Machine Learning Miscarriage Management Clinical Decision Support Tool Study
NCT ID: NCT06384144
Last Updated: 2024-04-25
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
1000 participants
OBSERVATIONAL
2023-01-01
2026-06-01
Brief Summary
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* Demographics: Maternal Age, Parity
* History: Previous CS, Previous SMM/MVA, Previous Myomectomy
* Gestation by LMP
* Presenting symptoms: Bleeding score, Pain score
* USS Measurements: CRL, GS, RPOC 3 dimensions, Vascularity
* Discrepancy between gestation by CRL and LMP
Audit to collate 1000 cases and identify features contributing to an algorithm that can predict outcome of miscarriage management for individualized case management.
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Detailed Description
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* To determine the reliability of the tool with test data sets
* To increase the sensitivity and specificity of the decision aid by widening the data collection to multiple sites and testing the algorithm with prospective data
The study will be conducted at Queen Charlotte's and Chelsea Hospital at Imperial College Healthcare NHS Trusts (Primary Centre of the study).
This is a multi-centre retrospective, cohort observational study.
The study will be conducted over a minimum of three years to enable sufficient time to go through the retrospective data and collate test data sets.
Retrospective annonymised cases of missed miscarriage and incomplete miscarriage managed at Imperial College Healthcare NHS Trust will be analyse:
For each case the following clinical features will be collated and outcomes:
* Demographics: Maternal Age, Parity
* History: Previous CS, Previous SMM/MVA, Previous Myomectomy
* Gestation by LMP
* Presenting symptoms: Bleeding score, Pain score
* USS Measurements: CRL, GS, RPOC 3 dimensions, Vascularity
* Discrepancy between gestation by CRL and LMP
All data will be collected retrospectively and annonymised.
Following data collection, machine learning models and feature reduction methods will be applied to determine the best performing model to predict success or failure of expectant or medical management of miscarriage respectively.
The next phase will include a prospective audit to collect data and test the predictive power of the MLM clinical decision support tool.
Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Expectant Management of Miscarriage
Cohort that chose to pursue expectant management of miscarriage, final outcome success or failure by day 14 from management choice
Expectant Management of First Trimester Miscarriage
Expectant Management: Conservative management if miscarriage with follow-up booked in 2 weeks to determine whether complete miscarriage has occurred.
Medical Management of Miscarriage
Cohort that chose to pursue medical management of miscarriage, final outcome success or failure by day 14 from management choice
Medical Management of First Trimester Miscarriage
Medical Management: Misoprostol taken to manage first trimester miscarriage, with follow-up booked in 2 weeks to determine whether complete miscarriage has occurred.
Interventions
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Expectant Management of First Trimester Miscarriage
Expectant Management: Conservative management if miscarriage with follow-up booked in 2 weeks to determine whether complete miscarriage has occurred.
Medical Management of First Trimester Miscarriage
Medical Management: Misoprostol taken to manage first trimester miscarriage, with follow-up booked in 2 weeks to determine whether complete miscarriage has occurred.
Eligibility Criteria
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Inclusion Criteria
* Follow-up recorded at 2 weeks
Exclusion Criteria
16 Years
55 Years
FEMALE
Yes
Sponsors
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Imperial College London
OTHER
Responsible Party
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Locations
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Imperial College Heatlhcare NHS Trust
London, , United Kingdom
Countries
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Facility Contacts
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Other Identifiers
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23QC8155
Identifier Type: -
Identifier Source: org_study_id
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