The Prediction of Pre‐Eclampsia Using Low Fetal Fraction in a Machine Learning Model
Low FF in NIPT may indicate an increased risk of pre-eclampsia. Regardless of the success of redraw, pregnancies with initial NIPT failure due to low FF warrant vigilance for pre-eclampsia development. The XGBoost machine learning model demonstrates good efficacy for predicting pre-eclampsia and has potential as an adjunctive prenatal screening tool for early diagnosis.
Authors
- Wangxiao Zhou (ORCID: https://orcid.org/0000-0003-3024-1414)
- Jinyuan Wang (ORCID: https://orcid.org/0009-0005-3808-9762)
- Minghua Jiang (ORCID: https://orcid.org/0000-0001-6421-8613)
- Xiaoou Wang (ORCID: https://orcid.org/0000-0002-7040-5383)
- Shanshan Li
- Yang Bai (ORCID: https://orcid.org/0000-0003-2652-7022)
- Ni Li (ORCID: https://orcid.org/0000-0001-6588-4407)
- Yao Lin
- S. Huang
- Songdao Ye
Institutions
- Wenzhou Medical University (CN)
- Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University (CN)
Publication Details
- Journal
- Prenatal Diagnosis
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1002/pd.70033
- Primary Topic
- Pregnancy and preeclampsia studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China