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Activity Modeling in Birth Room

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details. Identifier: NCT03965026
Recruitment Status : Completed
First Posted : May 28, 2019
Last Update Posted : May 28, 2019
Information provided by (Responsible Party):
Groupe Hospitalier Paris Saint Joseph

Brief Summary:

At this time, two methods exist to calculate a pregnant woman's presumed delivery date (DPA) : one adds 280 days to last menstruation date (Naegele rule), other estimates early pregnancy's date by imagery and adds 270 days. Unless pathology requires a trigger, this DPA estimated a early pregnancy is not re-estimated. These methods are simple and arbitrary : Mongelli and al. in 1996 found that out of nearly 40 000 unique pregnancies, only 4% give birth at determined DPA by echography and 70% at more or less 5 days. Jukic and al. in 2013 they estimate a natural variation of 37 days between pregnancy durations. Face of these poor performances, the calculating DPA method seems to be open to improvement.

Thus, the DPA calculation formula does not take into account the individual patients characteristics (age, occupation, antecedents ...), nor the follow-up data collected during pregnancy. Jukic and al. in 2013 propose a first model with some individual characteristics and medical measures (period between ovulation and early pregnancy, hormone peak) to refine the estimation. Their study gives promising results but their small patients number (a hundred) does not allow them to detect all interactions. Moreover, their method calculation is not dynamic, i.e it does not refine the DPA as pregnancy progresses. To our knowledge, no studies developing an evolutionary model over time for the DPA exist. However, objectives of a more accurate estimate of expected date are multiple and important. The investigators will mention here the two main ones :

  • A better understanding of mecanisms leading to early labour or abnormally long gestation in order to anticipate patients at risk
  • A better material and human needs anticipation, allowing a more efficient organization more adapted to activity and a care of each parturient in optimal conditions.

Our study will focus on predictive model elaboration of pregnancy duration that will evolve as the pregnancy progresses and new data collected. The investigators are considering a machine learning methodology by patient's medical record computerization at the Groupe Hospitalier Paris Saint-Joseph (GHPSJ) since early 2016. Thus, for patients who gave birth from end of 2016, the investigators have a large amount of information on their pregnancy and follow-up on hospital servers, which motivates an automatic approach based on massive data analysis.

This study thus intends to implement advanced techniques in Machine Learning (Online Learning, Support Vector Machine ...) to advance a powerful calculation model.

Condition or disease

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Study Type : Observational
Actual Enrollment : 5100 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Activity Modeling in Birth Room
Actual Study Start Date : June 22, 2018
Actual Primary Completion Date : September 30, 2018
Actual Study Completion Date : December 22, 2018

Primary Outcome Measures :
  1. Anticipate deliveries number 48 hours in advance [ Time Frame: Day 0 ]

    Number of anticipate deliveries -H48 Number of deliveries at day 0

    So the investigators reported the mean difference between expected and actual delivery date for included patients.

Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   Female
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Patient who gave birth at GHPSJ maternity between 01/01/2017 and 02/28/2018.

Inclusion Criteria:

  • Patient whose age ≥ 18 years old
  • Patient who gave birth at GHPSJ maternity between 01/01/2017 and 02/28/2018

Exclusion Criteria:

  • Patient who expressed her opposition to participate in the study
  • Patient under guardianship or curatorship (unless consent is provided)
  • Patient who gave birth at less than 32 weeks amenorrhea
  • Pregnancy marked by MFIU (fetal death in utero)

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its identifier (NCT number): NCT03965026

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Groupe Hospitalier Paris Saint Joseph
Paris, France
Sponsors and Collaborators
Groupe Hospitalier Paris Saint Joseph
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Principal Investigator: Elie AZRIA, Professor Groupe Hospitalier Paris Saint Joseph
Publications of Results:
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Responsible Party: Groupe Hospitalier Paris Saint Joseph Identifier: NCT03965026    
Other Study ID Numbers: MODELSAN
First Posted: May 28, 2019    Key Record Dates
Last Update Posted: May 28, 2019
Last Verified: May 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Yes

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No