Induction of Labor Affected Fetus / NewbornIOL-IDNCT04350437
Induction Of Labor: Predictors of Outcomes
A study testing induction of labor in people with Induction of Labor Affected Fetus / Newborn, run by Assiut University.
Trial summary
Induction of labor is a widely used intervention in OBGYN practice. Doctors still use the old Bishop score in patients' follow up. It remains difficult to anticipate the outcomes and the possibility of adverse effects during this process. Read more about this study
- Who is it for?
- Ages 18 to 40 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Induction of Labor Affected Fetus / Newborn
- What is being tested?
- induction of labor
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 3,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Jul 2020to 30 Jul 2021
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 40 years
- Women only
- Healthy volunteers accepted
- Adult
Who may be able to join
- Pregnant women admitted for IOL, aged between 18 to 40 years
- Term or late preterm pregnancy (gestational age at 34 weeks or beyond)
- Reassuring fetal heart tracing prior to IOL
Who cannot join
- Fetal growth restriction with abnormal Doppler indices
- Intrauterine fetal death
- Suspected intra-amniotic infection prior to IOL
- Fetal major congenital anomalies
- Patients who decline IOL in priori or during IOL without medical indication
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Outcomes of Induction of Labor: a Prospective Multi-center Study
Induction of labor is a widely used intervention in OBGYN practice. Doctors still use the old Bishop score in patients' follow up. It remains difficult to anticipate the outcomes and the possibility of adverse effects during this process. In this large prospective multicentric interventional study, we aim to develop a more precise and sensitive score based on machine learning tools programmed on python 3.8
This new tool will account for many variables in patient demography(age, race, weight ... etc ) and medical history (previous OBGYN surgery, comorbidities .... etc). These variables not usually found in the classic bishop score. We predict that our analysis will aid doctors in making better decisions and efficiently predict the outcomes, need for switching to operative delivery and possible complications.
Read more
Machine learning and digital calculation of hazards will allow more precise assessment and more efficient management during IOL as it considers variables not included in clinical scores.
this study aims to provide modern and efficient assessment parameters to guide clinical decision making during the IOL process and help doctors predict its outcomes based on subtle factors not usually considered.
This will minimize the complications and allow more evidence-based practice.
Read the detailed description
the objective is to create a database registry documenting the induction of labor (IOL) process and apply machine learning tools to create a more precise assessment score for doctors as a contemporary follow-up method.
we will collect data from at least 12 centers worldwide describing the course, outcomes, maternal or fetal complications, and any related data. The data will be collected after ethical approval and from consenting patients in a prospective manner. during the period from July 1st, 2020 to June 30th, 2021 (anticipated dates).
each center will be responsible for quality assessment, data collection, and ensuring the data is accurate, complete, and representative.
Data collection includes baseline pelvic examination (cervical position, consistency, dilation, effacement, fetal position, and bishop score), method of induction and their time of administration in relation to index time (start of IOL), findings and time of serial pelvic examinations, fetal heart tone, and maternal vital signs. The entry of data from serial examinations will continue during active labor and fetal and maternal outcomes will be reported. If the diagnosis of failed IOL is made and obstetric team decides delivery by Cesarean section, criteria of diagnosis/indication of Cesarean delivery will be reported. Length of active labor and the second stage will be documented, and maternal/perinatal complications will be reported. the collectors must ensure patient confidentiality and safety.
