Objective: 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.
Study Design: 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.
Results: 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).
Conclusion: 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.
Key Points: · 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..
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http://dx.doi.org/10.1055/s-0044-1786868 | DOI Listing |
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Department of Orthopedics, Tianjin Medical University General Hospital, International Science and Technology Cooperation Base of Spinal Cord Injury, Tianjin Key Laboratory of Spine and Spinal Cord, Tianjin, China.
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January 2025
Department of Chemistry, Idaho State University, Pocatello, Idaho, USA.
Impeding linear calibration models from accurately predicting target sample analyte amounts are the target sample-wise deviations in measurement profiles (e.g., spectra) relative to calibration samples.
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January 2025
A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
Zero echo time (zero-TE) pulse sequences provide a quiet and artifact-free alternative to conventional functional magnetic resonance imaging (fMRI) pulse sequences. The fast readouts (<1 ms) utilized in zero-TE fMRI produce an image contrast with negligible contributions from blood oxygenation level-dependent (BOLD) mechanisms, yet the zero-TE contrast is highly sensitive to brain function. However, the precise relationship between the zero-TE contrast and neuronal activity has not been determined.
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Department of Medicine, Division of Cardiology (M.P., N.J.P., N.P.S.), Duke University, Durham, NC.
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Stat Methods Med Res
January 2025
CITMAga and Department of Statistics and Operations Research, Universidade de Vigo, Vigo, Galicia, Spain.
The study of the predictive ability of a marker is mainly based on the accuracy measures provided by the so-called confusion matrix. Besides, the area under the receiver operating characteristic curve has become a popular index for summarizing the overall accuracy of a marker. However, the nature of the relationship between the marker and the outcome, and the role that potential confounders play in this relationship could be fundamental in order to extrapolate the observed results.
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