Comparative effectiveness of ML model and fetal medicine foundation algorithm for preterm preeclampsia prediction: a validation study in Russian population

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Abstract

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.

About the authors

Aleksandr A. Ivshin

Petrozavodsk State University

Author for correspondence.
Email: scipeople@mail.ru
ORCID iD: 0000-0001-7834-096X

PhD, Associate Professor, Head of the Department of Obstetrics and Gynecology, Dermatovenerology of the Medical Institute

Russian Federation, Petrozavodsk

Yuliya S. Boldina

Petrozavodsk State University; Republican Perinatal Center named after K.A. Gutkin

Email: ulia.isakova94@gmail.com
ORCID iD: 0000-0002-1450-650X

PhD Student, Senior Lecturer of Department of Obstetrics, Gynecology, Dermatovenereology of the Medical Institute, Obstetrician-Gynecologist

Russian Federation, Petrozavodsk; Petrozavodsk

Nikita A. Malyshev

Petrozavodsk State University

Email: malyshev.nikita.2016@gmail.com
ORCID iD: 0009-0005-2722-5976

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

Russian Federation, Petrozavodsk

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