A prediction model for stillbirth based on first trimester pre-eclampsia combined screening

Area of Science:

• Maternal-fetal medicine
• Obstetrics
• Perinatal diagnostics

Background:

• Stillbirth remains a significant concern in perinatal medicine.
• Accurate prediction of stillbirth is crucial for timely intervention and improved outcomes.
• Current predictive models may lack comprehensive accuracy.

Purpose of the Study:

• To evaluate the predictive accuracy of combined models for stillbirth.
• To assess the utility of maternal biophysical factors, ultrasound, and biochemical markers in predicting stillbirth.
• To differentiate between all stillbirths and placental dysfunction-related stillbirths.

Main Methods:

• Retrospective cohort study of 1643 women undergoing first-trimester screening (11-13 weeks gestation).
• Collected data included maternal characteristics, mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI) ultrasound, and placental growth factor (PlGF) serum.
• Developed combined prediction models and evaluated performance using area under the receiver-operating-characteristics curve (AUC), sensitivity, and specificity.

Main Results:

• 13 stillbirths (0.79%) were identified.
• The combined model (maternal factors, MAP, UtA-PI, PlGF) significantly predicted stillbirth (AUC 0.879 for all stillbirths; AUC 0.984 for placental dysfunction-related stillbirths).
• High sensitivity was achieved for all stillbirths (99.3%) and placental dysfunction-related stillbirths (98.5%).

Conclusions:

• Combining maternal factors, MAP, UtA-PI, and PlGF in the first trimester effectively predicts a high proportion of stillbirths.
• The model demonstrates good accuracy for overall stillbirth prediction and excellent accuracy for placental dysfunction-related stillbirths.
• This integrated approach offers a promising tool for early stillbirth risk assessment.