Machine Learning Miscarriage Management Clinical Decision Support Tool Study

Miscarriage in First Trimester Clinical Trial

— MLMM  

Official title:

Machine Learning Miscarriage Management Clinical Decision Support Tool Study

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06384144
• Other study ID #: 23QC8155
• Status: Recruiting
• Start date: January 1, 2023
• Est. completion date: June 1, 2026

Study information

• Verified date: April 2024
• Source: Imperial College London
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 1000
• Est. completion date: June 1, 2026
• Est. primary completion date: June 1, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 16 Years to 55 Years

Eligibility

Inclusion Criteria:

• Missed miscarriage and incomplete miscarriage less than 14weeks gestation
• Follow-up recorded at 2 weeks

Exclusion Criteria:

• Final outcome data unavailable

Related Conditions & MeSH terms

• Abortion, Spontaneous
• Miscarriage in First Trimester

Intervention

Other:
• 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.

Locations

Country	Name	City	State
United Kingdom	Imperial College Heatlhcare NHS Trust	London

Sponsors (1)

Lead Sponsor	Collaborator
Imperial College London

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	Jan 2023- June 2024
Secondary	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.	July 2024-June 2025