VP45.28: Effects of coronavirus disease 2019 (COVID‐19) on maternal, perinatal and neonatal outcomes: a systematic review

  1. J. Juan
  2. M.M. Gil
  3. Z. Rong
  4. Y. Zhang
  5. H. Yang
  6. L.C. Poon

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Abstract

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  1. Version published to 10.1002/uog.23059
  2. ScreenIT

    SciScore for 10.1101/2020.05.02.20088484: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Search strategy: We conducted a comprehensive literature search using PubMed, EMBASE, Cochrane library, China National Knowledge Infrastructure Database and Wan Fang Data until April 20, 2020 (Studies were identified through Pubmed alert after April 20, 2020).
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    Cochrane library
    suggested: (Cochrane Library, RRID:SCR_013000)
    For the search strategy, combinations of the following keywords and MeSH terms were used: SARS-CoV-2, COVID-19, coronavirus disease 2019, pregnancy, gestation, maternal, mothers, vertical transmission, maternal-fetal transmission, intrauterine transmission, neonates, infant.
    MeSH
    suggested: (MeSH, RRID:SCR_004750)
    A modification of the Cochrane Public Health Group Data Extraction and Assessment Template,15 which was previously piloted by the researchers, was used to tabulate findings of the included articles.
    Cochrane Public
    suggested: None

    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:
    There were several major limitations. We were unable to include the second biggest cohort series with available pregnancy outcome data from China because the data were pooled from a national registry.35 Despite contacting the corresponding author directly, it was not possible to identify the actual sources of cases. All 68 pregnant patients with pregnancy outcome data were cases from Wuhan, and the authors did not make reference to previously published cases. In our publication we included 77 cases from Wuhan and we had strong reasons to believe that there was significant overlap between the two series, and therefore we regrettably had to exclude the data from Chen et al in order to avoid inflating the number of COVID-19-infected pregnant cases in China. We also learned that COVID-19-infected pregnant cases could have been transferred to other hospitals, which made it difficult to determine duplicate reporting as cases could have been reported by both the admitting and receiving hospitals. We decided to combine laboratory-confirmed and clinically diagnosed COVID-19 cases in our analysis because both groups had similar outcomes.11 We believed it was important to include the clinically diagnosed cases in this review as clinically diagnosed cases had typical COVID-19 chest CT findings and significant epidemiological exposure. Only two cases of maternal death had been reported, as of April 23, 2020, in the literature. Traditionally any maternal death requires an extensive process...

    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 rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    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.

  3. Version published to 10.1101/2020.05.02.20088484v1 on medRxiv