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: Recruiting
• Conditions: Miscarriage in First Trimester
• Intervention / Treatment: Other: Expectant Management of First Trimester Miscarriage; Other: Medical Management of First Trimester Miscarriage

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: Observational
• Enrollment (Estimated): 1000

Contacts and Locations

Study Locations

• United Kingdom
  • 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

• Ages Eligible for Study: Child; Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Cases of missed miscarriage and incomplete miscarriage in the first trimester.

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?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / 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	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 Miscarriage; Cohort that chose to pursue medical management of miscarriage, final outcome success or failure by day 14 from management choice	Other: 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Imperial College London

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2023
• Primary Completion (Estimated): June 1, 2024
• Study Completion (Estimated): June 1, 2026

Study Registration Dates

• First Submitted: April 22, 2024
• First Submitted That Met QC Criteria: April 22, 2024
• First Posted (Actual): April 25, 2024

Study Record Updates

• Last Update Posted (Actual): April 25, 2024
• Last Update Submitted That Met QC Criteria: April 22, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pregnancy Complications; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Abortion, Spontaneous
• Other Study ID Numbers: 23QC8155

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No