
Risk factors for perinatal loss - reality or fiction?
Author(s) -
V. F. Bezhenar,
Л. А. Иванова,
Н. А. Татарова,
Mikhail Yu. Korshunov
Publication year - 2021
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.2021.185
Subject(s) - medicine , anamnesis , pregnancy , obstetrics , advanced maternal age , childbirth , medical record , obstetrics and gynaecology , shoulder dystocia , episiotomy , pediatrics , gynecology , fetus , surgery , genetics , biology
Aim : to identify factors predisposing to perinatal losses, assessment of which is available at the first (only) visit of pregnant woman in antenatal clinic. Materials and Methods . A retrospective analysis of the medical records of 964 women who performed delivery in 2009-2019 in 15 obstetric facilities was carried out. The patients were divided into 2 groups: the main group included 457 women with perinatal losses (stillbirth - 328 children, and 129 children with early neonatal death); the control group consisted of 507 women, whose children survived 7 days during postnatal period. We analyzed parameters routinely determined by an obstetrician-gynecologist at the first visit of woman during within ongoing pregnancy (regardless of gestation age), namely, social status, anamnesis, data of initial examination. Results . The following significant differences were revealed in pregnant women from the main group: a younger age of pregnancy; no registered marriage and permanent job as well as primary and secondary education; smoking, alcohol and drug use; concurrent diabetes mellitus, hypertensive disorders, blood contact infections, obesity; older menarche age and younger sexual debut age; medical history contains infectious genital pathology, more often pregnancies and childbirths, two or more abortions before repeated childbirth, premature births. Conclusion . Thus, the anamnestic indicators noted above can be used to create prognostic statistical systems and models to determine high risk of perinatal losses of any nature.