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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\n                    <jats:sup>+\u20090<\/jats:sup>\n                    to 23\n                    <jats:sup>+\u20096<\/jats:sup>\n                    weeks\u2019 gestation. The incidence of HDP was 16.6% (96\/577) most (87.5%) occurring at term (\u2265\u200937 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\u2013AUC), 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                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u200958; 10 HDP), the model achieved an Area Under the Receiver Operating Curve (ROC\u2013AUC) of 0.82 (95% CI 0.65\u20130.95) and a PR\u2013AUC of 0.57 (95% CI 0.26\u20130.84). At the optimized operating point, sensitivity was 70% (95% CI 0.40\u20131.00), specificity 79% (95% CI 0.67\u20130.90), precision 41% (95% CI 0.18\u20130.65), and negative predictive value 93% (95% CI 0.84\u20131.00). 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.\n                  <\/jats:p>","DOI":"10.1038\/s41598-025-30738-3","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T17:55:38Z","timestamp":1765302938000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["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"],"prefix":"10.1038","volume":"15","author":[{"given":"Juulia","family":"Lantto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kari","family":"Flo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c5se","family":"V\u00e5rtun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Widnes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonas","family":"Johnson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1997-3107","authenticated-orcid":false,"given":"Ganesh","family":"Acharya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,9]]},"reference":[{"key":"30738_CR1","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.preghy.2021.03.001","volume":"24","author":"F Li","year":"2021","unstructured":"Li, F. et al. 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