- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT04588597
Obstetric and Neonatal Outcomes of Twin Pregnancy (Twin-CTB)
Obstetric and Neonatal Outcomes of Twin Pregnancy (Twin Conception-To-Birth): A Multicenter International Study
Study Overview
Status
Conditions
Detailed Description
Twin pregnancies carry higher risks of maternal, fetal and neonatal adverse outcomes compared to singleton pregnancy.They are associated with increased perinatal morbidity and mortality, anemia, pregnancy- induced hypertension, increased incidence of cesarean section (CS), postpartum hemorrhage, prematurity and low birth weight and Increased rate of perinatal death.
This multicenter study aims at assessing the natural history of twin pregnancy, and developing a machine learning-based algorithm to predict clinical outcomes of twin pregnancy during pregnancy and delivery and to determine management strategies that are associated with best maternal and neonatal outcomes.
Medical records of eligible women will be reviewed, and data abstraction will be performed using a standardized excel sheet designed for this study. Target data include baseline demographics and clinical data (e.g. age, parity, ethnicity, smoking, IVF pregnancy, history of gynecologic surgeries, type of twin pregnancy, current medical disorders, current obstetric complications, fetal anomalies, administration of antenatal steroids, Placental site, and twin-specific complications). Information from serial ultrasound reports including fetal growth and Doppler studies will be collected and data on fetal intervention will be abstracted. Peripartum data include node of delivery, Method of induction, CS indication, and type of cesarean incision. Clinical outcomes include postpartum hemorrhage, and perinatal death, admission to neonatal intensive care unit (NICU), neonatal need for respiratory support, neonatal intracranial hemorrhage, neonatal respiratory distress syndrome and neonatal hypoxic ischemic encephalopathy. Data will not include any identifiable information.
Prediction model will be created using baseline demographic and obstetric features of pregnancy and individual maternal and perinatal complications will be set as outcomes (dependent variables). A composite outcome of major maternal and neonatal outcomes will be created separately.
Study Type
Enrollment (Anticipated)
Contacts and Locations
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- Women with twin pregnancy who received their antenatal care and were delivered in the participating institute
- Compliance to antenatal care visits
Exclusion Criteria:
- Major fetal anomalies of one or both twins
- Elective miscarriage
- Authorization to use medical records was not provided by the patient
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Postpartum hemorrhage
Time Frame: From delivery of the baby to 24 hours postoperative (Total of 24 hours)
|
Significant uterine bleeding > 1000 ml
|
From delivery of the baby to 24 hours postoperative (Total of 24 hours)
|
|
perinatal death
Time Frame: From onset of labor to 1 week postnatal
|
Death of the baby during delivery or early neonatal period
|
From onset of labor to 1 week postnatal
|
|
Admission to neonatal intensive care unit (NICU)
Time Frame: From delivery of the baby to 24 hours postoperative (Total of 24 hours)
|
Admission of the baby to NICU for observation or intervention
|
From delivery of the baby to 24 hours postoperative (Total of 24 hours)
|
|
Intrauterine fetal death
Time Frame: Death of the fetus any time from 24 weeks to onset of labor
|
Death of the fetus any time from 24 weeks to onset of labor
|
Collaborators and Investigators
Sponsor
Collaborators
Publications and helpful links
General Publications
- Qin JB, Wang H, Sheng X, Xie Q, Gao S. Assisted reproductive technology and risk of adverse obstetric outcomes in dichorionic twin pregnancies: a systematic review and meta-analysis. Fertil Steril. 2016 May;105(5):1180-1192. doi: 10.1016/j.fertnstert.2015.12.131. Epub 2016 Jan 19.
- Huang J, Maguire MG, Ciner E, Kulp MT, Cyert LA, Quinn GE, Orel-Bixler D, Moore B, Ying GS; Vision in Preschoolers (VIP) Study Group. Risk factors for astigmatism in the Vision in Preschoolers Study. Optom Vis Sci. 2014 May;91(5):514-21. doi: 10.1097/OPX.0000000000000242.
- Adinma JI, Agbai AO. Multiple births in Nigerian Igbo women: incidence and outcomes. J Obstet Gynaecol. 1997 Jan;17(1):42-4. doi: 10.1080/01443619750114077.
- Mutihir JT, Pam VC. Obstetric outcome of twin pregnancies in Jos, Nigeria. Niger J Clin Pract. 2007 Mar;10(1):15-8.
- Rzyska E, Ajay B, Chandraharan E. Safety of vaginal delivery among dichorionic diamniotic twins over 10 years in a UK teaching hospital. Int J Gynaecol Obstet. 2017 Jan;136(1):98-101. doi: 10.1002/ijgo.12017. Epub 2016 Nov 3.
- Kong CW, To WWK. The predicting factors and outcomes of caesarean section of the second twin. J Obstet Gynaecol. 2017 Aug;37(6):709-713. doi: 10.1080/01443615.2017.1286466. Epub 2017 Mar 21.
- Kato K, Fujiki K. Multiple births and congenital anomalies in Tokyo Metropolitan Hospitals, 1979-1990. Acta Genet Med Gemellol (Roma). 1992;41(4):253-9. doi: 10.1017/s0001566000002117.
- Rodis JF, McIlveen PF, Egan JF, Borgida AF, Turner GW, Campbell WA. Monoamniotic twins: improved perinatal survival with accurate prenatal diagnosis and antenatal fetal surveillance. Am J Obstet Gynecol. 1997 Nov;177(5):1046-9. doi: 10.1016/s0002-9378(97)70012-7.
- Carroll SG, Soothill PW, Abdel-Fattah SA, Porter H, Montague I, Kyle PM. Prediction of chorionicity in twin pregnancies at 10-14 weeks of gestation. BJOG. 2002 Feb;109(2):182-6. doi: 10.1111/j.1471-0528.2002.01172.x.
- Lee YM, Cleary-Goldman J, Thaker HM, Simpson LL. Antenatal sonographic prediction of twin chorionicity. Am J Obstet Gynecol. 2006 Sep;195(3):863-7. doi: 10.1016/j.ajog.2006.06.039.
- Cao K, Verspoor K, Sahebjada S, Baird PN. Evaluating the Performance of Various Machine Learning Algorithms to Detect Subclinical Keratoconus. Transl Vis Sci Technol. 2020 Apr 24;9(2):24. doi: 10.1167/tvst.9.2.24. eCollection 2020 Apr.
Study record dates
Study Major Dates
Study Start (ANTICIPATED)
Primary Completion (ANTICIPATED)
Study Completion (ANTICIPATED)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (ACTUAL)
Study Record Updates
Last Update Posted (ACTUAL)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- Twin-CTB
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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