1
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Diagnostic accuracy of Savanna RVP4 (QuidelOrtho) for the detection of Influenza A virus, RSV, and SARS-CoV-2

      research-article
      1 , 1 ,
      Microbiology Spectrum
      American Society for Microbiology
      Influenza A virus, Influenza B virus, respiratory syncytial virus, SARS-CoV-2, point-of-care testing, multiplex polymerase chain reaction

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          ABSTRACT

          Seasonal increase of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza virus A/B (Flu A/B), and respiratory syncytial virus (RSV) require rapid diagnostic test methods for the management of respiratory tract infections. In this study, we compared the diagnostic accuracy of Savanna RVP4 (RVP4, QuidelOrtho) with Xpert Xpress Plus SARS-CoV-2/Flu/RSV (Xpert, Cepheid). Nasopharyngeal swabs from patients treated at a tertiary care hospital (Germany) were tested for SARS-CoV-2, Flu A/B, and RSV by RVP4 to assess diagnostic accuracy (reference standard: Xpert). The intra and inter assay precision of Ct-values was assessed by repeated test in triplicates (on day 1) and duplicates (days 2–3). All patients with a physician’s order for a multiplex test for SARS-CoV-2, Flu, and RSV test were included. Duplicate swabs from the same patient, samples with a total volume ≤1 mL, or inappropriate shipment/storage were excluded. In total, 229 swabs were included between September 2023 and February 2024. The concordance between both tests was 96.5% (SARS-CoV-2), 98.7% (Flu A), and 99.6% (RSV). Flu B was not detected by both tests. The RVP4 test had a sensitivity of 85%–95% and a specificity of 100% for the detection of SARS-CoV-2, Flu A, and RSV. The intra and inter assay precision of Ct-values from RVP4 was 3% and 2% (SARS-CoV-2), 5% and 4% (Flu A), and 0% and 3% (RSV), respectively. The Savanna RVP4 has a favorable diagnostic accuracy for the detection of SARS-CoV-2, Flu A, and RSV.

          IMPORTANCE

          We assessed the diagnostic accuracy of a new point-of-care test for the rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza virus A/B (Flu A/B), and respiratory syncytial virus (RSV). The new test has a concordance with the reference standard of 96.5% (SARS-CoV-2), 98.7% (Flu A), and 99.1% (RSV). The sensitivity of 85%–95% and specificity of 100% for the detection of SARS-CoV-2, Flu A, and RSV is comparable with similar nucleic acid amplification-based point of care tests but at lower costs.

          Related collections

          Most cited references12

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Rapid, point‐of‐care antigen and molecular‐based tests for diagnosis of SARS‐CoV‐2 infection

