Abstract
Objectives
To evaluate the timing of antenatal corticosteroids (ACS) administration in relation to the delivery timing based on indications and risk factors for preterm delivery.
Methods
We conducted a retrospective cohort study to understand what factors predict the optimal timing of ACS administration (ACS administration within seven days). We reviewed consecutive charts of adult pregnant women receiving ACS from January 1, 2011, to December 31, 2019. We excluded pregnancies under 23 weeks, incomplete and duplicate records, and patients delivered outside our health system. The timing of ACS administration was categorized as optimal or suboptimal. These groups were analyzed regarding demographics, indications for ACS administration, risk factors for preterm delivery, and signs and symptoms of preterm labor.
Results
We identified 25,776 deliveries. ACS were administered to 531 pregnancies, of which 478 met the inclusion criteria. Of the 478 pregnancies included in the study, 266 (55.6 %) were delivered in the optimal timeframe. There was a higher proportion of patients receiving ACS for the indication of threatened preterm labor in the suboptimal group as compared to the optimal group (85.4 % vs. 63.5 %, p<0.001). In addition, patients who delivered in the suboptimal timeframe had a higher proportion of short cervix (33 % vs. 6.4 %, p<0.001) and positive fetal fibronectin (19.8 % vs. 1.1 %, p<0.001) compared to those who delivered in the optimal timeframe.
Conclusions
More emphasis should be placed on the judicious use of ACS. Emphasis should be placed on clinical assessment rather than relying solely on imaging and laboratory tests. Re-appraisal of institutional practices and thoughtful ACS administration based on the risk-benefit ratio is warranted.
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Research funding: None declared.
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Author contributions: LC and SC drafted the manuscript. HT performed the statistical analysis and ensured its correct interpretation. All authors contributed to the final version’s writing and editing. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interest: Authors state no conflict of interest.
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Informed consent: Not applicable.
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Ethical approval: The Institutional Review Board at Staten Island University Hospital approved the study under protocols #18–0157 and #20–0361, respectively.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/jpm-2023-0074).
© 2023 Walter de Gruyter GmbH, Berlin/Boston
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- Frontmatter
- Editorial
- ChatGPT and artificial intelligence in the Journal of Perinatal Medicine
- Reviews
- A systematic review and critical evaluation of quality of clinical practice guidelines on fetal growth restriction
- An exploration of barriers to access to trial of labor and vaginal birth after cesarean in the United States: a scoping review
- Opinion Paper
- A call for public funding of invasive and non-invasive prenatal testing
- Original Articles – Obstetrics
- The AccuFlow sensor: a novel digital health tool to assess intrapartum blood loss at cesarean delivery
- Risk factors associated with third- and fourth-degree perineal lacerations in singleton vaginal deliveries: a comprehensive United States population analysis 2016–2020
- Changes in use of 17-OHPC after the PROLONG trial: a physician survey
- Retrospective comparison of monochorionic diamniotic twin pregnancies stratified by spontaneous or artificial conception
- Associations of cesarean sections with comorbidities within the Pregnancy Risk Assessment Monitoring System
- The spatial expression of mTORC2-AKT-IP3R signal pathway in mitochondrial combination of endoplasmic reticulum of maternal fetal interface trophoblast in intrahepatic cholestasis of pregnancy
- Comprehensive analysis of macrosomia: exploring the association between first-trimester alanine aminotransferase and uric acid measurements in pregnant women
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- Classification of normal and abnormal fetal heart ultrasound images and identification of ventricular septal defects based on deep learning
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