Machine Learning Miscarriage Management Clinical Decision Support Tool Study (MLMM)
Machine learning used to develop an algorithm to determine chance of success with expectant or medical management for an individual patient. Taking into account the following objective measures:
- 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.
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
- Artificial intelligence discovery science: Algorithm Development based on a retrospective Audit of approximately 1000 cases of miscarriage
- 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.
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Locations
-
-
-
London, United Kingdom, W12 0HS
- Recruiting
- Imperial College Heatlhcare NHS Trust
-
Contact:
- Sughashini Murugesu
- Phone Number: 07988390772
- Email: sughashini.murugesu@nhs.net
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Missed miscarriage and incomplete miscarriage less than 14weeks gestation
- Follow-up recorded at 2 weeks
Exclusion Criteria:
- Final outcome data unavailable
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
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: 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: Misoprostol taken to manage first trimester miscarriage, with follow-up booked in 2 weeks to determine whether complete miscarriage has occurred.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Machine learning predictive model development for miscarriage management outcomes.
Time Frame: Jan 2023- June 2024
|
Machine learning predictive model development based on a retrospective audit of approximately 1000 cases of miscarriage.
|
Jan 2023- June 2024
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Prospective audit to test and validate predictive model
Time Frame: July 2024-June 2025
|
To increase the sensitivity and specificity of the decision aid by widening the data collection to multiple sites and testing the machine learning model with prospective data.
|
July 2024-June 2025
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 23QC8155
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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