{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T20:29:37Z","timestamp":1793132977906,"version":"4.5.0"},"reference-count":35,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2020,12,9]],"date-time":"2020-12-09T00:00:00Z","timestamp":1607472000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2020,12,9]],"date-time":"2020-12-09T00:00:00Z","timestamp":1607472000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003123","name":"Fetal Medicine Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003123","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":[[2021,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop a new competing\u2010risks model for the prediction of a small\u2010for\u2010gestational\u2010age (SGA) neonate, based on maternal factors and biophysical and biochemical markers at 11\u201313\u2009weeks' gestation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This was a prospective observational study in 60\u2009875 women with singleton pregnancy undergoing routine ultrasound examination at 11\u2009+\u20090 to 13\u2009+\u20096\u2009weeks' gestation. All pregnancies had pregnancy\u2010associated plasma protein\u2010A and placental growth factor (PlGF) measurements, 59\u2009001 had uterine artery pulsatility index (UtA\u2010PI) measurements and 58\u2009479 had mean arterial pressure measurements; 57\u2009131 cases had complete data for all biomarkers. We used a previously developed competing\u2010risks model for the joint distribution of gestational age (GA) at delivery and birth\u2010weight\n                      <jats:italic>Z<\/jats:italic>\n                      \u2010score, according to maternal demographic characteristics and medical history. The likelihoods of the biophysical markers were developed by fitting folded\u2010plane regression models, a technique that has already been used in previous studies for the likelihoods of biochemical markers. The next step was to modify the prior distribution by the likelihood, according to Bayes' theorem, to obtain individualized distributions for GA at delivery and birth\u2010weight\n                      <jats:italic>Z<\/jats:italic>\n                      \u2010score. We used the 57\u2009131 cases with complete data to assess the discrimination and calibration of the model for predicting SGA with, without or independently of pre\u2010eclampsia, by different combinations of maternal factors and biomarkers.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The distribution of biomarkers, conditional to both GA at delivery and birth\u2010weight\n                      <jats:italic>Z<\/jats:italic>\n                      \u2010score, was best described by folded\u2010plane regression models. These continuous two\u2010dimensional likelihoods update the joint distribution of birth\u2010weight\n                      <jats:italic>Z<\/jats:italic>\n                      \u2010score and GA at delivery that has resulted from a competing\u2010risks approach; this method allows application of user\u2010defined cut\u2010offs. The best biophysical predictor of preterm SGA was UtA\u2010PI and the best biochemical marker was PlGF. The prediction of SGA was consistently better for increasing degree of prematurity, greater severity of smallness, coexistence of PE and increasing number of biomarkers. The combination of maternal factors with all biomarkers predicted 34.3%, 48.6% and 59.1% of all cases of a SGA neonate with birth weight &lt;\u200910\n                      <jats:sup>th<\/jats:sup>\n                      percentile delivered at \u2265\u200937, &lt;\u200937 and &lt;\u200932\u2009weeks' gestation, at a 10% false\u2010positive rate. The respective values for birth weight &lt;\u20093\n                      <jats:sup>rd<\/jats:sup>\n                      percentile were 39.9%, 53.2% and 64.4%, and for birth weight &lt;\u20093\n                      <jats:sup>rd<\/jats:sup>\n                      percentile with pre\u2010eclampsia they were 46.3%, 66.8% and 80.4%. The new model was well calibrated.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study has presented a single continuous two\u2010dimensional model for prediction of SGA for any desired cut\u2010offs of smallness and GA at delivery, laying the ground for a personalized antenatal plan for predicting and managing SGA, in the milieu of a new inverted pyramid of prenatal care. \u00a9 2020 International Society of Ultrasound in Obstetrics and Gynecology<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/uog.23523","type":"journal-article","created":{"date-parts":[[2020,10,23]],"date-time":"2020-10-23T05:58:47Z","timestamp":1603432727000},"page":"52-61","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Competing\u2010risks model for prediction of small\u2010for\u2010gestational\u2010age neonate from biophysical and biochemical markers at 11\u201313 weeks' gestation"],"prefix":"10.1002","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7969-1435","authenticated-orcid":false,"given":"I.","family":"Papastefanou","sequence":"first","affiliation":[{"name":"Fetal Medicine Research Institute, King's College Hospital  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D.","family":"Wright","sequence":"additional","affiliation":[{"name":"Institute of Health Research University of Exeter  Exeter UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5856-6072","authenticated-orcid":false,"given":"A.","family":"Syngelaki","sequence":"additional","affiliation":[{"name":"Fetal Medicine Research Institute, King's College Hospital  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K.","family":"Souretis","sequence":"additional","affiliation":[{"name":"Fetal Medicine Research Institute, King's College Hospital  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"E.","family":"Chrysanthopoulou","sequence":"additional","affiliation":[{"name":"Fetal Medicine Research Institute, King's College Hospital  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. 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