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Prediction of small-for-gestational-age neonates at 33–39 weeks’ gestation in China: logistic regression modeling of the contributions of second- and third-trimester ultrasound data and maternal factors
OBJECTIVES: A screening model for prediction of small-for-gestational-age (SGA) neonates (SGAp) was established by logistic regression using ultrasound data and maternal factors (MF). We aimed to evaluate the ability of SGAp as well as abdominal circumference (AC) and estimated fetal weight (EFW) me...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9413926/ https://www.ncbi.nlm.nih.gov/pubmed/36008794 http://dx.doi.org/10.1186/s12884-022-04991-7 |
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author | Xu, Danping Shen, Xiuzhen Guan, Heqin Zhu, Yiyang Yan, Minchan Wu, Xiafang |
author_facet | Xu, Danping Shen, Xiuzhen Guan, Heqin Zhu, Yiyang Yan, Minchan Wu, Xiafang |
author_sort | Xu, Danping |
collection | PubMed |
description | OBJECTIVES: A screening model for prediction of small-for-gestational-age (SGA) neonates (SGAp) was established by logistic regression using ultrasound data and maternal factors (MF). We aimed to evaluate the ability of SGAp as well as abdominal circumference (AC) and estimated fetal weight (EFW) measurements to predict SGA neonates at 33–39 weeks’ gestation. METHODS: This retrospective study evaluated 5298 singleton pregnancies that had involved three ultrasound examinations at 21(+0)–27(+6), 28(+0)–32(+6), and 33(+0)–39(+6) weeks. All ultrasound data were transformed to MoM values (multiple of the median). Multivariate logistic regression was used to analyze the correlation between SGA status and various variables (ultrasound data and MF) during pregnancy to build the SGAp model. EFW was calculated according to the Hadlock formula at 33–39 weeks of gestation. The predictive performance of SGAp, AC MoM value at 33(+0)–39(+6) weeks (AC-M), EFW MoM value (EFW-M), EFW-M plus MF, AC value at 33(+0)–39(+6) weeks (AC), AC growth velocity, EFW, and EFW plus MF was evaluated using ROC curves. The detection rate (DR) of SGA neonate with SGAp, AC-M, EFW-M, and EFW-M plus MF at false positive rate (FPR) of 5% and 10%, and the FPR at DR of 85%, 90%, and 95% were observed. RESULTS: The AUCs of SGAp, AC-M, EFW-M, EFW-M plus MF, AC, AC growth velocity, EFW, and EFW plus MF for SGA neonates screening were 0.933 (95%CI: 0.916–0.950), 0.906 (95%CI: 0.887–0.925), 0.920 (95%CI: 0.903–0.936), 0.925 (95%CI: 0.909–0.941), 0.818 (95%CI: 0.791–0.845), 0.786 (95%CI: 0.752–0.821), 0.810 (95%CI: 0.782–0.838), and 0.834 (95%CI: 0.807–0.860), respectively. The screening efficiency of SGAp, AC-M, EFW-M, and EFW-M plus MF are significantly higher than AC, AC growth velocity, EFW, and EFW plus MF. The DR of SGAp, AC-M, EFW-M, and EFW-M plus MF for SGA neonates were 80.4%, 69.6%, 73.8% and 74.3% at 10% FPR. The AUCs of SGAp, AC-M, EFW-M, and EFW-M plus MF 0.950 (95%CI: 0.932–0.967), 0.929 (95%CI: 0.909–0.948), 0.938 (95%CI: 0.921–0.956) and 0.941 (95%CI: 0.924–0.957), respectively for screening SGA neonates delivered within 2 weeks after the assessment. The DR for these births increased to 85.8%, 75.8%, 80.0%, and 82.5%, respectively. CONCLUSION: The rational use of ultrasound data can significantly improve the prediction of SGA statuses. |
format | Online Article Text |
id | pubmed-9413926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-94139262022-08-27 Prediction of small-for-gestational-age neonates at 33–39 weeks’ gestation in China: logistic regression modeling of the contributions of second- and third-trimester ultrasound data and maternal factors Xu, Danping Shen, Xiuzhen Guan, Heqin Zhu, Yiyang Yan, Minchan Wu, Xiafang BMC Pregnancy Childbirth Research OBJECTIVES: A screening model for prediction of small-for-gestational-age (SGA) neonates (SGAp) was established by logistic regression using ultrasound data and maternal factors (MF). We aimed to evaluate the ability of SGAp as well as abdominal circumference (AC) and estimated fetal weight (EFW) measurements to predict SGA neonates at 33–39 weeks’ gestation. METHODS: This retrospective study evaluated 5298 singleton pregnancies that had involved three ultrasound examinations at 21(+0)–27(+6), 28(+0)–32(+6), and 33(+0)–39(+6) weeks. All ultrasound data were transformed to MoM values (multiple of the median). Multivariate logistic regression was used to analyze the correlation between SGA status and various variables (ultrasound data and MF) during pregnancy to build the SGAp model. EFW was calculated according to the Hadlock formula at 33–39 weeks of gestation. The predictive performance of SGAp, AC MoM value at 33(+0)–39(+6) weeks (AC-M), EFW MoM value (EFW-M), EFW-M plus MF, AC value at 33(+0)–39(+6) weeks (AC), AC growth velocity, EFW, and EFW plus MF was evaluated using ROC curves. The detection rate (DR) of SGA neonate with SGAp, AC-M, EFW-M, and EFW-M plus MF at false positive rate (FPR) of 5% and 10%, and the FPR at DR of 85%, 90%, and 95% were observed. RESULTS: The AUCs of SGAp, AC-M, EFW-M, EFW-M plus MF, AC, AC growth velocity, EFW, and EFW plus MF for SGA neonates screening were 0.933 (95%CI: 0.916–0.950), 0.906 (95%CI: 0.887–0.925), 0.920 (95%CI: 0.903–0.936), 0.925 (95%CI: 0.909–0.941), 0.818 (95%CI: 0.791–0.845), 0.786 (95%CI: 0.752–0.821), 0.810 (95%CI: 0.782–0.838), and 0.834 (95%CI: 0.807–0.860), respectively. The screening efficiency of SGAp, AC-M, EFW-M, and EFW-M plus MF are significantly higher than AC, AC growth velocity, EFW, and EFW plus MF. The DR of SGAp, AC-M, EFW-M, and EFW-M plus MF for SGA neonates were 80.4%, 69.6%, 73.8% and 74.3% at 10% FPR. The AUCs of SGAp, AC-M, EFW-M, and EFW-M plus MF 0.950 (95%CI: 0.932–0.967), 0.929 (95%CI: 0.909–0.948), 0.938 (95%CI: 0.921–0.956) and 0.941 (95%CI: 0.924–0.957), respectively for screening SGA neonates delivered within 2 weeks after the assessment. The DR for these births increased to 85.8%, 75.8%, 80.0%, and 82.5%, respectively. CONCLUSION: The rational use of ultrasound data can significantly improve the prediction of SGA statuses. BioMed Central 2022-08-25 /pmc/articles/PMC9413926/ /pubmed/36008794 http://dx.doi.org/10.1186/s12884-022-04991-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Xu, Danping Shen, Xiuzhen Guan, Heqin Zhu, Yiyang Yan, Minchan Wu, Xiafang Prediction of small-for-gestational-age neonates at 33–39 weeks’ gestation in China: logistic regression modeling of the contributions of second- and third-trimester ultrasound data and maternal factors |
title | Prediction of small-for-gestational-age neonates at 33–39 weeks’ gestation in China: logistic regression modeling of the contributions of second- and third-trimester ultrasound data and maternal factors |
title_full | Prediction of small-for-gestational-age neonates at 33–39 weeks’ gestation in China: logistic regression modeling of the contributions of second- and third-trimester ultrasound data and maternal factors |
title_fullStr | Prediction of small-for-gestational-age neonates at 33–39 weeks’ gestation in China: logistic regression modeling of the contributions of second- and third-trimester ultrasound data and maternal factors |
title_full_unstemmed | Prediction of small-for-gestational-age neonates at 33–39 weeks’ gestation in China: logistic regression modeling of the contributions of second- and third-trimester ultrasound data and maternal factors |
title_short | Prediction of small-for-gestational-age neonates at 33–39 weeks’ gestation in China: logistic regression modeling of the contributions of second- and third-trimester ultrasound data and maternal factors |
title_sort | prediction of small-for-gestational-age neonates at 33–39 weeks’ gestation in china: logistic regression modeling of the contributions of second- and third-trimester ultrasound data and maternal factors |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9413926/ https://www.ncbi.nlm.nih.gov/pubmed/36008794 http://dx.doi.org/10.1186/s12884-022-04991-7 |
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