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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
ArchiveEthical StandardsSubscription
For authors
InstructionsInformed consent policyContract offerEASE GuidelinesICJME CriteriaWAME Recommendations on ChatGPT and Chatbots in Relation to Scholarly Publications
Reviewing
Procedure for reviewingReviewers 2023-2024Reviewers 2026
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Obstetrics and Gynegology2026№10

External validation of the Fetal Medicine Foundation algorithm for predicting preeclampsia before 37 weeks' gestation in a Russian cohort

DOI
https://dx.doi.org/10.18565/aig.2026.105

Ivshin A.A.

Petrozavodsk State University, Petrozavodsk, Russia

Objective. To externally validate the Fetal Medicine Foundation (FMF) algorithm for predicting preeclampsia before 37 weeks' gestation (preterm preeclampsia) in a Russian cohort, comprehensively assessing its discrimination, calibration, and clinical utility.
Materials and methods. This retrospective cohort study utilized data from a multicenter pregnancy registry. The validation cohort included 7,369 women, with 119 cases (1.61%) of preterm preeclampsia. Preterm preeclampsia risk was calculated using the extended configuration of the FMF algorithm, which incorporates maternal factors and first-trimester biomarkers (MAP, UtAPI, PlGF, PAPP-A). Outcomes assessed included AUC, O:E ratio, calibration slope, screening performance at various risk thresholds, and decision curve analysis.
Results. The FMF algorithm demonstrated high discriminative ability, with an AUC of 0.893 (95% CI: 0.856–0.925). Calibration was good, indicated by an O:E ratio of 1.007 (95% CI: 0.842–1.206). At the standard 1:100 risk threshold, sensitivity was 84.0%, specificity 80.2%, screen-positive rate 20.8%, and negative predictive value 99.7%. Subgroup analysis revealed risk overestimation in women with chronic hypertension (O:E=0.41) and underestimation in women with prior preeclampsia (O:E=1.35). External validation in an additional cohort (n=7,581) confirmed these results, with an AUC of 0.907 and an O:E of 1.085. Sensitivity at the standardized 10% false-positive rate, conventionally used in international FMF validations, was 75.6% (95% CI: 66.9–83.0). Secondary analysis for early preeclampsia (delivery before 34 weeks; 131 cases in the pooled cohort) showed higher discriminative ability (AUC=0.919 and 0.956 in the two cohorts) but with systematic overestimation of risk (O:E=0.516 and 0.551).
Conclusion. The FMF algorithm demonstrates high discriminative ability and high calibration accuracy for preterm preeclampsia prediction in a Russian cohort, comparable to findings from major European validation studies. These results support the implementation of the FMF algorithm in the Russian prenatal screening system when using the standard 1:100 risk threshold.

Conflicts of interest. The author has no conflicts of interest to declare.
Funding. There was no funding for this study.
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.
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.
Author's Data Sharing Statement. Anonymized data are available from the author upon a substantiated request, in accordance with the requirements of Federal Law No. 152-FZ "On Personal Data." Supplementary materials (Appendix) are publicly available: https://doi.org/10.5281/zenodo.19286495
For citation: Ivshin A.A. External validation of the Fetal Medicine Foundation algorithm for 
predicting preeclampsia before 37 weeks' gestation in a Russian cohort.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2026; (10): 56-67 (in Russian)
https://dx.doi.org/10.18565/aig.2026.105

Keywords

preeclampsia
first-trimester screening
FMF algorithm
external validation
calibration
prediction model
Russian cohort
Full text (in Russian)

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Received 31.03.2026

Accepted 02.10.2026

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

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