Temporal course of SARS-CoV-2 antibody positivity in patients with COVID-19 following the first clinical presentation

  1. Martin Risch
  2. Myriam Weber
  3. Sarah Thiel
  4. Kirsten Grossmann
  5. Nadia Wohlwend
  6. Thomas Lung
  7. Dorothea Hillmann
  8. Michael Ritzler
  9. Francesca Ferrara
  10. Susanna Bigler
  11. Konrad Egli
  12. Thomas Bodmer
  13. Mauro Imperiali
  14. Yacir Salimi
  15. Felix Fleisch
  16. Alexia Cusini
  17. Harald Renz
  18. Philipp Kohler
  19. Pietro Vernazza
  20. Christian Kahlert
  21. Matthias Paprotny
  22. Lorenz Risch

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Abstract

Knowledge of the sensitivities of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) antibody tests beyond 35 days after the clinical onset of COVID-19 is insufficient. We aimed to describe positivity rate of SARS-CoV-2 assays employing three different measurement principles over a prolonged period. Two hundred sixty-eight samples from 180 symptomatic patients with COVID-19 and a reverse transcription polymerase chain reaction (RT-PCR) test followed by serological investigation of SARS-CoV-2 antibodies were included.. We conducted three chemiluminescence (including electrochemiluminscence, ECLIA), four enzyme linked immunosorbent assay (ELISA), and one lateral flow immunoassay (LFIA) test formats. Positivity rates, as well as positive (PPV) and negative predictive values (NPV) were calculated for each week after the first clinical presentation for COVID-19. Furthermore, combinations of tests were assessed within an orthogonal testing approach employing two independent assays and predictive values were calculated. Heat maps were constructed to graphically illustrate operational test characteristics. During a follow-up period of more than 9 weeks, chemiluminescence assays and one ELISA IgG test showed stable positivity rates after the third week. With the exception of ECLIA, the PPVs of the other chemiluminescence assays were ≥95% for COVID-19 only after the second week. ELISA and LFIA had somewhat lower PPVs. IgM exhibited insufficient predictive characteristics. An orthogonal testing approach provided PPVs ≥95% for patients with a moderate pretest probability (e.g., symptomatic patients), even for tests with a low single test performance. After the second week, NPVs of all but IgM assays were ≥95% for patients with low to moderate pretest probability. The confirmation of negative results using an orthogonal algorithm with another assay provided lower NPVs than the single assays. When interpreting results from SARS-CoV-2 tests, the pretest probability, time of blood draw and assay characteristics must be carefully considered. An orthogonal testing approach increases the accuracy of positive, but not negative, predictions.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.17.20214445: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study protocol was verified by the cantonal ethics boards of Zurich (BASEC Req-20-00587) and Eastern Switzerland (EKOS; BASEC Nr.
    Consent: Informed consent for performing a laboratory analysis of anonymized samples was waived.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical computations were performed with Medcalc version 18.11.3 (Mariakerke, Belgium).
    Medcalc
    suggested: (MedCalc, RRID:SCR_015044)
    Graphs were drawn with Microsoft Excel 2016 MSO (16.0.8431.2046) (Microsoft Inc., Seattle, USA) using the linear interpolation function.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    In our opinion, the use of heat maps to illustrate diagnostic strengths and weaknesses in certain situations according to the time since the first clinical presentation and pretest probability is useful, particularly when only one test is employed. To the best of our knowledge, heat maps have not yet been introduced as a tool to interpret serological assays of infectious diseases 4. Unfortunately, at the moment, there is no clinical score currently unavailable to assess pretest probability, which relies on a history of clinical symptoms alone and could also be utilized retrospectively (e.g., because no laboratory results are available at the time when a patient was symptomatic) 20. However, other rough estimates also allow for assessment of pretest probability and clinical use of the heatmaps according to the individual situation of a patient: symptomatic patients have a pretest probability of 10% and higher, asymptomatic patients usually have a pretest probability of less than 10%, close contacts of patients with confirmed COVID-19 cases have a pretest probability of 15-30% 10. Seroprevalence data of a region can also help to assess the pretest probability even in the absence of clinical symptoms. Positive predictive values differed among the different test formats. Based on the results of our study, chemiluminescence formats have somewhat better operative characteristics than at least some of the investigated ELISA and LFIA test formats. Furthermore, the SARS-CoV-2 IgM test...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.10.17.20214445v1 on medRxiv
  3. Version published to 10.1155/2020/9878453