{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T05:02:29Z","timestamp":1785214949495,"version":"3.55.0"},"reference-count":18,"publisher":"Georg Thieme Verlag KG","issue":"11","funder":[{"name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","award":["HD08631301"],"award-info":[{"award-number":["HD08631301"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Am J Perinatol"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This study aimed to determine whether unsupervised machine learning can identify phenotypically distinct subgroups at increased risk for preeclampsia among pregnant individuals with American Heart Association (AHA)-defined normal blood pressure in the first trimester.<\/jats:p>\n                  <jats:p>This was a secondary analysis of a prospective cohort study of singleton pregnancies enrolled at \u2264136\/7 weeks' gestation at two academic centers. Participants with prepregnancy chronic hypertension or major fetal\/placental abnormalities were excluded. First-trimester blood pressure was categorized using the 2017 AHA guidelines. Among individuals with AHA-defined normal blood pressure (&lt;120\/80\u2009mm Hg), unsupervised machine learning (k-means clustering) was applied to systolic, diastolic, and mean arterial pressure to identify distinct hemodynamic phenotypes. The primary outcome was preeclampsia; secondary outcomes included hypertensive disorders of pregnancy (HDP) and small-for-gestational age (SGA) neonates. Associations were assessed using multivariable Cox regression and Kaplan\u2013Meier analyses.<\/jats:p>\n                  <jats:p>Of 570 participants, 378 (66.3%) had AHA-normal blood pressure. Among these, machine learning identified a high-risk cluster (7.4%) and a low-risk cluster (92.6%). Despite normotensive values, individuals in the high-risk cluster had a significantly higher incidence of preeclampsia (25.0 vs. 3.1%; p\u2009&lt;\u20090.001) and HDP (28.6 vs. 5.7%; p\u2009&lt;\u20090.001) compared to the low-risk cluster. After adjustment, the high-risk normotensive cluster had an eight-fold increased hazard of preeclampsia (adjusted hazard ratio [aHR]\u2009=\u20098.01; 95% CI: 3.09\u201320.74) and increased risk of SGA (adjusted odds ratio [aOR]\u2009=\u20093.36; 95% CI: 1.36\u20138.31). Risk within this group exceeded that of individuals with AHA-abnormal blood pressure.<\/jats:p>\n                  <jats:p>Among pregnant individuals with first-trimester AHA-normal blood pressure, unsupervised clustering identified a distinct subgroup at elevated risk for preeclampsia and SGA. These findings suggest that conventional thresholds may overlook early vascular risk and support further investigation into machine learning-based risk stratification in pregnancy.<\/jats:p>","DOI":"10.1055\/a-2781-6377","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T23:53:40Z","timestamp":1768262020000},"page":"1472-1478","source":"Crossref","is-referenced-by-count":0,"title":["First-Trimester Machine Learning to Predict Preeclampsia in Normotensive Pregnancies by American Heart Association Guidelines"],"prefix":"10.1055","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2752-4061","authenticated-orcid":false,"given":"Rebecca","family":"Horgan","sequence":"additional","affiliation":[{"name":"Eastern Virginia Medical School, Old Dominion University, Norfolk, Virginia, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erkan","family":"Kalafat","sequence":"additional","affiliation":[{"name":"Department of Obstetrics & Gynecology, Koc University School of Medicine, Istanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"Sinkovskaya","sequence":"additional","affiliation":[{"name":"Eastern Virginia Medical School, Old Dominion University, Norfolk, Virginia, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfred Z.","family":"Abuhamad","sequence":"additional","affiliation":[{"name":"Eastern Virginia Medical School, Old Dominion University, Norfolk, Virginia, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Saade","sequence":"additional","affiliation":[{"name":"Eastern Virginia Medical School, Old Dominion University, Norfolk, Virginia, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"issue":"06","key":"ref1","doi-asserted-by":"crossref","first-page":"e323","DOI":"10.1016\/S2214-109X(14)70227-X","article-title":"Global causes of maternal death: A WHO systematic analysis","volume":"2","author":"L Say","year":"2014","journal-title":"Lancet Glob Health"},{"issue":"17","key":"ref2","doi-asserted-by":"crossref","first-page":"585","DOI":"10.15585\/mmwr.mm7117a1","article-title":"Hypertensive disorders in pregnancy and mortality at delivery hospitalization - United States, 2017-2019","volume":"71","author":"N D Ford","year":"2022","journal-title":"MMWR Morb Mortal Wkly Rep"},{"issue":"12","key":"ref3","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1001\/jama.2021.14781","article-title":"Aspirin use to prevent preeclampsia and related morbidity and mortality: US Preventive Services Task Force Recommendation Statement","volume":"326","author":"K W Davidson","year":"2021","journal-title":"JAMA"},{"issue":"01","key":"ref5","doi-asserted-by":"crossref","first-page":"e44","DOI":"10.1097\/AOG.0000000000002708","article-title":"ACOG Committee Opinion No. 743: Low-dose aspirin use during