Inclusion criteria:-
- Pregnant women admitted for IOL, aged between 18 to 40 years
- Term or late preterm pregnancy (gestational age at 34 weeks or beyond)
- A reassuring fetal heart tracing prior to IOL
Exclusion criteria:-
- Fetal growth restriction with abnormal Doppler indices
- Intrauterine fetal death
- Suspected intra-amniotic infection prior to IOL
- Fetal major congenital anomalies
- Patients who decline IOL in prior or during IOL without medical indication statistical analysis :- Data will be described using (mean, median, standard deviation, range) in the final sample. Machine learning method is superior to traditional statistical methods as it provides robust and automatic estimation of complex relationships between different variables and clinical outcomes. Data will be utilized as xi and yi where xi presents input (features) and yi presents dependent variables (outcomes). Functional regression is based on support vector machine by regressing the outcomes yi on inputs xi. Model Validation will be performed via bootstrap estimation to evaluate the predictive ability of the functional regression models. Data will be split to training data (approximately 63% of the data) to create prediction model where bootstrapping will be applied, and testing data where prediction model will be validated. Machine learning models will be created using python 3.8.
- Being tested
- induction of labor
- Keywords
- labor induction, assessment score
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
induction of laborDrug
Also called non operative vaginal delivery
Giving drugs to facilitate uterine contractions and fasten the process of delivery
| Study group | Role | What they receive |
|---|---|---|
| induction of labor monitoring | Other | meticulous data collection from patients and plotting that data in a machine learning model Receives: Drug: induction of labor |
What is being measured
The primary outcome is the main question the study is designed to answer.
Cesarean section rate
Incidence and indication of Cesarean section following induction of labor
Measured: Within 24 hours from start of induction of labor
Secondary outcomes (4)
Suspected intraamniotic infection
Maternal pyrexia > 39 or > 38 on 2 occasions
Measured: From start of induction of labor to 24 hours after delivery
Postpartum hemorrhage
Blood loss > 1000 ml after delivery
Measured: From start of induction of labor to 24 hours after delivery
Low neonatal APGAR Score
APGAR score < 7 at 5 minutes postpartum
Measured: 5 minutes after delivery
Admission to neonatal intensive care unit
Admission of the newborn to intensive care unit and its indication
Measured: Within 1 hour of delivery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Apr 2020
- Study start1 Jul 2020 (estimated)
- Primary completion30 Jun 2021 (estimated)
- Study completion30 Jul 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Induction of Labor Affected Fetus / NewbornStudies of the same condition, open and under way.
- More trials run by Assiut UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Model description
- Detailed data from patients undergoing induction of labor. Analysis of the data will predict the outcomes in regards to possible complications.
- Masking
- None (open label)
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Assiut University
- Sponsor type
- University, hospital or other
- Collaborators
- Aswan University, Middle-East OBGYN Graduate Education Foundation
- Responsible party
- Principal investigator, Sherif Abdelkarim Mohammed Shazly, Assiut University
- Investigators
- Sherif Shazly, M.S, Principal investigator, Assiut University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04350437
- Sponsor's protocol ID
- IOL-ID
- Acronym
- IOL-ID
- Status last verified
- Apr 2020
- First submitted
- 11 Apr 2020
- Last updated
- 17 Apr 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Martin JA, Hamilton BE, Ventura SJ, Osterman MJ, Mathews TJ. Births: final data for 2011. Natl Vital Stat Rep. 2013 Jun 28;62(1):1-69, 72.
PubMed 24974591Grobman WA, Bailit J, Lai Y, Reddy UM, Wapner RJ, Varner MW, Thorp JM Jr, Leveno KJ, Caritis SN, Prasad M, Tita ATN, Saade G, Sorokin Y, Rouse DJ, Blackwell SC, Tolosa JE; Eunice Kennedy Shriver National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network. Defining failed induction of labor. Am J Obstet Gynecol. 2018 Jan;218(1):122.e1-122.e8. doi: 10.1016/j.ajog.2017.11.556. Epub 2017 Nov 11.
PubMed 29138035Teixeira C, Lunet N, Rodrigues T, Barros H. The Bishop Score as a determinant of labour induction success: a systematic review and meta-analysis. Arch Gynecol Obstet. 2012 Sep;286(3):739-53. doi: 10.1007/s00404-012-2341-3. Epub 2012 May 1.
