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Methods Inf Med 2022; 61(03/04): 061-067
DOI: 10.1055/s-0042-1756282
Original Article

Use of Machine Learning to Identify Clinical Variables in Pregnant and Non-Pregnant Women with SARS-CoV-2 Infection

Itamar D. Futterman
1   Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Maimonides Medical Center, Brooklyn, New York
,
Rodney McLaren Jr.
1   Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Maimonides Medical Center, Brooklyn, New York
2   Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Thomas Jefferson University Hospital-Jefferson Health, Philadelphia, Pennsylvania, United States
,
Hila Friedmann
3   Gynisus, Inc., Santa Monica, California, United States
,
Nael Musleh
3   Gynisus, Inc., Santa Monica, California, United States
,
Shoshana Haberman
1   Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Maimonides Medical Center, Brooklyn, New York
› Author Affiliations Funding None.
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Abstract

Objective The aim of the study is to identify the important clinical variables found in both pregnant and non-pregnant women who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, using an artificial intelligence (AI) platform.

Methods This was a retrospective cohort study of all women between the ages of 18 to 45, who were admitted to Maimonides Medical Center between March 10, 2020 and December 20, 2021. Patients were included if they had nasopharyngeal PCR swab positive for SARS-CoV-2. Safe People Artificial Intelligence (SPAI) platform, developed by Gynisus, Inc., was used to identify key clinical variables predicting a positive test in pregnant and non-pregnant women. A list of mathematically important clinical variables was generated for both non-pregnant and pregnant women.

Results Positive results were obtained in 1,935 non-pregnant women and 1,909 non-pregnant women tested negative for SARS-CoV-2 infection. Among pregnant women, 280 tested positive, and 1,000 tested negative. The most important clinical variable to predict a positive swab result in non-pregnant women was age, while elevated D-dimer levels and presence of an abnormal fetal heart rate pattern were the most important clinical variable in pregnant women to predict a positive test.

Conclusion In an attempt to better understand the natural history of the SARS-CoV-2 infection we present a side-by-side analysis of clinical variables found in pregnant and non-pregnant women who tested positive for COVID-19. These clinical variables can help stratify and highlight those at risk for SARS-CoV-2 infection and shed light on the individual patient risk for testing positive.

Keywords

COVID-19 - machine learning - SARS-CoV-2 - red blood cell distribution width


Publication History

Received: 04 May 2022

Accepted: 06 July 2022

Article published online:
12 September 2022

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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