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<p>Hypertensive disorders of pregnancy (HDP) remain a leading cause of maternal and perinatal morbidity worldwide, and current screening strategies have limited predictive value in low-risk populations especially for late-onset HDP. This study aimed to develop and internally validate an interpretable machine-learning model for predicting HDP using noninvasive parameters of maternal systemic hemodynamics, endothelial function, and utero-placental and feto-placental blood flow measured in mid-pregnancy. In this cross-sectional cohort, 577 normotensive women underwent impedance cardiography and Doppler ultrasonography at 22<sup>+ 0</sup> to 23<sup>+ 6</sup> weeks’ gestation. The incidence of HDP was 16.6% (96/577) most (87.5%) occurring at term (≥ 37 weeks), including 73 cases of gestational hypertension (12.6%) and 23 cases of pre-eclampsia (4.0%). An optimized predictive model with seven physiological features was developed using Extreme Gradient Boosting (XGBoost). Hyperparameters were optimized using stratified 10-fold cross-validation maximizing average Precision Recall Area Under the Curve (PR–AUC), and the decision threshold was selected by maximizing the geometric mean of sensitivity and specificity on a separate validation set. On an independent test set (<i>n</i> = 58; 10 HDP), the model achieved an Area Under the Receiver Operating Curve (ROC–AUC) of 0.82 (95% CI 0.65–0.95) and a PR–AUC of 0.57 (95% CI 0.26–0.84). At the optimized operating point, sensitivity was 70% (95% CI 0.40–1.00), specificity 79% (95% CI 0.67–0.90), precision 41% (95% CI 0.18–0.65), and negative predictive value 93% (95% CI 0.84–1.00). This interpretable, non-invasive mid-gestation model demonstrates strong discrimination and excellent negative predictive value, supporting its integration into routine second-trimester screening for risk stratification without reliance on biochemical markers.</p>

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Development and validation of a novel prediction model for hypertensive disorders of pregnancy based on maternal cardiovascular function and placental blood flow metrics at 22 to 24 gestational weeks using machine learning

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  • 发表日期：

    2025年12月9日

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  • Juulia Lantto,
  • Kari Flo,
  • Åse Vårtun,
  • Christian Widnes,
  • Jonas Johnson,
  • Ganesh Acharya

摘要

Hypertensive disorders of pregnancy (HDP) remain a leading cause of maternal and perinatal morbidity worldwide, and current screening strategies have limited predictive value in low-risk populations especially for late-onset HDP. This study aimed to develop and internally validate an interpretable machine-learning model for predicting HDP using noninvasive parameters of maternal systemic hemodynamics, endothelial function, and utero-placental and feto-placental blood flow measured in mid-pregnancy. In this cross-sectional cohort, 577 normotensive women underwent impedance cardiography and Doppler ultrasonography at 22+ 0 to 23+ 6 weeks’ gestation. The incidence of HDP was 16.6% (96/577) most (87.5%) occurring at term (≥ 37 weeks), including 73 cases of gestational hypertension (12.6%) and 23 cases of pre-eclampsia (4.0%). An optimized predictive model with seven physiological features was developed using Extreme Gradient Boosting (XGBoost). Hyperparameters were optimized using stratified 10-fold cross-validation maximizing average Precision Recall Area Under the Curve (PR–AUC), and the decision threshold was selected by maximizing the geometric mean of sensitivity and specificity on a separate validation set. On an independent test set (n = 58; 10 HDP), the model achieved an Area Under the Receiver Operating Curve (ROC–AUC) of 0.82 (95% CI 0.65–0.95) and a PR–AUC of 0.57 (95% CI 0.26–0.84). At the optimized operating point, sensitivity was 70% (95% CI 0.40–1.00), specificity 79% (95% CI 0.67–0.90), precision 41% (95% CI 0.18–0.65), and negative predictive value 93% (95% CI 0.84–1.00). This interpretable, non-invasive mid-gestation model demonstrates strong discrimination and excellent negative predictive value, supporting its integration into routine second-trimester screening for risk stratification without reliance on biochemical markers.