How can we better predict the risk of spontaneous miscarriage among women experiencing threatened miscarriage?
This study seeks to establish progesterone and progesterone-induced blocking factor (PIBF) levels as predictors of subsequent completed miscarriage among women presenting with threatened miscarriage between 6 and 10 weeks of gestation. Our secondary objective was to assess the known maternal risk factors, toward development of a parsimonious and clinician-friendly risk assessment model for predicting completed miscarriage. In this article, we present a prospective cohort study of 119 patients presenting with threatened miscarriage from gestation weeks 6 to 10 at a tertiary women's hospital emergency unit in Singapore. Thirty (25.2%) women had a spontaneous miscarriage. Low progesterone and PIBF levels are similarly predictive of subsequent completed miscarriage. Study results (OR, 95% CI) showed that higher levels of progesterone (0.91, 95% CI 0.88-0.94) and PIBF (0.99, 95% CI 0.98-0.99) were associated with lower risk of miscarriage. Low progesterone level was a very strong predictor of miscarriage risk in our study despite previous concerns about its pulsatile secretion. Low serum progesterone and PIBF levels predicted spontaneous miscarriage among women presenting with threatened miscarriage between gestation weeks 6 to 10. Predictive models to calculate probability of spontaneous miscarriage based on serum progesterone, together with maternal BMI and fetal heart are proposed.
Duke Scholars
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- Young Adult
- Suppressor Factors, Immunologic
- Risk Assessment
- Risk
- Prospective Studies
- Progesterone
- Pregnancy Trimester, First
- Pregnancy Proteins
- Pregnancy
- Obstetrics & Reproductive Medicine
Citation
Published In
DOI
EISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Young Adult
- Suppressor Factors, Immunologic
- Risk Assessment
- Risk
- Prospective Studies
- Progesterone
- Pregnancy Trimester, First
- Pregnancy Proteins
- Pregnancy
- Obstetrics & Reproductive Medicine