akusherstvoObstetrics, Gynecology and ReproductionАкушерство, Гинекология и Репродукция2313-73472500-3194IRBIS LLC10.17749/2313-7347/ob.gyn.rep.2026.729akusherstvo-2897Research ArticleORIGINAL ARTICLEОРИГИНАЛЬНОЕ ИССЛЕДОВАНИЕComparative effectiveness of eight machine learning algorithms and the FMF competing risks model for first-trimester preeclampsia prediction: a multicenter cohort study with external validationСравнительная эффективность восьми алгоритмов машинного обучения и модели конкурирующих рисков FMF для прогнозирования преэклампсии в первом триместре: многоцентровое когортное исследование с внешней валидациейhttps://orcid.org/0000-0001-7834-096XИвшинА. А.IvshinA. A.

Ившин Александр Анатольевич, к.м.н.

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185910 Петрозаводск, проспект Ленина, д. 33

Alexandr A. Ivshin, МD, PhD.

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33 Lenin Avenue, Petrozavodsk 185910

scipeople@mail.ruhttps://orcid.org/0000-0002-1450-650XБолдинаЮ. С.BoldinaYu. S.

Болдина Юлия Сергеевна

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185910 Петрозаводск, проспект Ленина, д. 33

Yuliya S. Boldina, МD

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https://orcid.org/0009-0005-2722-5976МалышевН. А.MalyshevN. A.

Малышев Никита Андреевич

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185910 Петрозаводск, проспект Ленина, д. 33

Nikita A. Malyshev, МD

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33 Lenin Avenue, Petrozavodsk 185910

ФГБОУ ВО «Петрозаводский государственный университет»РоссияPetrozavodsk State UniversityRussian Federation20263107202600Online First2897Copyright © Ivshin A.A., Boldina Y.S., Malyshev N.A., 20262026Ившин А.А., Болдина Ю.С., Малышев Н.А.Ivshin A.A., Boldina Y.S., Malyshev N.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.gynecology.su/jour/article/view/2897Aim

Aim: to systematically compare the discrimination and calibration of eight machine learning (ML) algorithms with the Fetal Medicine Foundation (FMF) competing risks algorithm for first-trimester prediction of early-onset (< 34 weeks), late-onset (> 34 weeks), severe, and preterm (< 37 weeks) preeclampsia (PE) by assessing the incremental value of markers with a nested model architecture (M0 [model includes only clinical and anamnestic data] → M2 [model supplemented with biophysical markers] → M4 [model supplemented with biochemical markers]) and external validation in an independent dataset (n = 7,581).

Materials and Methods

Materials and Methods. A retrospective cohort study (TRIPOD 2b + 3) was conducted. Development: a development cohort (n = 7,581) with 10-fold stratified cross-validation. External validation: an independent cohort (n = 7,581). A total of 96 benchmark configurations were developed (8 algorithms × 3 predictor levels × 4 outcomes): M0 – 15 clinical and history-based factors; M2 – adding mean arterial pressure (MAP) and uterine artery pulsatility index (UtAPI); M4 – adding placental growth factor (PlGF) and pregnancy-associated plasma protein-A (PAPP-A). Evaluation metrics: AUC-ROC (Area Under the Receiver Operating Characteristic curve), calibration (O:E ratio (observed-to-expected ratio), calibration slope, Brier score), net reclassification improvement (NRI). integrated discrimination improvement (IDI), decision curve analysis (DCA), and SHapley Additive exPlanations (SHAP).

Results

Results. At the M4 level, logistic regression demonstrated the highest discrimination: early-onset PE – AUC = 0.976 (an optimistic estimate with EPV (events per variable) = 3.7; detection rate 92.9 % at 10 % FPR (false positive rate); late-onset PE – 0.837; severe PE – 0.885, preterm PE – 0.908; with the most robust calibration among all algorithms (O:E = 0.99–1.00), confirmed in the external cohort for M0 and M2 levels. Nonlinear algorithms and stacking did not achieve a significant advantage. For preterm PE (the target outcome of the FMF algorithm), logistic regression and FMF posterior were comparable in discrimination (ΔAUC = +0.002; p > 0.05) level, with the former showing superior calibration in the Russian population. For late-onset PE (the dominant disease phenotype not modeled by FMF), logistic regression provided significantly better risk stratification (AUC = 0.837 vs. 0.807; p < 0.05). The FMF algorithm exhibited miscalibration: risk overestimation for its target outcomes (O:E = 0.55 for early-onset PE) and structural underestimation for non-target outcomes (O:E = 2.84 for late-onset PE). Biophysical markers were critical for early-onset PE, whereas biochemical markers were critical for late-onset PE; this pattern was consistent across algorithms. External validation confirmed robust discrimination at the M0 level (AUC = 0.790–0.823; n = 7,581) and M2 level (AUC = 0.801–0.931; n = 4,080); results at the M4 level (n = 1,010; 11–40 events) are preliminary.

