Fetal growth prediction: Establishing fetal growth prediction curves in the second trimester.


Journal

Technology and health care : official journal of the European Society for Engineering and Medicine
ISSN: 1878-7401
Titre abrégé: Technol Health Care
Pays: Netherlands
ID NLM: 9314590

Informations de publication

Date de publication:
2021
Historique:
pubmed: 9 3 2021
medline: 18 9 2021
entrez: 8 3 2021
Statut: ppublish

Résumé

Monitoring fetal weight during pregnancy has a guiding role in prenatal care. To establish a personalized fetal growth curve for effectively monitoring fetal growth during pregnancy. (1) This study retrospectively analyzed the birth weight database of 2,474 singleton newborns delivered normally at term. The personalized fetal growth curve model was formed by combining the estimating birth weight of newborns with the proportional weight formula. (2) Multiple linear stepwise regression method was used to estimate the birth weight of newborns. (1) Delivery gestational age, weight at first visit, maternal height, pre-pregnancy body mass index, fetal sex, parity had significant effects on birth weight. Based on these parameters, the formula for calculating term optimal weight was obtained (R2= 22.8%, P< 0.001). (2) The personalized fetal growth curve was obtained according to the epidemiological factors input model of each pregnant woman. A model of personalized fetal growth curve can be established, and be used to evaluate fetal growth and development through estimated fetal weight monitoring.

Sections du résumé

BACKGROUND BACKGROUND
Monitoring fetal weight during pregnancy has a guiding role in prenatal care.
OBJECTIVE OBJECTIVE
To establish a personalized fetal growth curve for effectively monitoring fetal growth during pregnancy.
METHODS METHODS
(1) This study retrospectively analyzed the birth weight database of 2,474 singleton newborns delivered normally at term. The personalized fetal growth curve model was formed by combining the estimating birth weight of newborns with the proportional weight formula. (2) Multiple linear stepwise regression method was used to estimate the birth weight of newborns.
RESULTS RESULTS
(1) Delivery gestational age, weight at first visit, maternal height, pre-pregnancy body mass index, fetal sex, parity had significant effects on birth weight. Based on these parameters, the formula for calculating term optimal weight was obtained (R2= 22.8%, P< 0.001). (2) The personalized fetal growth curve was obtained according to the epidemiological factors input model of each pregnant woman.
CONCLUSIONS CONCLUSIONS
A model of personalized fetal growth curve can be established, and be used to evaluate fetal growth and development through estimated fetal weight monitoring.

Identifiants

pubmed: 33682771
pii: THC218032
doi: 10.3233/THC-218032
pmc: PMC8150552
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

345-350

Références

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Auteurs

Yan Wang (Y)

Peking University People's Hospital, Beijing 100044, China.

Xinyu Bao (X)

Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China.
Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.

Song Zhang (S)

Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China.
Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.

Lin Yang (L)

Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China.
Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.

Guoli Liu (G)

Peking University People's Hospital, Beijing 100044, China.

Yimin Yang (Y)

Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China.
Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.

Xuwen Li (X)

Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China.
Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.

Dongmei Hao (D)

Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China.
Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.

Aiqing Chen (A)

Beijing Yes Medical Devices Co. Ltd., Beijing 100152, China.

Xiaohong Liu (X)

Beijing Yes Medical Devices Co. Ltd., Beijing 100152, China.

Jing Shao (J)

Beijing Yes Medical Devices Co. Ltd., Beijing 100152, China.

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Classifications MeSH