Maternal and Neonatal Outcomes of Pregnant Women With Coronavirus Disease 2019 (COVID-19) Pneumonia: A Case-Control Study

  1. Na Li
  2. Lefei Han
  3. Min Peng
  4. Yuxia Lv
  5. Yin Ouyang
  6. Kui Liu
  7. Linli Yue
  8. Qiannan Li
  9. Guoqiang Sun
  10. Lin Chen
  11. Lin Yang

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Abstract

Background

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has caused serious concerns about its potential adverse effects on pregnancy. There are limited data on maternal and neonatal outcomes of pregnant women with COVID-19 pneumonia.

Methods

We conducted a case-control study to compare clinical characteristics and maternal and neonatal outcomes of pregnant women with and without COVID-19 pneumonia.

Results

During the period 24 January–29 February 2020, there were 16 pregnant women with confirmed COVID-19 pneumonia and 18 suspected cases who were admitted to labor in the third trimester. Two had vaginal delivery and the rest were cesarean delivery. Few patients presented respiratory symptoms (fever and cough) on admission, but most had typical chest computed tomographic images of COVID-19 pneumonia. Compared to the controls, patients with COVID-19 pneumonia had lower counts of white blood cells (WBCs), neutrophils, C-reactive protein (CRP), and alanine aminotransferase on admission. Increased levels of WBCs, neutrophils, eosinophils, and CRP were found in postpartum blood tests of pneumonia patients. Three (18.8%) of the mothers with confirmed COVID-19 pneumonia and 3 (16.7%) with suspected COVID-19 pneumonia had preterm delivery due to maternal complications, which were significantly higher than in the control group. None experienced respiratory failure during their hospital stay. COVID-19 infection was not found in the newborns, and none developed severe neonatal complications.

Conclusions

Severe maternal and neonatal complications were not observed in pregnant women with COVID-19 pneumonia who had vaginal or cesarean delivery. Mild respiratory symptoms of pregnant women with COVID-19 pneumonia highlight the need of effective screening on admission.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.10.20033605: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationThe control group of pregnant women without pneumonia during hospital stay were randomly selected from the medical records by an investigator (MP), who was not involved in statistical analysis.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableStudy design and patients: We retrospectively reviewed medical records of pregnant women who were admitted into the Hubei Provincial Maternal and Child Health Center, a tertiary hospital in Wuhan with 1,900 hospital beds, during January 24 – February 29, 2020.

    Table 2: Resources

    No key resources detected.

    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 are a few caveats in our study. First, this is a retrospective case control study from one single center, which could be subject to recall bias and selection bias. Second, we collected the data of sixteen pregnant women with laboratory confirmed COVID-19 pneumonia and eighteen suspected cases with typical CT imaging. Although this is the largest number of pregnant women with COVID-19 pneumonia in literature so far, the sample size is still relatively small. Nevertheless, given the potential of this virus to cause a global pandemic, we believe our study could be one of important clinical studies to guide the clinical diagnosis and treatment to this vulnerable group.

    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.

  2. Version published to 10.1093/cid/ciaa352
  3. Version published to 10.1101/2020.03.10.20033605v1 on medRxiv