Delivery Clinical Trial
Official title:
Activity Modeling in Birth Room
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.
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