ORIGINAL RESEARCH article
Front. Pediatr.
Sec. Obstetric and Pediatric Pharmacology
Volume 13 - 2025 | doi: 10.3389/fped.2025.1655615
Construction of SGA prediction model based on multi-dimensional indicators in the second trimester of pregnancy: Integrating parturient characteristics, serum markers and ultrasound parameters
Provisionally accepted
- The Sixth Affiliated Hospital of Jinan University, Dongguan, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Objective 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. Methods This retrospective study analyzed 546 pregnant women (training set: n=382; validation set: n=164) from February 2022 to December 2024. Maternal baseline data, first-trimester pregnancy-associated plasma protein-A (PAPP-A) and β-human chorionic gonadotropin (β-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). Results 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 β-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 P<0.05). 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): 0.716-0.850) and 0.754 (95% CI: 0.641-0.867) respectively. The optimal thresholds determined based on Youden's index were 0.206 (sensitivity = 0.726, specificity = 0.745) for the training set and 0.227 (sensitivity = 0.747, specificity = 0.714) for the validation set. Conclusion 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.
Keywords: small for gestational age, Prediction model, Maternal baseline characteristics, First-trimester indicators, Second-trimester fetal ultrasound indicators
Received: 28 Jun 2025; Accepted: 09 Sep 2025.
Copyright: © 2025 You, Chen, Gu, Peng and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Wenji Liu, The Sixth Affiliated Hospital of Jinan University, Dongguan, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.