A Novel Approach to Predicting Early Pregnancy Outcomes Dynamically in a Prospective Cohort Using Repeated Ultrasound and Serum Biomarkers

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This study aimed to develop a dynamic model for predicting outcome during the first trimester of pregnancy using baseline demographic data and serially collected blood samples and transvaginal sonographies. A prospective cohort of 203 unselected women with an assumed healthy pregnancy of < 8 weeks’ gestation was followed fortnightly from 4–14 weeks’ gestation until either miscarriage or confirmed first trimester viability. The main outcome was development of a model to predict outcome from gestational age-dependent hazard ratios using both baseline and updated serial data from each visit. Secondary outcomes were descriptions of risk factors for miscarriage. The results showed that 18% of the women experienced miscarriages. A fetal heart rate detected before 8 weeks’ gestation indicated a 90% (95% CI 85–95%) chance of subsequent delivery. Maternal age (≥ 35 years), insufficient crown-rump-length (CRL) and mean gestational sac diameter (MSD) development, and presence of bleeding increased the risk of miscarriage. Serum biomarkers, including hCG, progesterone, and estradiol, were found to impact the risk of miscarriage with estradiol as the most important. The best model to predict miscarriage was a combination of maternal age, vaginal bleeding, CRL, and hCG. The second-best model was the sonography-absent model of maternal age, bleeding, hCG, and estradiol. This study suggests that combining maternal age, and evolving data from hCG, estradiol, CRL, and bleeding could be used to predict fetal outcome during the first trimester of pregnancy. Trial registration ClinicalTrials.gov identifier: NCT02761772.

Original languageEnglish
JournalReproductive Sciences
Pages (from-to)3597–3609
Number of pages13
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

    Research areas

  • Early pregnancy, Estradiol, Miscarriage, Prediction model, Vaginal microbiota, Viability

ID: 370269687