Conclusion

Conclusion. Nonlinear ML algorithms do not outperform logistic regression, which demonstrated the most robust calibration confirmed by external validation at the M0 and M2 levels. Logistic regression with an M0 → M2 → M4 architecture is recommended for clinical decision support systems in first-trimester PE screening. The FMF algorithm requires population-specific recalibration for its target outcomes and is structurally not designed to predict late-onset PE.

Цель

Цель: систематическое сравнение дискриминации и калибровки 8 алгоритмов машинного обучения (англ. machine learning, ML) с эталонным алгоритмом Фонда фетальной медицины (англ. Fetal Medicine Foundation, FMF) для прогнозирования ранней (< 34 нед), поздней (> 34 нед), тяжелой и преждевременной (< 37 нед) преэклампсии (ПЭ) в I триместре с оценкой инкрементальной ценности маркеров при вложенной архитектуре моделей (M0 [модель включает только клинико-анамнестические данные] → M2 [модель дополнена биофизическими маркерами] → M4 [модель дополнена биохимическими маркерами]) и внешней валидацией на независимом наборе данных (n = 7581).

Материалы и методы

Материалы и методы. Ретроспективное когортное исследование (TRIPOD 2b + 3). Разработка: когорта разработки (n = 7581), 10-кратная стратифицированная кросс-валидация. Внешняя валидация: независимая когорта (n = 7581). Разработано 96 конфигураций моделей сравнения (8 алгоритмов × 3 уровня предикторов × 4 исхода): M0 – 15 клинико-анамнестических факторов, M2 – со средним артериальным давлением (англ. mean arterial pressure, MAP), пульсационным индексом маточных артерий (англ. uterine artery pulsatility index, UtAPI), M4 – с плацентарным фактором роста (англ. placental growth factor, PlGF) и ассоциированным с беременностью протеином-А плазмы (англ. pregnancy-associated plasma protein-A, PAPP-A). Оценка: AUC-ROC (англ. Area Under the Receiver Operating Characteristic curve; площадь под кривой рабочих характеристик приемника); калибровка – O:E (англ. observed-to-expected ratio; отношение наблюдаемого числа событий к ожидаемому); наклон калибровочной кривой, оценка Бриера; непрерывный индекс реклассификации (англ. net reclassification improvement, NRI); интегральный индекс улучшения дискриминации (англ. integrated discrimination improvement, IDI); анализ кривых принятия решений (англ. decision curve analysis, DCA) и глобальных объяснений Шепли (англ. SHapley Additive exPlanations, SHAP) – метод интерпретации прогнозов модели путем расчета вклада каждого признака в предсказание.

Результаты

Результаты. На уровне M4 логистическая регрессия продемонстрировала наивысшую дискриминацию: ранняя ПЭ – AUC = 0,976, оптимистичная оценка при числе событий на переменную (англ. events per variable; EPV) = 3,7; доля выявления при 10 % FPR (англ. false positive rate; доля ложноположительных результатов) – 92,9 %; поздняя ПЭ – AUC = 0,837; тяжелая ПЭ – AUC = 0,885; преждевременная ПЭ – AUC = 0,908 при наиболее устойчивой калибровке среди всех алгоритмов при внутренней валидации [O:E = 0,99–1,00 – ожидаемое свойство логистической регрессии (англ. Logistic Regression; LogReg)], подтвержденной на внешней когорте для уровней M0 и M2. Нелинейные алгоритмы и ансамблевые модели с метаобучением не достигли значимого превосходства. Для преждевременной ПЭ (целевого исхода алгоритма FMF) логистическая регрессия и апостериорный риск FMF (англ. FMF posterior) сопоставимы по дискриминации (ΔAUC = +0,002; p > 0,05) при лучшей калибровке LogReg на российской популяции. Для поздней ПЭ (доминирующей формы заболевания) логистическая регрессия, обученная на данный исход, превосходит FMF-риск, использованный как ранговый предиктор нецелевого исхода (AUC = 0,837 vs. 0,807; p < 0,05). FMF обнаружил дискалибровку: переоценку риска для моделируемых исходов (O:E = 0,55 для ранней ПЭ) и структурную недооценку для нецелевых исходов (O:E = 2,84 для поздней ПЭ). Биофизические маркеры были критичны для ранней ПЭ, биохимические – для поздней ПЭ; паттерн устойчив между алгоритмами. Внешняя валидация подтвердила устойчивую дискриминацию на уровне M0 (AUC = 0,790–0,823; n = 7581) и M2 (AUC = 0,801–0,931; n = 4 080); результаты на уровне M4 (n = 1010, 11–40 событий) являются предварительными.

Заключение

Заключение. Нелинейные алгоритмы ML не превосходят логистическую регрессию, обеспечивающую наиболее устойчивую калибровку, подтвержденную при внешней валидации на уровнях M0 и M2. Логистическая регрессия с архитектурой M0 → M2 → M4 рекомендуется для систем поддержки принятия врачебных решений (СППВР) в первотриместровом скрининге ПЭ. Алгоритм FMF требует популяционной рекалибровки для моделируемых исходов и структурно не предназначен для прогнозирования поздней ПЭ.

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The authors declare that there are no conflicts of interest present.