pregnancy","volume":"132","year":"2018","journal-title":"Obstet Gynecol"},{"issue":"06","key":"ref6","first-page":"e13","article-title":"2017 ACC\/AHA\/AAPA\/ABC\/ACPM\/AGS\/APhA\/ASH\/ASPC\/NMA\/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A report of the American College of Cardiology\/American Heart Association Task Force on Clinical Practice Guidelines","volume":"71","author":"P K Whelton","year":"2018","journal-title":"Hypertension"},{"issue":"03","key":"ref7","doi-asserted-by":"crossref","first-page":"101261","DOI":"10.1016\/j.ajogmf.2023.101261","article-title":"Pregnancy outcomes among patients with stage 1 chronic hypertension","volume":"6","author":"R Horgan","year":"2024","journal-title":"Am J Obstet Gynecol MFM"},{"issue":"06","key":"ref8","doi-asserted-by":"crossref","first-page":"e237","DOI":"10.1097\/AOG.0000000000003891","article-title":"Gestational hypertension and preeclampsia: ACOG Practice Bulletin, Number 222","volume":"135","author":"American College of Obstetricians and Gynecologists","year":"2020","journal-title":"Obstet Gynecol"},{"issue":"03","key":"ref9","doi-asserted-by":"crossref","first-page":"277.e1","DOI":"10.1016\/j.ajog.2019.06.031","article-title":"Blood pressure trajectory and category and risk of hypertensive disorders of pregnancy in nulliparous women","volume":"221","author":"A Hauspurg","year":"2019","journal-title":"Am J Obstet Gynecol"},{"issue":"03","key":"ref10","doi-asserted-by":"crossref","first-page":"e213808","DOI":"10.1001\/jamanetworkopen.2021.3808","article-title":"Prevalence of hypertension among pregnant women when using the 2017 American College of Cardiology\/American Heart Association Blood Pressure Guidelines and association with maternal and fetal outcomes","volume":"4","author":"N A Bello","year":"2021","journal-title":"JAMA Netw Open"},{"issue":"08","key":"ref11","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1007\/s11906-014-0454-8","article-title":"Pre-eclampsia: An update","volume":"16","author":"P von Dadelszen","year":"2014","journal-title":"Curr Hypertens Rep"},{"issue":"07","key":"ref12","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1056\/NEJMoa031884","article-title":"Circulating angiogenic factors and the risk of preeclampsia","volume":"350","author":"R J Levine","year":"2004","journal-title":"N Engl J Med"},{"issue":"S 01","key":"ref13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/ijgo.12802","article-title":"The International Federation of Gynecology and Obstetrics (FIGO) initiative on pre-eclampsia: A pragmatic guide for first-trimester screening and prevention","volume":"145 suppl 1","author":"L C Poon","year":"2019","journal-title":"Int J Gynaecol Obstet"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"100099","DOI":"10.1016\/j.crphys.2023.100099","article-title":"Machine learning and disease prediction in obstetrics","volume":"6","author":"Z Arain","year":"2023","journal-title":"Curr Res Physiol"},{"issue":"03","key":"ref15","doi-asserted-by":"crossref","first-page":"101612","DOI":"10.1016\/j.ajogmf.2025.101612","article-title":"Novel machine learning applications in peripartum care: A scoping review","volume":"7","author":"S Steinberg","year":"2025","journal-title":"Am J Obstet Gynecol MFM"},{"issue":"06","key":"ref16","doi-asserted-by":"crossref","first-page":"649.e1","DOI":"10.1016\/j.ajog.2024.03.031","article-title":"Prediction of metabolic syndrome following a first pregnancy","volume":"231","author":"T Kawakita","year":"2024","journal-title":"Am J Obstet Gynecol"},{"issue":"03","key":"ref17","doi-asserted-by":"crossref","first-page":"511.e1","DOI":"10.1016\/j.ajog.2022.04.008","article-title":"Natural language processing of admission notes to predict severe maternal morbidity during the delivery encounter","volume":"227","author":"M A Clapp","year":"2022","journal-title":"Am J Obstet Gynecol"},{"issue":"04","key":"ref18","doi-asserted-by":"crossref","first-page":"100386","DOI":"10.1016\/j.xagr.2024.100386","article-title":"Using machine learning to predict the risk of developing hypertensive disorders of pregnancy using a contemporary nulliparous cohort","volume":"4","author":"J S Schor","year":"2024","journal-title":"AJOG Glob Rep"},{"issue":"06","key":"ref19","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1002\/uog.26105","article-title":"Machine-learning-based prediction of pre-eclampsia using first-trimester maternal characteristics and biomarkers","volume":"60","author":"Z Ansbacher-Feldman","year":"2022","journal-title":"Ultrasound Obstet Gynecol"}],"container-title":["American Journal of Perinatology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/a-2781-6377.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T04:50:54Z","timestamp":1785214254000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/a-2781-6377"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,12]]},"references-count":18,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,7,27]]},"published-print":{"date-parts":[[2026,8]]}},"URL":"https:\/\/doi.org\/10.1055\/a-2781-6377","archive":["Portico","CLOCKSS"],"relation":{},"ISSN":["0735-1631","1098-8785"],"issn-type":[{"value":"0735-1631","type":"print"},{"value":"1098-8785","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,1,12]]},"published":{"date-parts":[[2026,1,12]]}}}