Validation of the first-trimester machine learning model for predicting pre-eclampsia in an Asian population

Long Nguyen-Hoang, Daljit S. Sahota, Ritsuko K. Pooh, Honglei Duan, Noppadol Chaiyasit, Akihiko Sekizawa, Steven W. Shaw, Suresh Seshadri, Mahesh Choolani, Piengbulan Yapan, Wen Shan Sim, Runmei Ma, Wing Cheong Leung, So Ling Lau, Nikki May Wing Lee, Hiu Yu Hillary Leung, Tal Meshali, Hamutal Meiri, Yoram Louzoun, Liona C. Poon

Research output: Contribution to journalArticlepeer-review


Objectives: To evaluate the performance of an artificial intelligence (AI) and machine learning (ML) model for first-trimester screening for pre-eclampsia in a large Asian population. Methods: This was a secondary analysis of a multicenter prospective cohort study in 10 935 participants with singleton pregnancies attending for routine pregnancy care at 11–13+6 weeks of gestation in seven regions in Asia between December 2016 and June 2018. We applied the AI+ML model for the first-trimester prediction of preterm pre-eclampsia (<37 weeks), term pre-eclampsia (≥37 weeks), and any pre-eclampsia, which was derived and tested in a cohort of pregnant participants in the UK (Model 1). This model comprises maternal factors with measurements of mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor (PlGF). The model was further retrained with adjustments for analyzers used for biochemical testing (Model 2). Discrimination was assessed by area under the receiver operating characteristic curve (AUC). The Delong test was used to compare the AUC of Model 1, Model 2, and the Fetal Medicine Foundation (FMF) competing risk model. Results: The predictive performance of Model 1 was significantly lower than that of the FMF competing risk model in the prediction of preterm pre-eclampsia (0.82, 95% confidence interval [CI] 0.77–0.87 vs. 0.86, 95% CI 0.811–0.91, P = 0.019), term pre-eclampsia (0.75, 95% CI 0.71–0.80 vs. 0.79, 95% CI 0.75–0.83, P = 0.006), and any pre-eclampsia (0.78, 95% CI 0.74–0.81 vs. 0.82, 95% CI 0.79–0.84, P < 0.001). Following the retraining of the data with adjustments for the PlGF analyzers, the performance of Model 2 for predicting preterm pre-eclampsia, term pre-eclampsia, and any pre-eclampsia was improved with the AUC values increased to 0.84 (95% CI 0.80–0.89), 0.77 (95% CI 0.73–0.81), and 0.80 (95% CI 0.76–0.83), respectively. There were no differences in AUCs between Model 2 and the FMF competing risk model in the prediction of preterm pre-eclampsia (P = 0.135) and term pre-eclampsia (P = 0.084). However, Model 2 was inferior to the FMF competing risk model in predicting any pre-eclampsia (P = 0.024). Conclusion: This study has demonstrated that following adjustment for the biochemical marker analyzers, the predictive performance of the AI+ML prediction model for pre-eclampsia in the first trimester was comparable to that of the FMF competing risk model in an Asian population.

Original languageEnglish
JournalInternational Journal of Gynecology and Obstetrics
Early online date26 Apr 2024
StateE-pub ahead of print - 26 Apr 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors. International Journal of Gynecology & Obstetrics published by John Wiley & Sons Ltd on behalf of International Federation of Gynecology and Obstetrics.


  • artificial intelligence
  • competing risk model
  • first trimester
  • machine learning
  • mean arterial pressure
  • placental growth factor
  • pre-eclampsia
  • uterine artery pulsatility index


Dive into the research topics of 'Validation of the first-trimester machine learning model for predicting pre-eclampsia in an Asian population'. Together they form a unique fingerprint.

Cite this