PubMed 22546948Khandelwal R, Patel P, Pitre D, Sheth T, Maitra N. Comparison of Cervical Length Measured by Transvaginal Ultrasonography and Bishop Score in Predicting Response to Labor Induction. J Obstet Gynaecol India. 2018 Feb;68(1):51-57. doi: 10.1007/s13224-017-1027-y. Epub 2017 Jun 23.
PubMed 29391676
Study at a glance
From the registry record, last updated by the sponsor on 17 Apr 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Outcomes of Induction of Labor: a Prospective Multi-center Study
- ClinicalTrials.gov ID
- NCT04350437
- Acronym / sponsor ID
- IOL-ID / IOL-ID
- Keywords
- labor induction, assessment score
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Assiut UniversityUniversity, hospital or other
- Collaborators
- Aswan University, Middle-East OBGYN Graduate Education Foundation
- Study start
- 1 Jul 2020 (estimated)
- Primary completion
- 30 Jun 2021 (estimated)
- Study completion
- 30 Jul 2021 (estimated)
- First posted
- 17 Apr 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Induction of labor is a widely used intervention in OBGYN practice. Doctors still use the old Bishop score in patients' follow up. It remains difficult to anticipate the outcomes and the possibility of adverse effects during this process. In this large prospective multicentric interventional study, we aim to develop a more precise and sensitive score based on machine learning tools programmed on python 3.8
This new tool will account for many variables in patient demography(age, race, weight ... etc ) and medical history (previous OBGYN surgery, comorbidities .... etc). These variables not usually found in the classic bishop score. We predict that our analysis will aid doctors in making better decisions and efficiently predict the outcomes, need for switching to operative delivery and possible complications.
Machine learning and digital calculation of hazards will allow more precise assessment and more efficient management during IOL as it considers variables not included in clinical scores.
this study aims to provide modern and efficient assessment parameters to guide clinical decision making during the IOL process and help doctors predict its outcomes based on subtle factors not usually considered.
This will minimize the complications and allow more evidence-based practice.
Detailed description
the objective is to create a database registry documenting the induction of labor (IOL) process and apply machine learning tools to create a more precise assessment score for doctors as a contemporary follow-up method.
we will collect data from at least 12 centers worldwide describing the course, outcomes, maternal or fetal complications, and any related data. The data will be collected after ethical approval and from consenting patients in a prospective manner. during the period from July 1st, 2020 to June 30th, 2021 (anticipated dates).
each center will be responsible for quality assessment, data collection, and ensuring the data is accurate, complete, and representative.
Data collection includes baseline pelvic examination (cervical position, consistency, dilation, effacement, fetal position, and bishop score), method of induction and their time of administration in relation to index time (start of IOL), findings and time of serial pelvic examinations, fetal heart tone, and maternal vital signs. The entry of data from serial examinations will continue during active labor and fetal and maternal outcomes will be reported. If the diagnosis of failed IOL is made and obstetric team decides delivery by Cesarean section, criteria of diagnosis/indication of Cesarean delivery will be reported. Length of active labor and the second stage will be documented, and maternal/perinatal complications will be reported. the collectors must ensure patient confidentiality and safety.
Inclusion criteria:-
- Pregnant women admitted for IOL, aged between 18 to 40 years
- Term or late preterm pregnancy (gestational age at 34 weeks or beyond)
- A reassuring fetal heart tracing prior to IOL
Exclusion criteria:-
- Fetal growth restriction with abnormal Doppler indices
- Intrauterine fetal death
- Suspected intra-amniotic infection prior to IOL
- Fetal major congenital anomalies
- Patients who decline IOL in prior or during IOL without medical indication statistical analysis :- Data will be described using (mean, median, standard deviation, range) in the final sample. Machine learning method is superior to traditional statistical methods as it provides robust and automatic estimation of complex relationships between different variables and clinical outcomes. Data will be utilized as xi and yi where xi presents input (features) and yi presents dependent variables (outcomes). Functional regression is based on support vector machine by regressing the outcomes yi on inputs xi. Model Validation will be performed via bootstrap estimation to evaluate the predictive ability of the functional regression models. Data will be split to training data (approximately 63% of the data) to create prediction model where bootstrapping will be applied, and testing data where prediction model will be validated. Machine learning models will be created using python 3.8.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
- Model description
- Detailed data from patients undergoing induction of labor. Analysis of the data will predict the outcomes in regards to possible complications.
