You are in:Home/Publications/Role of cervical gland area as a predictor of preterm labor

Dr. Samar Ali Mohamed Ali Taha :: Publications:

Title:
Role of cervical gland area as a predictor of preterm labor
Authors: F.Y.Aseel, M.A.Mohamed,I.I.Sewedin and S.A.Mohamed
Year: 2022
Keywords: Not Available
Journal: Not Available
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: Local
Paper Link: Not Available
Full paper Samar Ali Mohamed Ali Taha_article_288872_be8df1e7fdfc0afd0bf5c7eb300824de.pdf
Supplementary materials Not Available
Abstract:

Cervical gland area (CGA) is a relatively recent ultrasonographic measure proposed for the prediction of preterm birth. During an ultrasonography examination, the cervical mucosa is often characterised by a hyperechoic or hypoechoic segment, which may or may not correspond to the presence of mucosal glands in the cervical canal. Preterm birth may be predicted by determining whether or not these glands have developed by the end of the second trimester. As a result, the primary purpose of this research was to assess the validity of transvaginal ultrasonography for predicting cervical gland area and, by extension, premature labour. Methods: Benha University Hospitals' Obstetrics and Gynecology Departments recruited 160 pregnant women for this prospective cohort research. Women who tested positive for CGA during pregnancy were placed in Group A, whereas those who did not were placed in Group B. (pregnant women with absent CGA by TVUS). As a result, 78.1% of individuals had CGA and 21.9% did not. With a sensitivity of 64.6%, a specificity of 96.4%, and an accuracy of 86.9%, the presence of CGA is a strong predictor of premature labour. Because of the requirement for specialised prenatal care, it is crucial to accurately predict preterm and extremely preterm birth (gestational age 32 weeks). It would seem that CGA detection is a useful signal for this. CGA should be studied in conjunction with other markers to see which ones are most useful for predicting the likelihood of a premature birth.

Google ScholarAcdemia.eduResearch GateLinkedinFacebookTwitterGoogle PlusYoutubeWordpressInstagramMendeleyZoteroEvernoteORCIDScopus