{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:50:23Z","timestamp":1759333823251,"version":"build-2065373602"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T00:00:00Z","timestamp":1759190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pediatr."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>To develop and validate a prediction model integrating first-trimester maternal characteristics, serum markers, and second-trimester fetal ultrasound parameters for small-for-gestational-age (SGA) infants.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This retrospective study analyzed 546 pregnant women (training set: <jats:italic>n<\/jats:italic>\u2009=\u2009382; validation set: <jats:italic>n<\/jats:italic>\u2009=\u2009164) from February 2022 to December 2024. Maternal baseline data, first-trimester pregnancy-associated plasma protein-A (PAPP-A) and <jats:italic>\u03b2<\/jats:italic>-human chorionic gonadotropin (<jats:italic>\u03b2<\/jats:italic>-hCG) levels, and second-trimester ultrasound indicators were collected. Multivariate logistic regression identified independent predictors, and a nomogram was constructed. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The incidence of SGA was 18.85% (72\/382) in the training set and 19.51% (32\/164) in the validation set. Multivariate logistic regression showed that maternal age, levels of PAPP-A and <jats:italic>\u03b2<\/jats:italic>-hCG in the first trimester, fetal abdominal circumference, femur length, and umbilical artery PI in the second trimester were independent risk factors for SGA (all <jats:italic>P<\/jats:italic>\u2009&amp;lt;\u20090.05). The nomogram model showed good calibration and predictive efficacy in both the training set and the validation set. The C-index reached 0.783 and 0.754 respectively. The areas under the ROC curve (AUC) 0.783 [95% confidence interval (CI): 0.716\u20130.850] and 0.754 (95% CI: 0.641\u20130.867) respectively. The optimal thresholds determined based on Youden's index were 0.206 (sensitivity\u2009=\u20090.726, specificity\u2009=\u20090.745) for the training set and 0.227 (sensitivity\u2009=\u20090.747, specificity\u2009=\u20090.714) for the validation set.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The nomogram prediction model constructed with these combined indicators is helpful for evaluating the risk of SGA. However, further verification through large-sample and multi-center studies is still needed to provide a reference for early clinical intervention.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fped.2025.1655615","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T04:11:43Z","timestamp":1759205503000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Construction of SGA prediction model based on multi-dimensional indicators in the second trimester of pregnancy: integrating parturient characteristics, serum markers and ultrasound 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