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The Journal "Obstetrics and Gynecology"Journal HistoryAims and ScopePolicies on Conflict of Interest, Human and Animal Rights, and Informed ConsentEditorial ProcessData sharing statementAdvertising PolicyThe Process for Handling Cases Requiring Corrections, Retractions, and Editorial Expressions of ConcernEditorial BoardEditorial CounsilInformation for ProfessionalsNewsContacts
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Obstetrics and Gynegology2026№6

Comparative effectiveness of ML model and Fetal Medicine Foundation algorithm for preterm preeclampsia prediction: a validation study in Russian population

DOI
https://dx.doi.org/10.18565/aig.2026.13

Ivshin A.A., Boldina Yu.S., Malyshev N.A.

1) Petrozavodsk State University, Petrozavodsk, Russia; 2) Republican Perinatal Center named after K.A. Gutkin, Petrozavodsk, Russia

Objective. To compare the performance of a machine-learning (ML) model based on logistic regression with Platt scaling and the Fetal Medicine Foundation (FMF) algorithm for predicting preterm pre-eclampsia in a Russian population.
Materials and methods. This retrospective cohort study included 14 950 singleton pregnancies (development cohort: n=7581; validation cohort: n=7369). Maternal characteristics, biophysical markers (systolic blood pressure and uterine artery pulsatility index), and biochemical markers (PlGF, PAPP-A, and sFlt-1) assessed at 11–13 weeks of gestation were analyzed. The primary outcome was pre-eclampsia requiring delivery before 37 weeks of gestation. Discrimination (AUC), calibration (observed-to-expected ratio [O:E] and Brier score), and clinical utility (decision curve analysis [DCA] ) were compared.
Results. On internal validation, the ML model outperformed the FMF algorithm (AUC 0.923 vs. 0.906; ΔAUC=+0.017; p=0.013). On external validation, the between-model difference was not statistically significant (AUC 0.900 vs. 0.889; p=0.229). The ML model demonstrated substantially better calibration (O:E 0.974 vs. 0.808). The intersection of the ROC curves was observed at a false-positive rate threshold of approximately 7–8%.
Conclusion. The ML model showed comparable discriminative performance and markedly superior calibration compared with the FMF algorithm. The observed complementarity of the models suggests the potential of hybrid approaches to optimize preeclampsia screening in the Russian population.

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 supported by the Russian Science Foundation grant 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: 17 of 20.03.2024).
Generative Artificial Intelligence. No artificial intelligence tools were used in the preparation of this manuscript. Statistical analysis was performed by the author using R 4.3 and Python 3.11. The author bears full responsibility for the content of the publication.
Patient Consent for Publication. Given the retrospective nature of the study and the use of de-identified data, informed consent was not required under applicable law.
Authors' Data Sharing Statement. The study data contain personal medical information and cannot be made publicly available in accordance with the requirements of Federal Law No. 152-FZ “On Personal Data.” Depersonalized aggregated data may be provided for justified research requests following approval by the local ethics committee. The Python code, including scripts for data preprocessing, model training, and validation, is available in a GitHub repository upon request from the corresponding author.
For citation: Ivshin A.A., Boldina Yu.S., Malyshev N.A. Comparative effectiveness of ML model and Fetal Medicine Foundation algorithm for preterm preeclampsia prediction: a validation study in Russian population.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2026; (6): 109-124 (in Russian)
https://dx.doi.org/10.18565/aig.2026.13

Keywords

pre-eclampsia
prediction
machine learning
FMF algorithm
first-trimester screening
validation
calibration
Full text (in Russian)

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Received 19.01.2026

Accepted 11.02.2026

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/0000-0001-7834-096X
Yuliya S. Boldina, PhD Student, Senior Lecturer of 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 of the Medical Institute, 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: Alexandr A. Ivshin, scipeople@mail.ru

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