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.
調査の概要
状態
条件
研究の種類
入学 (実際)
連絡先と場所
研究場所
-
-
-
Paris、フランス
- Groupe hospitalier Paris saint Joseph
-
-
参加基準
適格基準
就学可能な年齢
健康ボランティアの受け入れ
受講資格のある性別
サンプリング方法
調査対象母集団
説明
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)
研究計画
研究はどのように設計されていますか?
デザインの詳細
- 観測モデル:コホート
- 時間の展望:回顧
この研究は何を測定していますか?
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
---|---|---|
Anticipate deliveries number 48 hours in advance
時間枠: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. |
Day 0
|
協力者と研究者
捜査官
- 主任研究者:Elie AZRIA, Professor、Groupe hospitalier Paris saint Joseph
出版物と役立つリンク
一般刊行物
- Mongelli M, Wilcox M, Gardosi J. Estimating the date of confinement: ultrasonographic biometry versus certain menstrual dates. Am J Obstet Gynecol. 1996 Jan;174(1 Pt 1):278-81. doi: 10.1016/s0002-9378(96)70408-8.
- Jukic AM, Baird DD, Weinberg CR, McConnaughey DR, Wilcox AJ. Length of human pregnancy and contributors to its natural variation. Hum Reprod. 2013 Oct;28(10):2848-55. doi: 10.1093/humrep/det297. Epub 2013 Aug 6.
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (実際)
研究の完了 (実際)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
その他の研究ID番号
- MODELSAN
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。