Clinical and anamnestic factors in the prediction and diagnosis of fetal growth restriction
- 作者: Volochaeva M.V.1, Kan N.E.1, Tyutyunnik V.L.1, Amiraslanov E.Y.1, Leonova A.A.1, Soldatova E.E.1
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隶属关系:
- Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation
- 期: 编号 9 (2023)
- 页面: 82-90
- 栏目: Original Articles
- URL: https://journals.eco-vector.com/0300-9092/article/view/622961
- DOI: https://doi.org/10.18565/aig.2023.123
- ID: 622961
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详细
Objective: To develop a model for predicting and diagnosing fetal growth restriction based on clinical and anamnestic factors and examination data during pregnancy.
Materials and methods: Postnatally, the weight and growth parameters of 473 newborns were assessed according to the INTERGROWTH-21 centile curves, which made it possible to form a study group that included 202 pregnant women with fetal growth restriction. The comparison group included 206 women without fetal growth restriction who delivered at terms corresponding to the terms in the study group. Risk factors, parameters of somatic and gynecological anamnesis, features of the course of pregnancy and delivery, ultrasound and Doppler data, and a comprehensive assessment of the health status of newborns were analyzed. After statistical processing of the parameters, binary logistic regression was used to develop a mathematical model for predicting fetal growth restriction.
Results: The prognostic model based on binary logistic regression, including somatic and gynecological diseases and medical history, had a sensitivity of 55.6% and specificity of 82.3%. When physical examination data during pregnancy, including fetal abdominal circumference as measured by ultrasound, were added, the model had a sensitivity of 96.7% and a specificity of 76.8%.
Conclusion: The models developed using the binary logistic regression method can be proposed for use in practical healthcare to identify risk groups and predict and diagnose fetal growth retardation, which will allow for timely prevention to reduce the incidence of perinatal complications.
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作者简介
Maria Volochaeva
Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation
编辑信件的主要联系方式.
Email: volochaeva.m@yandex.ru
ORCID iD: 0000-0001-8953-7952
PhD, Senior Researcher at the Department of Regional Cooperation and Integration, Physician at the 1 Maternity Department
俄罗斯联邦, MoscowNatalia Kan
Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation
Email: kan-med@mail.ru
ORCID iD: 0000-0001-5087-5946
SPIN 代码: 5378-8437
Scopus 作者 ID: 57008835600
Researcher ID: B-2370-2015
Dr. Med. Sci., Professor, Deputy Director for Research
俄罗斯联邦, MoscowVictor Tyutyunnik
Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation
Email: tioutiounnik@mail.ru
ORCID iD: 0000-0002-5830-5099
SPIN 代码: 1963-1359
Scopus 作者 ID: 56190621500
Researcher ID: B-2364-2015
Dr. Med. Sci., Professor, Leading Researcher at the Department of Research Administration
俄罗斯联邦, MoscowElrad Amiraslanov
Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation
Email: e_amiraslanov@oparina4.ru
ORCID iD: 0000-0001-5601-1241
SPIN 代码: 7601-2404
Scopus 作者 ID: 57009098500
PhD, Head of the Department of Obstetrics
俄罗斯联邦, MoscowAnastasia Leonova
Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation
Email: nastena27-03@mail.ru
ORCID iD: 0000-0001-6707-3464
Postgraduate Student
俄罗斯联邦, MoscowEkaterina Soldatova
Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation
Email: katerina.soldatova95@bk.ru
ORCID iD: 0000-0001-6463-3403
Postgraduate Student
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