Evidence map›Paper›PMID 33508754›Full record

SynthesisClinical imaging2021

COVID-19 in pregnancy: a systematic review of chest CT findings and associated clinical features in 427 patients.

Rachel R Oshay, Michael Y C Chen, Brandon K K Fields, Natalie L Demirjian, Ryan S Lee, Daniel Mosallaei, Ali Gholamrezanezhad

Open access · bronzeAbstract readSystematic Review
In one paragraph

Synthesis in Clinical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
6.3field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Coronavirus Disease-2019 in Pregnancy.Clinics in chest medicine · 2023
    Review
  6. Review
  7. Article
  8. Article
  9. Review
  10. Review
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Rachel R OshayKeck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America; Department of Integrative Anatomical Sciences, University of Southern California, Los Angeles, CA 90033, United States of America.
Michael Y C ChenKeck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America; Department of Integrative Anatomical Sciences, University of Southern California, Los Angeles, CA 90033, United States of America.
Brandon K K FieldsKeck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America. Electronic address: https://twitter.com/bkkfields.
Natalie L DemirjianKeck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America; Department of Integrative Anatomical Sciences, University of Southern California, Los Angeles, CA 90033, United States of America. Electronic address: https://twitter.com/nataliedemir.
Ryan S LeeKeck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America.
Daniel MosallaeiKeck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America.
Ali GholamrezanezhadKeck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America; Department of Radiology, Los Angeles, CA 90033, United States of America. Electronic address: ali.gholamrezanezhad@med.usc.edu.
University of Southern California · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeOur purpose was to conduct a comprehensive systematic review of all existing literature regarding imaging findings on chest CT and associated clinical features in pregnant patients diagnosed with COVID-19. MATERIALS &

methodsA literature search was conducted on April 21, 2020 and updated on July 24, 2020 using PubMed, Embase, World Health Organization, and Google Scholar databases. Only studies which described chest CT findings of COVID-19 in pregnant patients were included for analysis.

resultsA total of 67 articles and 427 pregnant patients diagnosed with COVID-19 were analyzed. The most frequently encountered pulmonary findings on chest CT were ground-glass opacities (77.2%, 250/324), posterior lung involvement (72.5%, 50/69), multilobar involvement (71.8%, 239/333), bilateral lung involvement (69.4%, 231/333), peripheral distribution (68.1%, 98/144), and consolidation (40.9%, 94/230). Pregnant patients were also found to present more frequently with consolidation (40.9% vs. 21.0-31.8%) and pleural effusion (30.0% vs. 5.0%) in comparison to the general population. Associated clinical features included antepartum fever (198 cases), lymphopenia (128 cases), and neutrophilia (97 cases). Of the 251 neonates delivered, 96.8% had negative RT-PCR and/or IgG antibody testing for COVID-19. In the eight cases (3.2%) of reported neonatal infection, tests were either conducted on samples collected up to 72 h after birth or were found negative on all subsequent RT-PCR tests.

conclusionPregnant patients appear to present more commonly with more advanced COVID-19 CT findings compared to the general adult population. Furthermore, characteristic laboratory abnormalities found in pregnant patients tended to mirror those found in the general patient population. Lastly, results from neonatal testing suggest a low risk of vertical transmission.

Indexed as

COVID-19Lung DiseasesAdultCOVID-19 TestingFemaleHumansInfant, NewbornLungPregnancySARS-CoV-2Tomography, X-Ray ComputedComputed tomographyCOVID-19PneumoniaPregnancySARS-CoV-2

Identifiers

PMID33508754
PMCPMC7804384
OpenAlexW3119293457

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.