New Tools for Predicting Preterm Birth in Asymptomatic High-Risk Women
A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Obstetrics & Gynecology".
Deadline for manuscript submissions: closed (15 September 2021) | Viewed by 10091
Special Issue Editor
2. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
Interests: maternal-fetal medicine; hypertensive disorders of pregnancy; preeclampsia; twins; perinatology; preterm birth
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Preterm birth (PTB) remains one of the biggest challenges in contemporary obstetrics, as it is associated with an increased risk of mortality as well as short- and long-term morbidity of the offspring. Many risk factors for PTB were identified, including multiple gestation, history of PTB, cervical pathology, and uterine factors, among others. As a result, many women are currently classified as being at high risk for PTB, although many of them are asymptomatic prior to delivery. Although extensive research was conducted in this field, the ability of the various available tools to accurately predict PTB remains insufficient. Accurate prediction of PTB is crucial, as it can assist in selecting optimal prevention measurements and counseling regarding the suitable antepartum follow up and the need for potential transfer to the appropriate facility in cases of very high risk of PTB. In contrast, in those found to be at a lower risk, reassurance may relive emotional stress and prevent unnecessary impairment of the daily routine. The present Special Issue aims to deepen the specificity of new tools designed for the prediction of PTB among high-risk women who are asymptomatic. Hopefully, new evidence will increase the knowledge of clinicians and researchers, facilitate collaborations, and improve maternal and neonatal health.
Dr. Liran Hiersch
Guest Editor
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Keywords
- preterm birth
- high-risk pregnancy
- prediction
- prematurity
- tools
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