{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:01:59Z","timestamp":1775581319145,"version":"3.50.1"},"reference-count":32,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2021,3,18]],"date-time":"2021-03-18T00:00:00Z","timestamp":1616025600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["obgyn.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Acta Obstet Gynecol Scand"],"published-print":{"date-parts":[[2021,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>In extremely and very preterm infants, predicting individual risks for adverse outcomes antenatally is challenging but necessary for risk\u2010stratified perinatal management and parents\u2019 participation in decision\u2010making about treatment. Our aim was to develop and validate prediction models for short\u2010term (neonatal period) and medium\u2010term (3\u00a0years of age) outcomes based on antenatal maternal and fetal factors alone.<\/jats:p><\/jats:sec><jats:sec><jats:title>Material and methods<\/jats:title><jats:p>A population\u2010based study was conducted on 31\u00a0157 neonates weighing \u22641500\u00a0g and born between 22 and 31\u00a0weeks of gestation registered in the Neonatal Research Network of Japan during 2006\u20132015. Short\u2010term outcomes were assessed in 31\u00a0157 infants and medium\u2010term outcomes were assessed in 13\u00a0751 infants among the 31\u00a0157 infants. The clinical data were randomly divided into training and validation data sets in a ratio of 2:1. The prediction models were developed by factors selected using stepwise logistic regression from 12 antenatal maternal and fetal factors with the training data set. The number of factors incorporated into the model varied from 3 to 10, on the basis of each outcome. To evaluate predictive performance, the area under the receiver operating characteristics curve (AUROC) was calculated for each outcome with the validation data set.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among short\u2010term outcomes, AUROCs for in\u2010hospital death, chronic lung disease, intraventricular hemorrhage (grade III or IV) and periventricular leukomalacia were 0.85 (95% CI 0.83\u20130.86), 0.80 (95% CI 0.79\u20130.81), 0.78 (95% CI 0.75\u20130.80), and 0.58 (95% CI 0.55\u20130.61), respectively. Among medium\u2010term outcomes, AUROCs for cerebral palsy and developmental quotient of &lt;70 at 3\u00a0years of age were 0.66 (95% CI 0.63\u20130.69) and 0.72 (95% CI 0.70\u20130.74), respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Although the predictive performance of these models varied for each outcome, their discriminative ability for in\u2010hospital death, chronic lung disease, and intraventricular hemorrhage (grade III or IV) was relatively good. We provided a bedside prediction tool for calculating the likelihood of various infant complications for clinical use. To develop these prediction models would be valuable in each country, and these risk assessment tools could facilitate risk\u2010stratified perinatal management and parents\u2019 shared understanding of their infants' subsequent risks.<\/jats:p><\/jats:sec>","DOI":"10.1111\/aogs.14136","type":"journal-article","created":{"date-parts":[[2021,3,3]],"date-time":"2021-03-03T16:57:08Z","timestamp":1614790628000},"page":"1089-1096","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Antenatal prediction models for short\u2010 and medium\u2010term outcomes in preterm infants"],"prefix":"10.1111","volume":"100","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8042-5919","authenticated-orcid":false,"given":"Takafumi","family":"Ushida","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology Nagoya University Graduate School of Medicine Nagoya Japan"},{"name":"Division of Perinatology Center for Maternal\u2010Neonatal Care Nagoya University Hospital Nagoya Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshinori","family":"Moriyama","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Nagoya University Graduate School of Medicine Nagoya Japan"},{"name":"Department of Obstetrics and Gynecology Fujita Health University School of Medicine Toyoake Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1838-4837","authenticated-orcid":false,"given":"Masahiro","family":"Nakatochi","sequence":"additional","affiliation":[{"name":"Division of Public Health Informatics Department of Integrative Health Science Nagoya University Graduate School of Medicine Nagoya Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yumiko","family":"Kobayashi","sequence":"additional","affiliation":[{"name":"Data Science Division Data Coordinating Center Department of Advanced 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