Machine Learning Miscarriage Management Clinical Decision Support Tool Study (MLMM)

Imperial College London

Status: Enrolling

Conditions: Miscarriage in First Trimester

Treatments: Other: Medical Management of First Trimester Miscarriage; Other: Expectant Management of First Trimester Miscarriage

Study type: Observational

Funder types: Other

Identifiers

NCT06384144

23QC8155

Details and patient eligibility

About

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.

Full 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.

Enrollment: 1,000 estimated patients

Sex: Female

Ages: 16 to 55 years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

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

Exclusion criteria

• Final outcome data unavailable

Trial design

1,000 participants in 2 patient groups

Expectant Management of Miscarriage

Description:

Cohort that chose to pursue expectant management of miscarriage, final outcome success or failure by day 14 from management choice

Treatment:

Other: Expectant Management of First Trimester Miscarriage

Medical Management of Miscarriage

Description:

Cohort that chose to pursue medical management of miscarriage, final outcome success or failure by day 14 from management choice

Treatment:

Other: Medical Management of First Trimester Miscarriage