Clinical and anamnestic risk factors and prediction models for the development of fetal growth restriction

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Abstract

Objective: To analyze clinical and anamnestic risk factors and develop prediction models for early- and late-onset fetal growth restriction.

Materials and methods: A cross-sectional study was conducted at the V.I. Kulakov NMRC for OG&P of Minzdrav of Russia, involving 382 pregnant women. Group 1 included 110 pregnant women with fetal growth restriction, whereas Group 2 included 272 women with pregnancies without fetal growth restriction. An analysis of the somatic and obstetric-gynecological history and course of the current pregnancy in both groups was performed. Prediction models for the likelihood of developing early- and late-onset fetal growth restriction were developed.

Results: In group 1, a history of arterial hypertension [10/110 (9.1%) vs. 6/272 (2.2%), p=0.045, OR=0.226 (95% CI 0.052–0.979)], chronic pyelonephritis [8/110 (7.3%) vs. 2/272 (0.7%), p=0.025, OR=0.094 (95% CI 0.010–0.865)], chronic endometritis [8/110 (7.3%) vs. 58/272 (21.3%), p=0.02, OR=0.82 (95% CI 0.015–10.353)], and a history of giving birth to children with growth restriction in previous pregnancies [8/110 (7.3%) vs. 2/272 (0.7%), p=0.025, OR=0.094 (95% CI 0.010–0.865)] were significantly more common than in group 2. During the current pregnancy, the following complications were significantly more prevalent in group 1 than in group 2: threatened miscarriage [35/110 (31.8%) vs. 64/272 (23.5%), p=0.04, RR=1.97 (95% CI 1.03–3.75)], preeclampsia [11/110 (10%) vs. 9/272 (3.3%), p=0.01, RR=3.53 (95% CI 1.37–9.08)], chronic arterial hypertension [11/110 (10%) vs. 11/272 (4.0%), p=0.02, RR=2.84 (95% CI 1.15–7.01)], and placenta previa [5/110 (4.5%) vs. 5/272 (1.8%), p=0.01, RR=0.84 (95% CI 0.42–1.68)]. Significant predictors of fetal growth restriction included a history of having a child with growth restriction, placenta previa, obesity, and preeclampsia. Based on the identified predictors, mathematical predictive models were developed to determine the likelihood of fetal growth restriction.

Conclusion: The developed predictive models for early- and late-onset fetal growth restriction can help reduce the incidence of pregnancy complications and improve perinatal outcomes.

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About the authors

Shagane R. Gasymova

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

Author for correspondence.
Email: shagane2501@mail.ru
ORCID iD: 0009-0001-2626-6670

Junior Researcher, Department of Fetal Medicine of the Institute of Obstetrics; Diagnostic Medical Sonographer, Department of Ultrasound and Functional Diagnostics; Obstetrician-Gynecologist

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

Professor, Dr. Med. Sci., Leading Researcher at the Center for Scientific and Clinical Research

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

Professor, Dr. Med. Sci., Deputy Director of Science

Russian Federation, Moscow

Maria V. Volochaeva

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

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 Maternity Department No. 1

Russian Federation, Moscow

Andrey E. Donnikov

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

Email: donnikov@dna-technology.ru
ORCID iD: 0000-0003-3504-2406

PhD, Head of the Laboratory of Molecular Genetic Methods

Russian Federation, Moscow

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Supplementary files

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2. Fig. 1. ROC-curve of dependence of the probability of early form of PRA on the value of the logistic function P

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3. Fig. 2. Sensitivity and specificity analysis of the model depending on the threshold values of the logistic function P

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4. Fig. 3. ROC-curve of dependence of the probability of late form of PRA on the value of the logistic function P

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5. Fig. 4. Sensitivity and specificity analysis of the model depending on the threshold values of the logistic function P

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