (i.e., a Ct-value <25) and to 83.8% (CI 76.3 to 89.2) when tests were performed on patients within the first week after symptom onset What do these findings mean? – Ag-RDTs detect the vast majority of cases 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 – Out of all assessed tests, the Lumira Dx showed the highest accuracy. The Standard Q wasthe best performing test when only considering those that don’t require an instrument – A standardization of reporting methods for clinical accuracy studies would enhance future test-comparisons
Résumé
Currently, more than 33 million peoples have been infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and more than a million people died from coronavirus disease 2019 (COVID-19), a disease caused by the virus. There have been multiple reports of autoimmune and inflammatory diseases following SARS-CoV-2 infections. There are several suggested mechanisms involved in the development of autoimmune diseases, including cross-reactivity (molecular mimicry). A typical workflow for discovering cross-reactive epitopes (mimotopes) starts with a sequence similarity search between protein sequences of human and a pathogen. However, sequence similarity information alone is not enough to predict cross-reactivity between proteins since proteins can share highly similar conformational epitopes whose amino acid residues are situated far apart in the linear protein sequences. Therefore, we used a hidden Markov model-based tool to identify distant viral homologs of human proteins. Also, we utilized experimentally determined and modeled protein structures of SARS-CoV-2 and human proteins to find homologous protein structures between them. Next, we predicted binding affinity (IC50) of potentially cross-reactive T-cell epitopes to 34 MHC allelic variants that have been associated with autoimmune diseases using multiple prediction algorithms. Overall, from 8,138 SARS-CoV-2 genomes, we identified 3,238 potentially cross-reactive B-cell epitopes covering six human proteins and 1,224 potentially cross-reactive T-cell epitopes covering 285 human proteins. To visualize the predicted cross-reactive T-cell and B-cell epitopes, we developed a web-based application "Molecular Mimicry Map (3M) of SARS-CoV-2" (available at https://ahs2202.github.io/3M/). The web application enables researchers to explore potential cross-reactive SARS-CoV-2 epitopes alongside custom peptide vaccines, allowing researchers to identify potentially suboptimal peptide vaccine candidates or less ideal part of a whole virus vaccine to design a safer vaccine for people with genetic and environmental predispositions to autoimmune diseases. Together, the computational resources and the interactive web application provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune disease following COVID-19.
Sujets)
COVID-19 , Maladies auto-immunes , Syndrome respiratoire aigu sévèreRésumé
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: 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, CI 50.43 to 87.77), lactate dehydrogenase (LDH; DoM 189.49, CI 155.00 to 223.98), cardiac troponin I (cTnI; DoM 21.88, CI 9.78 to 33.99) and D-Dimer (DoM 1.29mg/L, CI 0.9 - 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 in predicting severe COVID-19 outcomes and will inform decision analytical tools to support clinical decision-making.