{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T16:12:32Z","timestamp":1770912752865,"version":"3.50.1"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:00:00Z","timestamp":1770854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Glob. Women\u2019s Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Hypertensive disorders in pregnancy (HDP) include gestational hypertension, preeclampsia, and eclampsia. Not all cases of gestational hypertension or mild preeclampsia progress to severe conditions. However, once they develop into severe preeclampsia (SPE), the risks to both the mother and the fetus increase significantly. We aimed to establish a nomogram and train a machine learning (ML) model that could identify SPE, early in the course of HDP.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this retrospective study, 593 patients with HDP were enrolled in the training cohort. For predicting SPE early, six supervised ML models were employed, such as XGBoost, K-nearest neighbors (KNN), random forest (RF), LightGBM (LGBM), Support Vector Machines (SVM), and Decision Trees (DT), which were evaluated by accuracy (ACC) and the areas under the receiver operating characteristic curve (AUC). The nomogram was established, and the predictive ability was assessed by AUC, the calibration curve and clinical decision curves (DCA). They were validated by a validation cohort of 255 patients with HDP.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The nomogram model achieved an AUC of 0.934 in the training cohort, with a calibration curve Brier score of 0.083 and a clinical applicability probability threshold of 5%\u201395%. In the validation cohort, it showed an AUC of 0.882, a calibration curve Brier score of 0.115, and a clinical applicability probability threshold of 10%\u201395%. In the validation cohort, the AUC of XGBoost, KNN, RF, LGBM, SVM, DT, and multivariate logistic regression analysis models were 0.876, 0.822, 0.866, 0.866, 0.871, 0.784, and 0.847, the XGBoost model showed the highest AUC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study demonstrates that a family history of hypertension, urine protein, umbilical artery S\/D ratio, WBC, TBIL, UA, LDL, TG, CRP, and blood Ca are predictors of HDP progression to SPE. A nomogram model for predicting the progression of HDP to SPE was constructed using these predictors. The model exhibited good discrimination, calibration, and clinical utility in both the training and validation cohorts. Additionally, a ML model was developed, with the XGBoost model identified as the optimal one, which can be applied clinically in conjunction with the nomogram prediction model.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fgwh.2026.1672534","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T15:13:11Z","timestamp":1770909191000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The clinical value of nomogram combined with machine learning models in predicting the progression of hypertensive disorders in pregnancy to severe preeclampsia"],"prefix":"10.3389","volume":"7","author":[{"given":"Xiaoming","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Clinical Laboratory, Wenzhou People\u2019s Hospital, Wenzhou Women and Children\u2019s Hospital","place":["Zhejiang, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingcheng","family":"Mao","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, the Second Affiliated Hospital and Yuying Children\u2019s Hospital of Wenzhou Medical University","place":["Zhejiang, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, the Second Affiliated Hospital and Yuying Children\u2019s Hospital of Wenzhou Medical University","place":["Zhejiang, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1434","DOI":"10.1080\/13813455.2020.1773863","article-title":"Association between maternal serum uric acid and preeclampsia","volume":"128","author":"Shakarami","year":"2022","journal-title":"Arch Physiol Biochem"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1097\/01.AOG.0000437382.03963.88","article-title":"Hypertension in pregnancy. Report of the American college of obstetricians and gynecologists\u2019 task force on hypertension in pregnancy","volume":"122","year":"2013","journal-title":"Obstet Gynecol"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e26","DOI":"10.1097\/AOG.0000000000003020","article-title":"ACOG practice bulletin no. 203: chronic hypertension in pregnancy","volume":"133","year":"2019","journal-title":"Obstet Gynecol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1016\/S0140-6736(15)00070-7","article-title":"Pre-eclampsia","volume":"387","author":"Mol","year":"2016","journal-title":"Lancet"},{"key":"B5","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/s11906-016-0637-6","article-title":"Preterm birth and hypertension: is there a link?","volume":"18","author":"Bertagnolli","year":"2016","journal-title":"Curr Hypertens Rep"},{"key":"B6","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1038\/s41581-019-0119-6","article-title":"Pre-eclampsia: pathogenesis, novel diagnostics and therapies","volume":"15","author":"Phipps","year":"2019","journal-title":"Nat Rev Nephrol"},{"key":"B7","doi-asserted-by":"publisher","first-page":"S115","DOI":"10.1016\/j.ajog.2015.08.042","article-title":"Why