          Background Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and the resulting COVID‐19 pandemic present important diagnostic challenges. Several diagnostic strategies are available to identify or rule out current infection, identify people in need of care escalation, or to test for past infection and immune response. Point‐of‐care antigen and molecular tests to detect current SARS‐CoV‐2 infection have the potential to allow earlier detection and isolation of confirmed cases compared to laboratory‐based diagnostic methods, with the aim of reducing household and community transmission. Objectives To assess the diagnostic accuracy of point‐of‐care antigen and molecular‐based tests to determine if a person presenting in the community or in primary or secondary care has current SARS‐CoV‐2 infection. Search methods On 25 May 2020 we undertook electronic searches in the Cochrane COVID‐19 Study Register and the COVID‐19 Living Evidence Database from the University of Bern, which is updated daily with published articles from PubMed and Embase and with preprints from medRxiv and bioRxiv. In addition, we checked repositories of COVID‐19 publications. We did not apply any language restrictions. Selection criteria We included studies of people with suspected current SARS‐CoV‐2 infection, known to have, or not to have SARS‐CoV‐2 infection, or where tests were used to screen for infection. We included test accuracy studies of any design that evaluated antigen or molecular tests suitable for a point‐of‐care setting (minimal equipment, sample preparation, and biosafety requirements, with results available within two hours of sample collection). We included all reference standards to define the presence or absence of SARS‐CoV‐2 (including reverse transcription polymerase chain reaction (RT‐PCR) tests and established clinical diagnostic criteria). Data collection and analysis Two review authors independently screened studies and resolved any disagreements by discussion with a third review author. One review author independently extracted study characteristics, which were checked by a second review author. Two review authors independently extracted 2x2 contingency table data and assessed risk of bias and applicability of the studies using the QUADAS‐2 tool. We present sensitivity and specificity, with 95% confidence intervals (CIs), for each test using paired forest plots. We pooled data using the bivariate hierarchical model separately for antigen and molecular‐based tests, with simplifications when few studies were available. We tabulated available data by test manufacturer. Main results We included 22 publications reporting on a total of 18 study cohorts with 3198 unique samples, of which 1775 had confirmed SARS‐CoV‐2 infection. Ten studies took place in North America, two in South America, four in Europe, one in China and one was conducted internationally. We identified data for eight commercial tests (four antigen and four molecular) and one in‐house antigen test. Five of the studies included were only available as preprints. We did not find any studies at low risk of bias for all quality domains and had concerns about applicability of results across all studies. We judged patient selection to be at high risk of bias in 50% of the studies because of deliberate over‐sampling of samples with confirmed COVID‐19 infection and unclear in seven out of 18 studies because of poor reporting. Sixteen (89%) studies used only a single, negative RT‐PCR to confirm the absence of COVID‐19 infection, risking missing infection. There was a lack of information on blinding of index test (n = 11), and around participant exclusions from analyses (n = 10). We did not observe differences in methodological quality between antigen and molecular test evaluations. Antigen tests Sensitivity varied considerably across studies (from 0% to 94%): the average sensitivity was 56.2% (95% CI 29.5 to 79.8%) and average specificity was 99.5% (95% CI 98.1% to 99.9%; based on 8 evaluations in 5 studies on 943 samples). Data for individual antigen tests were limited with no more than two studies for any test. Rapid molecular assays Sensitivity showed less variation compared to antigen tests (from 68% to 100%), average sensitivity was 95.2% (95% CI 86.7% to 98.3%) and specificity 98.9% (95% CI 97.3% to 99.5%) based on 13 evaluations in 11 studies of on 2255 samples. Predicted values based on a hypothetical cohort of 1000 people with suspected COVID‐19 infection (with a prevalence of 10%) result in 105 positive test results including 10 false positives (positive predictive value 90%), and 895 negative results including 5 false negatives (negative predictive value 99%). Individual tests We calculated pooled results of individual tests for ID NOW (Abbott Laboratories) (5 evaluations) and Xpert Xpress (Cepheid Inc) (6 evaluations). Summary sensitivity for the Xpert Xpress assay (99.4%, 95% CI 98.0% to 99.8%) was 22.6 (95% CI 18.8 to 26.3) percentage points higher than that of ID NOW (76.8%, (95% CI 72.9% to 80.3%), whilst the specificity of Xpert Xpress (96.8%, 95% CI 90.6% to 99.0%) was marginally lower than ID NOW (99.6%, 95% CI 98.4% to 99.9%; a difference of −2.8% (95% CI −6.4 to 0.8)) Authors' conclusions This review identifies early‐stage evaluations of point‐of‐care tests for detecting SARS‐CoV‐2 infection, largely based on remnant laboratory samples. The findings currently have limited applicability, as we are uncertain whether tests will perform in the same way in clinical practice, and according to symptoms of COVID‐19, duration of symptoms, or in asymptomatic people. Rapid tests have the potential to be used to inform triage of RT‐PCR use, allowing earlier detection of those testing positive, but the evidence currently is not strong enough to determine how useful they are in clinical practice. Prospective and comparative evaluations of rapid tests for COVID‐19 infection in clinically relevant settings are urgently needed. Studies should recruit consecutive series of eligible participants, including both those presenting for testing due to symptoms and asymptomatic