{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T18:30:21Z","timestamp":1787941821537,"version":"build-2784847793"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004587","name":"Instituto de Salud Carlos III","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004587","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Early onset preeclampsia (eoPE) is a hypertensive disorder of pregnancy with endothelial dysfunction manifested before 34 weeks where expectant management is usually attempted. However, the timing of hospitalization, corticosteroids, and delivery remain a challenge. We aim to develop a prediction model using machine-learning tools for the need for delivery within 7 days of diagnosis (model D) and the risk of developing hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome or <jats:italic>abruptio placentae<\/jats:italic> (model HA).<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>A retrospective cohort of singleton pregnancies with eoPE and attempted expectant management between 2014 and 2020. A Mono-objective Genetic Algorithm based on supervised classification models was implemented to develop D and HA models. Maternal basal characteristics and data gathered during eoPE diagnosis: gestational age, blood pressure, platelets, creatinine, transaminases, angiogenesis biomarkers (soluble fms-like tyrosine kinase-1, placental growth factor), and ultrasound data were pooled for analysis. The most relevant variables were selected by bio-inspired algorithms. We developed basal models that solely included demographic characteristics of the patient (D1, HA1), and advanced models adding information available at diagnosis of eoPE (D2, HA2).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We evaluated 215 eoPE cases and 47.9% required delivery within 7 days. The median time-to-delivery was 8 days. Basal models were better predicted by K-nearest-neighbor in D1, which had a diagnostic precision of 0.68 \u00b1 0.09, with 63.6% sensitivity (Sn), 71.4% specificity (Sp), 70% positive predictive value (PPV), and 65.2% negative predictive value (NPV) using 13 variables and HA1 of 0.77 \u00b1 0.09, 60.4% Sn, 80% Sp, 50% PPV, and 87.9% NPV. Models at diagnosis were better developed by support vector machine (SVM) using 18 variables, where D2\u2019s precision improved to 0.79 \u00b1 0.05 with 77.3% Sn, 80.1% Sp, 81.5% PPV, and 76.2% NPV, and HA2 had a precision of 0.79 \u00b1 0.08 with 66.7% Sn, 82.8% Sp, 51.6% PPV, and 90.3% NPV.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>At the time of diagnosis of eoPE, SVM with evolutionary feature selection process provides good predictive information of the need for delivery within 7 days and development of HELLP\/<jats:italic>abruptio placentae<\/jats:italic>, using maternal characteristics and markers that can be obtained routinely. This information could be of value when assessing hospitalization and timing of antenatal corticosteroid administration.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcvm.2022.910701","type":"journal-article","created":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T05:09:13Z","timestamp":1656652153000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["Prediction of Delivery Within 7 Days After Diagnosis of Early Onset Preeclampsia Using Machine-Learning Models"],"prefix":"10.3389","volume":"9","author":[{"given":"Cecilia","family":"Villala\u00edn","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ignacio","family":"Herraiz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paula","family":"Dom\u00ednguez-Del Olmo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pablo","family":"Angulo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Luis","family":"Ayala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Galindo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,7,1]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","first-page":"e237","DOI":"10.1097\/AOG.0000000000003891","volume":"135","year":"2020","journal-title":"Obstet Gynecol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.bpobgyn.2016.05.005","article-title":"Preventing deaths due to the hypertensive disorders of pregnancy.","volume":"36","author":"von Dadelszen","year":"2016","journal-title":"Best Pract Res Clin Obstet Gynaecol."},{"key":"B3","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/S0140-6736(10)60279-6","article-title":"Pre-eclampsia.","volume":"376","author":"Steegers","year":"2010","journal-title":"Lancet."},{"key":"B4","article-title":"Interventionist versus expectant care for severe pre-eclampsia between 24 and 34 weeks\u2019s gestation.","volume":"10","author":"Churchill","year":"2018","journal-title":"Cochrane Database Syst Rev."},{"key":"B5","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1373\/clinchem.2011.169094","article-title":"The promise of angiogenic markers for the early diagnosis and prediction of preeclampsia.","volume":"58","author":"Hagmann","year":"2012","journal-title":"Clin Chem."},{"key":"B6","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1172\/JCI17189","article-title":"Excess placental soluble fms-like tyrosine kinase 1 (sFlt1) may contribute to endothelial dysfunction, hypertension, and proteinuria in preeclampsia.","volume":"111","author":"Maynard","year":"2003","journal-title":"J Clin Invest."},{"key":"B7","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1056\/NEJMoa031884","article-title":"Circulating angiogenic factors and the risk of preeclampsia.","volume":"350","author":"Levine","year":"2004","journal-title":"N Engl J Med."},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.119.015548","article-title":"Maternal and perinatal outcomes associated with extremely high values for the sFlt-1 (soluble fms-like tyrosine kinase 1) \/PlGF (placental growth factor) ratio.","volume":"9","author":"Villala\u00edn","year":"2020","journal-title":"J Am Heart Assoc."},{"key":"B9","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/S0140-6736(10)61351-7","article-title":"Prediction of adverse maternal outcomes in pre-eclampsia: development and validation of the fullPIERS model.","volume":"377","author":"von Dadelszen","year":"2011","journal-title":"Lancet."},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajogmf.2020.100100","article-title":"Early prediction of preeclampsia via machine learning.","volume":"2","author":"Mari\u0107","year":"2020","journal-title":"Am J Obstet Gynecol MFM."},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0221202","article-title":"Prediction model development of late-onset preeclampsia using machine learning-based methods.","volume":"23","author":"Jhee","year":"2019","journal-title":"PLoS One."},{"key":"B12","doi-asserted-by":"publisher","first-page":"251","DOI":"10.4018\/978-1-5225-3004-6.ch014","article-title":"Bio-inspired algorithms for medical data analysis.","author":"Menad","year":"2018","journal-title":"Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management."},{"key":"B13","year":"2019","journal-title":"Hypertension in Pregnancy: Diagnosis and Management. 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