{"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":1792254962367,"version":"4.4.0"},"reference-count":26,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,1,2]],"date-time":"2024-01-02T00:00:00Z","timestamp":1704153600000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100007406","name":"Fundaci\u00f3n BBVA","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007406","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004587","name":"Instituto de Salud Carlos III","doi-asserted-by":"publisher","award":["PI18\/01749"],"award-info":[{"award-number":["PI18\/01749"]}],"id":[{"id":"10.13039\/501100004587","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ultrasound in Obstet &amp;amp; Gyne"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Effective first\u2010trimester screening for pre\u2010eclampsia (PE) can be achieved using a competing\u2010risks model that combines risk factors from the maternal history with multiples of the median (MoM) values of biomarkers. A new model using artificial intelligence through machine\u2010learning methods has been shown to achieve similar screening performance without the need for conversion of raw data of biomarkers into MoM. This study aimed to investigate whether this model can be used across populations without specific adaptations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Previously, a machine\u2010learning model derived with the use of a fully connected neural network for first\u2010trimester prediction of early (&lt;\u200934\u2009weeks), preterm (&lt;\u200937\u2009weeks) and all PE was developed and tested in a cohort of pregnant women in the UK. The model was based on maternal risk factors and mean arterial blood pressure (MAP), uterine artery pulsatility index (UtA\u2010PI), placental growth factor (PlGF) and pregnancy\u2010associated plasma protein\u2010A (PAPP\u2010A). In this study, the model was applied to a dataset of 10\u2009110 singleton pregnancies examined in Spain who participated in the first\u2010trimester PE validation (PREVAL) study, in which first\u2010trimester screening for PE was carried out using the Fetal Medicine Foundation (FMF) competing\u2010risks model. The performance of screening was assessed by examining the area under the receiver\u2010operating\u2010characteristics curve (AUC) and detection rate (DR) at a 10% screen\u2010positive rate (SPR). These indices were compared with those derived from the application of the FMF competing\u2010risks model. The performance of screening was poor if no adjustment was made for the analyzer used to measure PlGF, which was different in the UK and Spain. Therefore, adjustment for the analyzer used was performed using simple linear regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The DRs at 10% SPR for early, preterm and all PE with the machine\u2010learning model were 84.4% (95%\u2009CI, 67.2\u201394.7%), 77.8% (95%\u2009CI, 66.4\u201386.7%) and 55.7% (95%\u2009CI, 49.0\u201362.2%), respectively, with the corresponding AUCs of 0.920 (95%\u2009CI, 0.864\u20130.975), 0.913 (95%\u2009CI, 0.882\u20130.944) and 0.846 (95%\u2009CI, 0.820\u20130.872). This performance was achieved with the use of three of the biomarkers (MAP, UtA\u2010PI and PlGF); inclusion of PAPP\u2010A did not provide significant improvement in DR. The machine\u2010learning model had similar performance to that achieved by the FMF competing\u2010risks model (DR at 10% SPR, 82.7% (95%\u2009CI, 69.6\u201395.8%) for early PE, 72.7% (95%\u2009CI, 62.9\u201382.6%) for preterm PE and 55.1% (95%\u2009CI, 48.8\u201361.4%) for all PE) without requiring specific adaptations to the population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A machine\u2010learning model for first\u2010trimester prediction of PE based on a neural network provides effective screening for PE that can be applied in different populations. However, before doing so, it is essential to make adjustments for the analyzer used for biochemical testing. \u00a9 2023 International Society of Ultrasound in Obstetrics and Gynecology.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/uog.27478","type":"journal-article","created":{"date-parts":[[2023,9,12]],"date-time":"2023-09-12T08:30:55Z","timestamp":1694507455000},"page":"68-74","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Validation of machine\u2010learning model for first\u2010trimester prediction of pre\u2010eclampsia using cohort from\n                    <scp>PREVAL<\/scp>\n                    study"],"prefix":"10.1002","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9993-5249","authenticated-orcid":false,"given":"M. M.","family":"Gil","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology Hospital Universitario de Torrej\u00f3n, Torrej\u00f3n de Ardoz  Madrid Spain"},{"name":"Faculty of Medicine Universidad Francisco de Vitoria, Pozuelo de Alarc\u00f3n  Madrid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D.","family":"Cuenca\u2010G\u00f3mez","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Hospital Universitario de Torrej\u00f3n, Torrej\u00f3n de Ardoz  Madrid Spain"},{"name":"Faculty of Medicine Universidad Francisco de Vitoria, Pozuelo de Alarc\u00f3n  Madrid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V.","family":"Rolle","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Hospital Universitario de Torrej\u00f3n, Torrej\u00f3n de Ardoz  Madrid Spain"},{"name":"Clinical Research Unit, Hospital Universitario de Torrej\u00f3n, Torrej\u00f3n de Ardoz  Madrid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Pertegal","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Hospital Cl\u00ednico Universitario \u2018Virgen de la Arrixaca\u2019, El Palmar  Murcia Spain"},{"name":"Institute for Biomedical Research of Murcia IMIB\u2010Arrixaca, El Palmar  Murcia Spain"},{"name":"Faculty of Medicine Universidad de Murcia  Murcia Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C.","family":"D\u00edaz","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Complejo Hospitalario Universitario A Coru\u00f1a  A Coru\u00f1a Galicia Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Revello","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Hospital Universitario Quir\u00f3n, Pozuelo de Alarc\u00f3n  Madrid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B.","family":"Adiego","sequence":"additional","affiliation":[{"name":"Obstetrics and Gynecology Department Hospital Universitario Fundaci\u00f3n Alcorc\u00f3n, Alcorc\u00f3n  Madrid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Mendoza","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Hospital Universitari Vall d'Hebr\u00f3n  Barcelona Catalonia Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F. 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