is placentation abnormal in preeclampsia?","volume":"213","author":"Fisher","year":"2015","journal-title":"Am J Obstet Gynecol"},{"key":"B8","doi-asserted-by":"publisher","first-page":"105","DOI":"10.3109\/10641958209037184","article-title":"Uteroplacental blood flow in pre-eclampsia measurements with indium-113\u2005m and a computer-linked gamma camera","volume":"1","author":"Lunell","year":"1982","journal-title":"Clin Exp Hypertens B"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1161\/01.HYP.37.4.1191","article-title":"Reduced uterine perfusion pressure during pregnancy in the rat is associated with increases in arterial pressure and changes in renal nitric oxide","volume":"37","author":"Alexander","year":"2001","journal-title":"Hypertension"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.1016\/j.jtcvs.2017.12.107","article-title":"Nomogram: an analogue tool to deliver digital knowledge","volume":"155","author":"Park","year":"2018","journal-title":"J Thorac Cardiovasc Surg"},{"key":"B11","doi-asserted-by":"publisher","first-page":"961348","DOI":"10.3389\/fnins.2022.961348","article-title":"Prediction of pre-eclampsia by using radiomics nomogram from gestational hypertension patients","volume":"16","author":"Liu","year":"2022","journal-title":"Front Neurosci"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1089\/jwh.2020.8575","article-title":"Development and validation of a nomogram for predicting the risk of pregnancy-induced hypertension: a retrospective cohort study","volume":"30","author":"Li","year":"2021","journal-title":"J Womens Health (Larchmt)"},{"key":"B13","doi-asserted-by":"publisher","first-page":"104641","DOI":"10.1016\/j.ijmedinf.2021.104641","article-title":"Machine learning predictive models for acute pancreatitis: a systematic review","volume":"157","author":"Zhou","year":"2022","journal-title":"Int J Med Inform"},{"key":"B14","doi-asserted-by":"publisher","first-page":"77.e71","DOI":"10.1016\/j.ajog.2022.01.026","article-title":"A machine-learning-based algorithm improves prediction of preeclampsia-associated adverse outcomes","volume":"227","author":"Schmidt","year":"2022","journal-title":"Am J Obstet Gynecol"},{"key":"B15","doi-asserted-by":"publisher","first-page":"e0221202","DOI":"10.1371\/journal.pone.0221202","article-title":"Prediction model development of late-onset preeclampsia using machine learning-based methods","volume":"14","author":"Jhee","year":"2019","journal-title":"PLoS One"},{"key":"B16","doi-asserted-by":"publisher","first-page":"896969","DOI":"10.3389\/fphys.2022.896969","article-title":"Development of a prediction model on preeclampsia using machine learning-based method: a retrospective cohort study in China","volume":"13","author":"Liu","year":"2022","journal-title":"Front Physiol"},{"key":"B17","doi-asserted-by":"publisher","first-page":"566","DOI":"10.13602\/j.cnki.jcls.2021.08.02","article-title":"Research on a risk prediction model for preeclampsia based on risk factors and routine laboratory indicators","volume":"39","author":"Deng","year":"2021","journal-title":"Chin J Clin Lab Sci"},{"key":"B18","doi-asserted-by":"publisher","first-page":"802","DOI":"10.11855\/j.issn.0577-7402.2022.08.0802","article-title":"Construction of a prediction model for preeclampsia based on machine learning algorithms","volume":"47","author":"Zheng","year":"2022","journal-title":"Med J Chin Peoples Lib Army"},{"key":"B19","doi-asserted-by":"publisher","first-page":"910701","DOI":"10.3389\/fcvm.2022.910701","article-title":"Prediction of delivery within 7 days after diagnosis of early onset preeclampsia using machine-learning models","volume":"9","author":"Villala\u00edn","year":"2022","journal-title":"Front Cardiovasc Med"},{"key":"B20","doi-asserted-by":"publisher","first-page":"e22115","DOI":"10.1371\/journal.pone.0022115","article-title":"Relationships of risk factors for pre-eclampsia with patterns of occurrence of isolated gestational proteinuria during normal term pregnancy","volume":"6","author":"Macdonald-Wallis","year":"2011","journal-title":"PLoS One"},{"key":"B21","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1016\/j.bpobgyn.2011.01.006","article-title":"Epidemiology of pre-eclampsia and the other hypertensive disorders of pregnancy","volume":"25","author":"Hutcheon","year":"2011","journal-title":"Best Pract Res Clin Obstet Gynaecol"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1620","DOI":"10.1111\/aogs.14198","article-title":"Progression from isolated gestational proteinuria to preeclampsia with severe features","volume":"100","author":"Tzur","year":"2021","journal-title":"Acta Obstet Gynecol Scand"}],"container-title":["Frontiers in Global Women's Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fgwh.2026.1672534\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T15:17:14Z","timestamp":1770909434000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fgwh.2026.1672534\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,12]]},"references-count":22,"alternative-id":["10.3389\/fgwh.2026.1672534"],"URL":"https:\/\/doi.org\/10.3389\/fgwh.2026.1672534","relation":{},"ISSN":["2673-5059"],"issn-type":[{"value":"2673-5059","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,12]]},"article-number":"1672534"}}