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Published on: June 29, 2013
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Intrauterine Growth Restriction-Prediction and Peripartum Data on Hospital Care
Marina Dinu1, Andreea Florentina Stancioi-Cismaru2, Mihaela Gheonea1,2
18th Department, Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania.
Medicina (Kaunas, Lithuania)
|April 28, 2023
Summary
This study found that early-onset intrauterine growth restriction (IUGR) and late-onset IUGR have distinct prenatal ultrasound features and postnatal outcomes. Lower estimated fetal weight percentiles improve IUGR detection rates, guiding closer prenatal monitoring.
Area of Science:
- Obstetrics and Gynecology
- Fetal Medicine
- Neonatology
Background:
- Intrauterine growth restriction (IUGR) poses significant risks to fetal well-being and neonatal outcomes.
- Accurate prenatal diagnosis and management are crucial for mitigating complications associated with IUGR.
Purpose of the Study:
- To prospectively evaluate pregnancies with IUGR, focusing on prenatal ultrasound (US) performance in estimating fetal weight and detecting IUGR.
- To describe prenatal care patterns for IUGR cases.
- To identify predictors for postnatal hospitalization duration.
Main Methods:
- Prospective data collection on 111 pregnancies with IUGR from September 2019 to September 2022.
- Comparison of estimated fetal weight (EFW) percentiles (Hadlock 4) with actual birth weight percentiles.
- Retrospective regression analysis to predict hospitalization days.
Main Results:
- Significant differences in US features were observed between early-onset (Eo) and late-onset (Lo) IUGR.
- Higher detection rates for IUGR were associated with lower EFW percentiles.
- Eo-IUGR cases required more US scans.
- A predictive formula for total postnatal hospitalization days was developed.
Conclusions:
- Eo-IUGR and Lo-IUGR exhibit distinct prenatal US characteristics and postnatal outcomes.
- Lower EFW percentiles correlate with increased likelihood of prenatal diagnosis and closer follow-up.
- Intrapartum and immediate postnatal data can predict hospitalization duration, aiding resource management.