people who may have come into contact with confirmed cases. Studies should clearly describe symptomatic status and document time from symptom onset or time since exposure. Point‐of‐care tests must be conducted on samples according to manufacturer instructions for use and be conducted at the point of care. Any future research study report should conform to the Standards for Reporting of Diagnostic Accuracy (STARD) guideline. How accurate are rapid tests, performed during a health‐care visit (point‐of‐care), for diagnosing COVID‐19? Why is this question important? People with suspected COVID‐19 need to know quickly whether they are infected, so that they can self‐isolate, receive treatment, and inform close contacts. Currently, COVID‐19 infection is confirmed by sending away samples, taken from the nose and throat, for laboratory testing. The laboratory test, called RT‐PCR, requires specialist equipment, may require repeat healthcare visits, and typically takes at least 24 hours to produce a result. Rapid point‐of‐care tests can provide a result ‘while you wait’, ideally within two hours of providing a sample. This could help people isolate early and reduce the spread of infection. What did we want to find out? We were interested in two types of rapid point‐of‐care tests, antigen and molecular tests. Antigen tests identify proteins on the virus, often using disposable devices. Molecular tests detect the virus’s genetic material, using small portable or table‐top devices. Both test the same nose or throat samples as RT‐PCR tests. We wanted to know whether rapid point‐of‐care antigen and molecular tests are accurate enough to replace RT‐PCR for diagnosing infection, or to select people for further testing if they have a negative result. What did we do? We looked for studies that measured the accuracy of rapid point‐of‐care tests compared with RT‐PCR tests to detect current COVID‐19 infection. Studies could assess any rapid antigen or molecular point‐of‐care test, compared with a reference standard test. The reference standard is the best available method for diagnosing the infection; we considered RT‐PCR test results and clinically defined COVID‐19 as reference tests. People could be tested in hospital or the community. Studies could test people with or without symptoms. Tests had to use minimal equipment, be performed safely without risking infection from the sample, and have results available within two hours of the sample being collected. Tests could be used in small laboratories or wherever the patient is (in primary care, urgent care facilities, or in hospital). How did studies assess diagnostic test accuracy? Studies tested participants with the rapid point‐of‐care tests. Participants were classified as known to have – and not to have ‐ COVID‐19, by RT‐PCR in all studies. Studies then identified false positive and false negative errors in the point‐of‐care test results, compared to RT‐PCR. False positive tests incorrectly identified COVID‐19 when it was not present, potentially leading to unnecessary self‐isolation and further testing. False negatives missed COVID‐19 when it was present, risking delayed self‐isolation and treatment, and spread of infection. What we found We found 18 relevant studies. Ten studies took place in North America, four in Europe, two in South America, one in China and one in multiple countries. Nine studies deliberately included a high percentage of people with confirmed COVID‐19 or included only people with COVID‐19. Fourteen studies did not provide any information about the people providing the samples for testing and 12 did not provide any information about where people were tested. None of the studies reported includedsamples from people without symptoms. Main results Five studies reported eight evaluations of five different antigen tests. Overall, there was considerable variation between the results of the antigen tests in how well they detected COVID‐19 infection. Tests gave false positive results in less than 1% of samples. Thirteen evaluations of four different molecular tests correctly detected an average of 95% of samples with COVID‐19 infection. Around 1% of samples gave false positive results. If 1000 people had molecular tests, and 100 (10%) of them really had COVID‐19: ‐ 105 people would test positive for COVID‐19. Of these, 10 people (10%) would not have COVID‐19 (false positive result). ‐ 895 people would test negative for COVID‐19. Of these, 5 people (1%) would actually have COVID‐19 (false negative result). We noted a large difference in COVID‐19 detection between the two most commonly evaluated molecular tests. How reliable were the results of the studies? Our confidence in the evidence is limited. ‐ Three‐quarters of studies did not follow the test manufacturers’ instructions, so may have found different results if they had. ‐ Often, studies did not use the most reliable methods or did not report enough information for us to judge their methods. This may have affected estimates of test accuracy, but it is impossible to identify by how much. ‐ A quarter of studies were published early online as ‘preprints’ and are included in the review. Preprints do not undergo the normal rigorous checks of published studies, so we are uncertain how reliable they are. What are the implications of this review? Studies provided little information about their participants, so it is not possible to tell if the results can be applied to people with no symptoms, mild symptoms, or who were hospitalised with COVID‐19. Accurate rapid tests would have the potential to select people for RT‐PCR testing or to be used where RT‐PCR is not available. However, the evidence currently is not strong enough and more studies are urgently needed to be able to say if these tests are good enough to be used in practice. How up‐to‐date is this review? This review includes evidence published up to 25 May 2020. Because new research is being published in this field, we will update this review soon.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Use of coefficient of variation in assessing variability of quantitative assays.

