{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T16:36:02Z","timestamp":1792254962404,"version":"4.4.0"},"reference-count":35,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ultrasound in Obstet &amp;amp; Gyne"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>Pre\u2010eclampsia (PE) is a serious complication of pregnancy associated with maternal and fetal morbidity and mortality. As current prediction models have limitations and may not be applicable in resource\u2010limited settings, we aimed to develop a machine\u2010learning (ML) algorithm that offers a potential solution for developing accurate and efficient first\u2010trimester prediction of PE.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We conducted a prospective cohort study in Mexico City, Mexico to develop a first\u2010trimester prediction model for preterm PE (pPE) using ML. Maternal characteristics and locally derived multiples of the median (MoM) values for mean arterial pressure, uterine artery pulsatility index and serum placental growth factor were used for variable selection. The dataset was split into training, validation and test sets. An elastic\u2010net method was employed for predictor selection, and model performance was evaluated using area under the receiver\u2010operating\u2010characteristics curve (AUC) and detection rates (DR) at 10% false\u2010positive rates (FPR).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The final analysis included 3050 pregnant women, of whom 124 (4.07%) developed PE. The ML model showed good performance, with AUCs of 0.897, 0.963 and 0.778 for pPE, early\u2010onset PE (ePE) and any type of PE (all\u2010PE), respectively. The DRs at 10% FPR were 76.5%, 88.2% and 50.1% for pPE, ePE and all\u2010PE, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our ML model demonstrated high accuracy in predicting pPE and ePE using first\u2010trimester maternal characteristics and locally derived MoM. The model may provide an efficient and accessible tool for early prediction of PE, facilitating timely intervention and improved maternal and fetal outcome. \u00a9 2023 The Authors. <jats:italic>Ultrasound in Obstetrics &amp; Gynecology<\/jats:italic> published by John Wiley &amp; Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.<\/jats:p><\/jats:sec>","DOI":"10.1002\/uog.27510","type":"journal-article","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T14:40:10Z","timestamp":1695998410000},"page":"350-357","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Performance of machine\u2010learning approach for prediction of\u00a0pre\u2010eclampsia in a middle\u2010income country"],"prefix":"10.1002","volume":"63","author":[{"given":"J.","family":"Torres\u2010Torres","sequence":"first","affiliation":[{"name":"Clinical Research Branch Instituto Nacional de Perinatolog\u00eda Isidro Espinosa de los Reyes  Mexico City Mexico"},{"name":"Obstetrics and Gynecology Department The American British Cowdray Medical Center  Mexico City Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. R.","family":"Villafan\u2010Bernal","sequence":"additional","affiliation":[{"name":"Laboratory of Immunogenomics and Metabolic Diseases, INMEGEN  Mexico City Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R. J.","family":"Martinez\u2010Portilla","sequence":"additional","affiliation":[{"name":"Clinical Research Branch Instituto Nacional de Perinatolog\u00eda Isidro Espinosa de los Reyes  Mexico City Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. A.","family":"Hidalgo\u2010Carrera","sequence":"additional","affiliation":[{"name":"Obstetrics and Gynecology Department The American British Cowdray Medical Center  Mexico City Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G.","family":"Estrada\u2010Gutierrez","sequence":"additional","affiliation":[{"name":"Clinical Research Branch Instituto Nacional de Perinatolog\u00eda Isidro Espinosa de los Reyes  Mexico City Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Adalid\u2010Martinez\u2010Cisneros","sequence":"additional","affiliation":[{"name":"Servicios de Salud P\u00fablica de la Ciudad de M\u00e9xico  Mexico City Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"L.","family":"Rojas\u2010Zepeda","sequence":"additional","affiliation":[{"name":"Maternal\u2013Fetal Medicine Department Instituto Materno Infantil del Estado de M\u00e9xico  Toluca Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Acevedo\u2010Gallegos","sequence":"additional","affiliation":[{"name":"Clinical Research Branch Instituto Nacional de Perinatolog\u00eda Isidro Espinosa de los Reyes  Mexico City Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D. M.","family":"Camarena\u2010Cabrera","sequence":"additional","affiliation":[{"name":"Clinical Research Branch Instituto Nacional de Perinatolog\u00eda Isidro Espinosa de los Reyes  Mexico City Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. 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