Prediction of COVID-19 Severity at Delivery after Asymptomatic or Mild COVID-19 during Pregnancy.


Journal

American journal of perinatology
ISSN: 1098-8785
Titre abrégé: Am J Perinatol
Pays: United States
ID NLM: 8405212

Informations de publication

Date de publication:
10 May 2024
Historique:
medline: 11 5 2024
pubmed: 11 5 2024
entrez: 10 5 2024
Statut: aheadofprint

Résumé

 This study aimed to develop a prediction model that estimates the probability that a pregnant person who has had asymptomatic or mild coronavirus disease 2019 (COVID-19) prior to delivery admission will progress in severity to moderate, severe, or critical COVID-19.  This was a secondary analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-positive patients who delivered from March through December 2020 at hospitals across the United States. Those eligible for this analysis presented for delivery with a current or previous asymptomatic or mild SARS-CoV-2 infection. The primary outcome was moderate, severe, or critical COVID-19 during the delivery admission through 42 days postpartum. The prediction model was developed and internally validated using stratified cross-validation with stepwise backward elimination, incorporating only variables that were known on the day of hospital admission.  Of the 2,818 patients included, 26 (0.9%; 95% confidence interval [CI], 0.6-1.3%) developed moderate-severe-critical COVID-19 during the study period. Variables in the prediction model were gestational age at delivery admission (adjusted odds ratio [aOR], 1.15; 95% CI, 1.08-1.22 per 1-week decrease), a hypertensive disorder in a prior pregnancy (aOR 3.05; 95% CI, 1.25-7.46), and systolic blood pressure at admission (aOR, 1.04; 95% CI, 1.02-1.05 per mm Hg increase). This model yielded an area under the receiver operating characteristic curve of 0.82 (95% CI, 0.72-0.91).  Among individuals presenting for delivery who had asymptomatic-mild COVID-19, gestational age at delivery admission, a hypertensive disorder in a prior pregnancy, and systolic blood pressure at admission were predictive of delivering with moderate, severe, or critical COVID-19. This prediction model may be a useful tool to optimize resources for SARS-CoV-2-infected pregnant individuals admitted for delivery. · Three factors were associated with delivery with more severe COVID-19.. · The developed model yielded an area under the receiver operating characteristic curve of 0.82 and model fit was good.. · The model may be useful tool for SARS-CoV-2 infected pregnancies admitted for delivery..

Identifiants

pubmed: 38729164
doi: 10.1055/s-0044-1786868
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Thieme. All rights reserved.

Déclaration de conflit d'intérêts

None declared.

Auteurs

Grecio J Sandoval (GJ)

Biostatistics Center, George Washington University, Washington, District of Columbia.

Torri D Metz (TD)

Department of Obstetrics and Gynecology, University of Utah Health Sciences Center, Salt Lake City, Utah.

William A Grobman (WA)

Department of Obstetrics and Gynecology, Northwestern University, Chicago, Illinois.

Tracy A Manuck (TA)

Department of Obstetrics and Gynecology, University of North Carolina, Chapel Hill, North Carolina.

Brenna L Hughes (BL)

Department of Obstetrics and Gynecology, University of North Carolina, Chapel Hill, North Carolina.

George R Saade (GR)

Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, Texas.

Monica Longo (M)

Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland.

Hyagriv N Simhan (HN)

Department of Obstetrics and Gynecology, University of Pittsburgh, Pittsburgh, Pennsylvania.

Dwight J Rouse (DJ)

Department of Obstetrics and Gynecology, Brown University, Providence, Rhode Island.

Hector Mendez-Figueroa (H)

Department of Obstetrics and Gynecology, Children's Memorial Hermann Hospital, University of Texas Health Science Center at Houston, Houston, Texas.

Cynthia Gyamfi-Bannerman (C)

Department of Obstetrics and Gynecology, Columbia University, New York, New York.

Angela C Ranzini (AC)

Department of Obstetrics and Gynecology, MetroHealth Medical Center, Case Western Reserve University, Cleveland, Ohio.

Maged M Costantine (MM)

Department of Obstetrics and Gynecology, The Ohio State University, Columbus, Ohio.

Harish M Sehdev (HM)

Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, Pennsylvania.

Alan T N Tita (ATN)

Department of Obstetrics and Gynecology, University of Alabama at Birmingham, Birmingham, Alabama.

Classifications MeSH