Open AccessPredicting intrauterine fetal infection risk in extremely preterm and early preterm births induced by rupture of the amniotic membranes
Author(s) -
Yu.A. Shadeeva,
Valentina Andreyevna Guryeva,
М. Г. Николаева,
N. V. Evtushenko
Publication year - 2020
Publication title -
akušerstvo, ginekologiâ i reprodukciâ
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.124
H-Index - 4
eISSN - 2500-3194
pISSN - 2313-7347
DOI - 10.17749/2313-7347/ob.gyn.rep.2020.110
Subject(s) - medicine , obstetrics , rupture of membranes , pregnancy , premature rupture of membranes , fetus , amniotic fluid , logistic regression , intrauterine device , neonatal infection , population , family planning , environmental health , biology , research methodology , genetics
Aim: developing a prognostic model for intrauterine fetal infection risk in extremely preterm and early preterm births induced by rupture of the amniotic membranes. Materials and Methods. A single-center prospective cohort study was conducted aimed at studying features of pregnancy and childbirth in 160 patients with extremely preterm and early preterm births induced by rupture of the amniotic membranes as well as neonatal period of 160 neonates. Two groups were distinguished: the main group – 37 patients with intrauterine neonatal infection and the comparison group – 123 women without signs of neonatal infection. Along with examination regulated by the order of the Ministry of Health of the Russian Federation No. 572 n, serum level of highly sensitive C-reactive protein, pro-inflammatory cytokines interleukin-6, tumor necrosis factor-α and anti-inflammatory cytokine interleukin-10 were assessed in patients. Risk factors (predictors) of intrauterine infection were assessed by analyzing anamnesis data, echography and laboratory assay data. Results. Multivariate logistic regression analysis allowed us to identify 10 predictors of intrauterine fetal infection based on which a mathematical model was developed that allowed to predict a risk of intrauterine fetal infection in 95.7 % of cases. Conclusion. Using a model for predicting intrauterine infection based on a comprehensive predictor assessment, it may allow to accomplish a personalized approach to justify adequate obstetric care and reduce adverse infection-associated perinatal outcomes.