            We have derived the mathematical relationship between the coefficient of variation associated with repeated measurements from quantitative assays and the expected fraction of pairs of those measurements that differ by at least some given factor, i.e., the expected frequency of disparate results that are due to assay variability rather than true differences. Knowledge of this frequency helps determine what magnitudes of differences can be expected by chance alone when the particular coefficient of variation is in effect. This frequency is an operational index of variability in the sense that it indicates the probability of observing a particular disparity between two measurements under the assumption that they measure the same quantity. Thus the frequency or probability becomes the basis for assessing if an assay is sufficiently precise. This assessment also provides a standard for determining if two assay results for the same subject, separated by an intervention such as vaccination or infection, differ by more than expected from the variation of the assay, thus indicating an intervention effect. Data from an international collaborative study are used to illustrate the application of this proposed interpretation of the coefficient of variation, and they also provide support for the assumptions used in the mathematical derivation.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Risk factors for respiratory syncytial virus associated with acute lower respiratory infection in children under five years: Systematic review and meta–analysis

              Background Respiratory syncytial virus (RSV) is the most common pathogen identified in young children with acute lower respiratory infection (ALRI) as well as an important cause of hospital admission. The high incidence of RSV infection and its potential severe outcome make it important to identify and prioritise children who are at higher risk of developing RSV–associated ALRI. We aimed to identify risk factors for RSV–associated ALRI in young children. Methods We carried out a systematic literature review across 4 databases and obtained unpublished studies from RSV Global Epidemiology Network (RSV GEN) collaborators. Quality of all eligible studies was assessed according to modified GRADE criteria. We conducted meta–analyses to estimate odds ratios with 95% confidence intervals (CI) for individual risk factors. Results We identified 20 studies (3 were unpublished data) with “good quality” that investigated 18 risk factors for RSV–associated ALRI in children younger than five years old. Among them, 8 risk factors were significantly associated with RSV–associated ALRI. The meta–estimates of their odds ratio (ORs) with corresponding 95% confidence intervals (CI) are prematurity 1.96 (95% CI 1.44–2.67), low birth weight 1.91 (95% CI 1.45–2.53), being male 1.23 (95% CI 1.13–1.33), having siblings 1.60 (95% CI 1.32–1.95), maternal smoking 1.36 (95% CI 1.24–1.50), history of atopy 1.47 (95% CI 1.16–1.87), no breastfeeding 2.24 (95% CI 1.56–3.20) and crowding 1.94 (95% CI 1.29–2.93). Although there were insufficient studies available to generate a meta–estimate for HIV, all articles (irrespective of quality scores) reported significant associations between HIV and RSV–associated ALRI. Conclusions This study presents a comprehensive report of the strength of association between various socio–demographic risk factors and RSV–associated ALRI in young children. Some of these amenable risk factors are similar to those that have been identified for (all cause) ALRI and thus, in addition to the future impact of novel RSV vaccines, national action against ALRI risk factors as part of national control programmes can be expected to reduce burden of disease from RSV. Further research which identifies, accesses and analyses additional unpublished RSV data sets could further improve the precision of these estimates.
                Bookmark

                Author and article information

                Contributors
                Role: Formal analysisRole: MethodologyRole: Writing – original draftRole: Writing – review and editing
                Role: ConceptualizationRole: Writing – original draftRole: Writing – review and editing
                Role: Editor
                Journal
                Microbiol Spectr
                Microbiol Spectr
                spectrum
                Microbiology Spectrum
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2165-0497
                August 2024
                11 July 2024
                11 July 2024
                : 12
                : 8
                : e01153-24
                Affiliations
                [1 ]Institute of Medical Microbiology, University Hospital Münster; , Münster, Germany
                Icahn School of Medicine at Mount Sinai; , New York, New York, USA
                Author notes
                Address correspondence to Frieder Schaumburg, frieder.schaumburg@ 123456ukmuenster.de

                The authors declare no conflict of interest.

                Author information
                https://orcid.org/0000-0002-9168-9290
                Article
                spectrum01153-24 spectrum.01153-24
                10.1128/spectrum.01153-24
                11302293
                38990032
                66b8ffb6-f844-49d0-a5f2-c37e403a171a
                Copyright © 2024 Köse and Schaumburg.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                : 17 May 2024
                : 21 June 2024
                Page count
                supplementary-material: 2, authors: 2, Tables: 2, References: 17, Pages: 7, Words: 3964
                Categories
                Research Article
                open-peer-review, Open Peer Review
                applied-and-industrial-microbiology, Applied and Industrial Microbiology
                Custom metadata
                August 2024

                influenza a virus,influenza b virus,respiratory syncytial virus,sars-cov-2,point-of-care testing,multiplex polymerase chain reaction

                Comments

                Comment on this article