Miscarriage in First TrimesterMLMMNCT06384144
Machine Learning Miscarriage Management Clinical Decision Support Tool Study
An observational study of Miscarriage in First Trimester, run by Imperial College London at 1 site in United Kingdom.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 16 to 55 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Miscarriage in First Trimester
- What is being tested?
- Expectant Management of First Trimester Miscarriage and Medical Management of First Trimester Miscarriage
- How many people
- 1,000 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 1 Jan 2023to 1 Jun 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 16 to 55 years
- Women only
- Healthy volunteers accepted
- Child, Adult
Study population: Cases of missed miscarriage and incomplete miscarriage in the first trimester.
Who may be able to join
- Missed miscarriage and incomplete miscarriage less than 14weeks gestation
- Follow-up recorded at 2 weeks
Who cannot join
- Final outcome data unavailable
Trial locations and contacts
1 site in 1 country.
United Kingdom1 site, 1 recruiting
- Recruiting
Imperial College Heatlhcare NHS Trust
London, W12 0HS
Sughashini Murugesu, 07988390772, sughashini.murugesu@nhs.net
About this study
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.
Read the 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.
- Conditions
- Miscarriage in First Trimester
- Being tested
- Expectant Management of First Trimester Miscarriage, Medical Management of First Trimester Miscarriage
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Expectant Management of First Trimester MiscarriageOther
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 MiscarriageOther
Medical Management: Misoprostol taken to manage first trimester miscarriage, with follow-up booked in 2 weeks to determine whether complete miscarriage has occurred.
| Study group | Role | What they receive |
|---|---|---|
| Expectant Management of Miscarriage | Cohort that chose to pursue expectant management of miscarriage, final outcome success or failure by day 14 from management choice Receives: Other: Expectant Management of First Trimester Miscarriage | |
| Medical Management of Miscarriage | Cohort that chose to pursue medical management of miscarriage, final outcome success or failure by day 14 from management choice Receives: Other: Medical Management of First Trimester Miscarriage |
What is being measured
The primary outcome is the main question the study is designed to answer.
Machine learning predictive model development for miscarriage management outcomes.
Machine learning predictive model development based on a retrospective audit of approximately 1000 cases of miscarriage.
Measured: Jan 2023- June 2024
Secondary outcomes (1)
Prospective audit to test and validate predictive model
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.
Measured: July 2024-June 2025
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Apr 2024
- Study start1 Jan 2023
- Primary completion1 Jun 2024 (estimated)
- Study completion1 Jun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Miscarriage in First TrimesterStudies of the same condition, open and under way.
- More trials run by Imperial College LondonThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Imperial College London
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06384144
- Sponsor's protocol ID
- 23QC8155
- Acronym
- MLMM
- Status last verified
- Apr 2024
- First submitted
- 22 Apr 2024
- Last updated
- 25 Apr 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 25 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning Miscarriage Management Clinical Decision Support Tool Study
- ClinicalTrials.gov ID
- NCT06384144
- Acronym / sponsor ID
- MLMM / 23QC8155
- Conditions
- Miscarriage in First Trimester
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Imperial College LondonUniversity, hospital or other
- Study start
- 1 Jan 2023 (actual)
- Primary completion
- 1 Jun 2024 (estimated)
- Study completion
- 1 Jun 2026 (estimated)
- First posted
- 25 Apr 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Expectant Management of Miscarriage | – | Other: Expectant Management of First Trimester MiscarriageCohort that chose to pursue expectant management of miscarriage, final outcome success or failure by day 14 from management choice |
| Medical Management of Miscarriage | – | Other: Medical Management of First Trimester MiscarriageCohort that chose to pursue medical management of miscarriage, final outcome success or failure by day 14 from management choice |
Who may qualify
- Ages
- 16 to 55 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
- Study population
- Cases of missed miscarriage and incomplete miscarriage in the first trimester.
Key inclusion criteria (2 listed)
- Missed miscarriage and incomplete miscarriage less than 14weeks gestation
- Follow-up recorded at 2 weeks
Key exclusion criteria (1 listed)
- Final outcome data unavailable
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Expectant Management of First Trimester MiscarriageExpectant Management: Conservative management if miscarriage with follow-up booked in 2 weeks to determine whether complete miscarriage has occurred. | Other | Expectant Management of Miscarriage |
| Medical Management of First Trimester MiscarriageMedical Management: Misoprostol taken to manage first trimester miscarriage, with follow-up booked in 2 weeks to determine whether complete miscarriage has occurred. | Other | Medical Management of Miscarriage |
Primary outcome
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.
1 secondary outcome
Prospective audit to test and validate predictive model
Time frame: July 2024-June 2025
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Apr 2024.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Miscarriage in First Trimester, run by Imperial College London at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 25 Apr 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Machine learning predictive model development for miscarriage management outcomes. (time frame: Jan 2023- June 2024). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Apr 2024.