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Risk Assessment for Birth Defects in Offspring of Chinese Pregnant Women

Objective: This study aimed to develop a nomogram for the risk assessment of any type of birth defect in offspring using a large birth-defect database in Northwest China. Methods: This study was based on a birth-defect survey, which included 29,204 eligible women who were pregnant between 2010 and 2...

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Autores principales: Qu, Pengfei, Zhao, Doudou, Yan, Mingxin, Liu, Danmeng, Pei, Leilei, Zeng, Lingxia, Yan, Hong, Dang, Shaonong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319985/
https://www.ncbi.nlm.nih.gov/pubmed/35886437
http://dx.doi.org/10.3390/ijerph19148584
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author Qu, Pengfei
Zhao, Doudou
Yan, Mingxin
Liu, Danmeng
Pei, Leilei
Zeng, Lingxia
Yan, Hong
Dang, Shaonong
author_facet Qu, Pengfei
Zhao, Doudou
Yan, Mingxin
Liu, Danmeng
Pei, Leilei
Zeng, Lingxia
Yan, Hong
Dang, Shaonong
author_sort Qu, Pengfei
collection PubMed
description Objective: This study aimed to develop a nomogram for the risk assessment of any type of birth defect in offspring using a large birth-defect database in Northwest China. Methods: This study was based on a birth-defect survey, which included 29,204 eligible women who were pregnant between 2010 and 2013 in the Shaanxi province of Northwest China. The participants from central Shaanxi province were assigned to the training group, while the subjects from the south and north of Shaanxi province were assigned to the external validation group. The primary outcome was the occurrence of any type of birth defect in the offspring. A multivariate logistic regression model was used to establish a prediction nomogram, while the discrimination and calibration were evaluated by external validation. Results: The multivariate analyses revealed that household registration, history of miscarriages, family history of birth defects, infection, taking medicine, pesticide exposure, folic acid supplementation, and single/twin pregnancy were significant factors in the occurrence of birth defects. The area under the receiver operating characteristic curve (AUC) in the prediction model was 0.682 (95% CI 0.653 to 0.710) in the training set. The validation set showed moderate discrimination, with an AUC of 0.651 (95% CI 0.614 to 0.689). Additionally, the prediction model had a good calibration (HL χ(2) = 8.106, p= 0.323). Conclusions: We developed a nomogram risk model for any type of birth defect in a Chinese population based on important modifying factors in pregnant women. This risk-prediction model could be a tool for clinicians to assess the risk of birth defects and promote health education.
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spelling pubmed-93199852022-07-27 Risk Assessment for Birth Defects in Offspring of Chinese Pregnant Women Qu, Pengfei Zhao, Doudou Yan, Mingxin Liu, Danmeng Pei, Leilei Zeng, Lingxia Yan, Hong Dang, Shaonong Int J Environ Res Public Health Article Objective: This study aimed to develop a nomogram for the risk assessment of any type of birth defect in offspring using a large birth-defect database in Northwest China. Methods: This study was based on a birth-defect survey, which included 29,204 eligible women who were pregnant between 2010 and 2013 in the Shaanxi province of Northwest China. The participants from central Shaanxi province were assigned to the training group, while the subjects from the south and north of Shaanxi province were assigned to the external validation group. The primary outcome was the occurrence of any type of birth defect in the offspring. A multivariate logistic regression model was used to establish a prediction nomogram, while the discrimination and calibration were evaluated by external validation. Results: The multivariate analyses revealed that household registration, history of miscarriages, family history of birth defects, infection, taking medicine, pesticide exposure, folic acid supplementation, and single/twin pregnancy were significant factors in the occurrence of birth defects. The area under the receiver operating characteristic curve (AUC) in the prediction model was 0.682 (95% CI 0.653 to 0.710) in the training set. The validation set showed moderate discrimination, with an AUC of 0.651 (95% CI 0.614 to 0.689). Additionally, the prediction model had a good calibration (HL χ(2) = 8.106, p= 0.323). Conclusions: We developed a nomogram risk model for any type of birth defect in a Chinese population based on important modifying factors in pregnant women. This risk-prediction model could be a tool for clinicians to assess the risk of birth defects and promote health education. MDPI 2022-07-14 /pmc/articles/PMC9319985/ /pubmed/35886437 http://dx.doi.org/10.3390/ijerph19148584 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Qu, Pengfei
Zhao, Doudou
Yan, Mingxin
Liu, Danmeng
Pei, Leilei
Zeng, Lingxia
Yan, Hong
Dang, Shaonong
Risk Assessment for Birth Defects in Offspring of Chinese Pregnant Women
title Risk Assessment for Birth Defects in Offspring of Chinese Pregnant Women
title_full Risk Assessment for Birth Defects in Offspring of Chinese Pregnant Women
title_fullStr Risk Assessment for Birth Defects in Offspring of Chinese Pregnant Women
title_full_unstemmed Risk Assessment for Birth Defects in Offspring of Chinese Pregnant Women
title_short Risk Assessment for Birth Defects in Offspring of Chinese Pregnant Women
title_sort risk assessment for birth defects in offspring of chinese pregnant women
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9319985/
https://www.ncbi.nlm.nih.gov/pubmed/35886437
http://dx.doi.org/10.3390/ijerph19148584
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