| Arm | Type | Receives |
|---|---|---|
| induction of labor monitoring | Other | Drug: induction of labormeticulous data collection from patients and plotting that data in a machine learning model |
Who may qualify
- Ages
- 18 to 40 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult
Key inclusion criteria (3 listed)
- Pregnant women admitted for IOL, aged between 18 to 40 years
- Term or late preterm pregnancy (gestational age at 34 weeks or beyond)
- Reassuring fetal heart tracing prior to IOL
Key exclusion criteria (5 listed)
- Fetal growth restriction with abnormal Doppler indices
- Intrauterine fetal death
- Suspected intra-amniotic infection prior to IOL
- Fetal major congenital anomalies
- Patients who decline IOL in priori or during IOL without medical indication
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| induction of laborAlso: non operative vaginal deliveryGiving drugs to facilitate uterine contractions and fasten the process of delivery | Drug | induction of labor monitoring |
Primary outcome
Cesarean section rate
Time frame: Within 24 hours from start of induction of labor
Incidence and indication of Cesarean section following induction of labor
4 secondary outcomes
Suspected intraamniotic infection
Time frame: From start of induction of labor to 24 hours after delivery
Postpartum hemorrhage
Time frame: From start of induction of labor to 24 hours after delivery
Low neonatal APGAR Score
Time frame: 5 minutes after delivery
Admission to neonatal intensive care unit
Time frame: Within 1 hour of delivery
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Apr 2020.
- Results publication: PubMed 24974591Martin JA, Hamilton BE, Ventura SJ, Osterman MJ, Mathews TJ. Births: final data for 2011. Natl Vital Stat Rep. 2013 Jun 28;62(1):1-69, 72.
- Results publication: PubMed 29138035Grobman WA, Bailit J, Lai Y, Reddy UM, Wapner RJ, Varner MW, Thorp JM Jr, Leveno KJ, Caritis SN, Prasad M, Tita ATN, Saade G, Sorokin Y, Rouse DJ, Blackwell SC, Tolosa JE; Eunice Kennedy Shriver National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network. Defining failed induction of labor. Am J Obstet Gynecol. 2018 Jan;218(1):122.e1-122.e8. doi: 10.1016/j.ajog.2017.11.556. Epub 2017 Nov 11.
- Results publication: PubMed 22546948Teixeira C, Lunet N, Rodrigues T, Barros H. The Bishop Score as a determinant of labour induction success: a systematic review and meta-analysis. Arch Gynecol Obstet. 2012 Sep;286(3):739-53. doi: 10.1007/s00404-012-2341-3. Epub 2012 May 1.
- Results publication: PubMed 29391676Khandelwal R, Patel P, Pitre D, Sheth T, Maitra N. Comparison of Cervical Length Measured by Transvaginal Ultrasonography and Bishop Score in Predicting Response to Labor Induction. J Obstet Gynaecol India. 2018 Feb;68(1):51-57. doi: 10.1007/s13224-017-1027-y. Epub 2017 Jun 23.
- official website for the NGO foundation that the principle investigator created and sponsor the researchLink provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Sherif Shazly, M.S principal investigator
Assiut University
Questions about this trial
What is this clinical trial studying?
A study testing induction of labor in people with Induction of Labor Affected Fetus / Newborn, run by Assiut University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 17 Apr 2020. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Sherif A shazly, M.S (+15075131392, sherif.shazly.mogge@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Cesarean section rate (time frame: Within 24 hours from start of induction of labor). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04350437, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Apr 2020.