Clinical and anamnestic factors in the prediction and diagnosis of fetal growth restriction

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Abstract

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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About the authors

Maria V. Volochaeva

Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russian Federation

Author for correspondence.
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

Russian Federation, Moscow

Natalia E. 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-code: 5378-8437
Scopus Author ID: 57008835600
ResearcherId: B-2370-2015

Dr. Med. Sci., Professor, Deputy Director for Research

Russian Federation, Moscow

Victor L. 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-code: 1963-1359
Scopus Author ID: 56190621500
ResearcherId: B-2364-2015

Dr. Med. Sci., Professor, Leading Researcher at the Department of Research Administration

Russian Federation, Moscow

Elrad Yu. 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-code: 7601-2404
Scopus Author ID: 57009098500

PhD, Head of the Department of Obstetrics

Russian Federation, Moscow

Anastasia A. 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

Russian Federation, Moscow

Ekaterina E. 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

Russian Federation, Moscow

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Supplementary files

Supplementary Files
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1. JATS XML
2. Fig. 1. ROC curve characterizing the dependence of the probability of fetal growth retardation on the value of the logistic function P

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3. Fig. 2. ROC curve characterizing the dependence of fetal growth retardation prediction on the value of the logistic function P

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