Related Experiment Video
Updated: Apr 1, 2026
07:35
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
8.1K
Optimal first trimester preeclampsia prediction: a comparison of multimarker algorithm, risk profiles and their
R Gabbay-Benziv1, N Oliveira2, A A Baschat3
1Helen Schneider Hospital for Women, Rabin Medical Center, PetachTikva; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Prenatal Diagnosis
|October 9, 2015
Summary
Sequential application of a multimarker algorithm and risk factor assessment is optimal for first-trimester preeclampsia prediction. This approach identifies women who may benefit from targeted metabolic or cardiovascular interventions.
Area of Science:
- Obstetrics and Gynecology
- Maternal-Fetal Medicine
- Biomarker Discovery
Background:
- Preeclampsia prediction in early pregnancy remains a clinical challenge.
- Multimarker algorithms and risk profiles offer potential for improved prediction.
- Identifying treatable risk factors is crucial for timely intervention.
Purpose of the Study:
- To compare the predictive performance of a multimarker algorithm, risk profiles, and their sequential application for preeclampsia.
- To determine potential intervention targets for women at high risk of preeclampsia.
Main Methods:
- Prospective collection of maternal characteristics, ultrasound variables, and serum biomarkers in the first trimester.
- Univariate and logistic regression analyses to develop prediction rules.
- Comparison of risk profiles, multimarker algorithm, and sequential application methods.
Main Results:
- Out of 2433 women, 108 (4.4%) developed preeclampsia.
- A probability score including nulliparity, prior preeclampsia, BMI, DBP, and PlGF achieved an AUC of 0.784.
- Sequential application reduced false positives by 26% and identified treatable risks in 91% of preeclampsia cases.
Conclusions:
- Sequential application of a multimarker algorithm followed by risk factor assessment is optimal for first-trimester preeclampsia prediction.
- This strategy effectively identifies women who may benefit from targeted metabolic or cardiovascular treatment.
- Early identification of treatable risk factors can guide personalized interventions.
