Background: There is a trend in healthcare for developing models for predictions of disease to enable early intervention and improve outcome. Instrument: We present the use of artificial intelligence algorithms that were developed by Gynisus Ltd. using mathematical algorithms. Experience: Data were retrospectively collected on pregnant women that delivered at a single institution. Hundreds of parameters were collected and found to have different importance and correlation with the likelihood to develop gestational diabetes mellitus (GDM). We highlight 3 of 29 specific parameters that were important in pregestation and in early pregnancy, which have not been previously correlated with GDM. Conclusion: This predictive tool identified parameters that are not currently being used as predictors in GDM, even before pregnancy. This tool opens the possibility of intervening on patients identified at risk for GDM and its complications. Future prospective studies are needed.
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.
Materials and 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.
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