Additional prognostic value of second-trimester biomarker measurement for predicting preeclampsia before 37 weeks in a Russian cohort
Ivshin A.A., Boldina Yu.S., Malyshev N.A.
Objective: To assess the additional prognostic value of second-trimester biomarker measurements (MAP, UtAPI, PlGF) for predicting preeclampsia before 37 weeks (pPE) and to compare the discrimination, calibration, and clinical utility of a machine learning (ML) logistic regression model and the Fetal Medicine Foundation (FMF) Bayesian algorithm.
Materials and methods: A multicenter retrospective cohort study was conducted using the MARS register of pregnant women (development cohort: 7,101 singleton pregnancies, 133 pPE cases; external validation: 1,325 pregnancies, 24 pPE cases). First-trimester (M2:15 maternal factors + 3 biomarkers) and combined first- and second-trimester (M4: M2 + 3 second-trimester biomarkers) ML models were developed and compared with FMF algorithms (T1, T2). The AUC, calibration (O:E ratio), NRI, IDI, decision curve analysis, and reclassification safety were evaluated.
Results: Second-trimester biomarkers significantly improved discrimination: ΔAUC +0.025 (p=0.001) for ML and +0.031 (p<0.001) for FMF. ML M4 and FMF T2 showed equivalent discrimination (AUC 0.927 vs. 0.935; p=0.91), but ML M4 demonstrated superior calibration (O:E 1.00 vs. 0.81). The detection rates at the 1:100 threshold were 89.5% (ML M4) and 88.0% (FMF T2). Over 99% of the risk downgrades were correct. External validation: AUC of 0.897 (ML M4) and 0.905 (FMF T2).
Conclusion: Additional second-trimester biomarker measurements significantly improved pPE prediction. ML and FMF achieved equivalent discrimination, but ML provided superior calibration in a Russian cohort, supporting the prospective evaluation of a two-stage prenatal pPE screening protocol.
Authors' contributions: Ivshin A.A. – conception and supervision of the study, expert analysis of results, editing of the manuscript; Boldina Yu.S. – drafting of the manuscript; Malyshev N.A. – data analysis and mathematical modeling.
Conflicts of interest: The authors have no conflicts of interest to declare.
Funding: The study was financially supported by the Russian Science Foundation No 24-25-00429,
https://rscf.ru/project/24-25-00429/
Ethical Approval: The study was reviewed and approved by the Research Ethics Committee of the Petrozavodsk State University (Ref. No: 18 of 20.03.2024).
Acknowledgements: The authors express their gratitude to Professor Zulfiya S. Khodjaeva, MD, PhD, Deputy Director for Research at the Institute of Obstetrics, V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Moscow, Russia, for her foundational research on the heterogeneity of preeclampsia, which shaped the theoretical framework of the present study, and for her pioneering work on the validation of preeclampsia prediction algorithms in the Russian population, which served as the starting point and methodological reference for this research.
AI disclosure: The Claude 3.5 Sonnet model (Anthropic) was used to reduce the length of this article through editorial and stylistic editing. The resulting content was then reviewed, edited and approved by the authors. The authors bear full responsibility for the accuracy of the publication's content.
Authors' Data Sharing Statement: De-identified data used in this study are available upon reasonable request, in accordance with the requirements of Federal Law No. 152-FZ 'On Personal Data'. Requests should be directed to the corresponding author (scipeople@mail.ru).
For citation: Ivshin A.A., Boldina Yu.S., Malyshev N.A. Additional prognostic value of second-trimester biomarker measurement for predicting preeclampsia before 37 weeks in a Russian cohort.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2026; (4): 96-104 (in Russian)
https://dx.doi.org/10.18565/aig.2026.51
Keywords
preeclampsia
prediction
machine learning
Fetal Medicine Foundation
placental growth factor
uterine artery pulsatility index
calibration
prenatal screening
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Received 03.02.2026
Accepted 31.03.2026
About the Authors
Aleksandr A. Ivshin, PhD, Associate Professor, Head of the Department of Obstetrics and Gynecology, Dermatovenerology of the Medical Institute, Petrozavodsk State University, 31, Krasnoarmeyskaya str., Petrozavodsk, Republic of Karelia, 185035, Russia, +7(909)567-12-51, scipeople@mail.ru, https://orcid.org/0000-0001-7834-096XYuliya S. Boldina, Senior Lecturer at the Department of Obstetrics, Gynecology, Dermatovenereology of the Medical Institute, Petrozavodsk State University, 31, Krasnoarmeyskaya str., Petrozavodsk, Republic of Karelia, 185035, Russia; Obstetrician-Gynecologist, Republican Perinatal Center named after K.A. Gutkin, Petrozavodsk, +7(981)405-85-24, ulia.isakova94@gmail.com, https://orcid.org/0000-0002-1450-650X
Nikita A. Malyshev, PhD Student in the scientific specialty «Information-Measuring and Control Systems», Lecturer, Department of Family Medicine, Public Health, Healthcare Management, Life Safety, Disaster Medicine, Petrozavodsk State University, 31, Krasnoarmeyskaya str., Petrozavodsk, Republic of Karelia, 185035, Russia, +7(921)461-38-60, malyshev.nikita.2016@gmail.com, https://orcid.org/0009-0005-2722-5976
Corresponding author: Alexander A. Ivshin, scipeople@mail.ru
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