Combined models using maternal biophysical factors, ultrasound and biochemical markers may predict stillbirths accurately: Study

Stillbirth presents a substantial global health issue with an estimated annual occurrence of 2.6 million cases, predominantly affecting low- and middle-income nations. The primary causes of stillbirth differ between high-income and low- to middle-income countries, with factors like fetal growth restriction, placental insufficiency, and congenital anomalies more prevalent in the former, while maternal infections, hypertensive disorders, and obstetric complications are more common in the latter. Recent study aimed to evaluate the accuracy of combined models using maternal biophysical factors, ultrasound, and biochemical markers to predict stillbirths. The researchers conducted a retrospective cohort study of 1,643 pregnant women who underwent first-trimester pre-eclampsia screening at 11-13 weeks of gestation. The study found that there were 13 (0.79%) cases of stillbirth. The combination of maternal factors (including chronic hypertension and previous pregnancy with pre-eclampsia), mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), and placental growth factor (PlGF) significantly contributed to the prediction of stillbirth. This combined model was a good predictor for all (including controls) types of stillbirth, with an area under the receiver-operating-characteristics curve (AUC) of 0.879 and a sensitivity of 99.3% and specificity of 38.5%.

Accuracy in Prediction

The model was especially accurate in predicting placental dysfunction-related stillbirths, with an AUC of 0.984, sensitivity of 98.5%, and specificity of 85.7%. However, the model had lower predictive accuracy for non-placental dysfunction-related stillbirths, with an AUC of 0.780.

Research Conclusion

The researchers concluded that screening at 11-13 weeks' gestation by combining maternal factors, MAP, UtA-PI, and PlGF can predict a high proportion of stillbirths, particularly those related to placental dysfunction. The model has good accuracy and could be used in clinical practice to allow early intervention strategies to prevent stillbirth.

Key Points

1. The study aimed to evaluate the accuracy of combined models using maternal biophysical factors, ultrasound, and biochemical markers to predict stillbirths.

2. The study found that the combination of maternal factors (including chronic hypertension and previous pregnancy with pre-eclampsia), mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), and placental growth factor (PlGF) significantly contributed to the prediction of stillbirth.

3. The combined model was a good predictor for all types of stillbirth, with an area under the receiver-operating-characteristics curve (AUC) of 0.879 and a sensitivity of 99.3% and specificity of 38.5%.

4. The model was especially accurate in predicting placental dysfunction-related stillbirths, with an AUC of 0.984, sensitivity of 98.5%, and specificity of 85.7%.

5. The model had lower predictive accuracy for non-placental dysfunction-related stillbirths, with an AUC of 0.780.

6. The researchers concluded that screening at 11-13 weeks' gestation by combining maternal factors, MAP, UtA-PI, and PlGF can predict a high proportion of stillbirths, particularly those related to placental dysfunction, and the model has good accuracy that could be used in clinical practice to allow early intervention strategies to prevent stillbirth.

Reference –

Adly Nanda Al-Fattah et al. (2024). A Prediction Model For Stillbirth Based On First Trimester Pre-Eclampsia Combined Screening.. *International Journal Of Gynaecology And Obstetrics: The Official Organ Of The International Federation Of Gynaecology And Obstetrics*. https://doi.org/10.1002/ijgo.15755.