Clinical trial NCT06384144: Machine Learning Miscarriage Management Clinical Decision Support Tool Study
Official title: Machine Learning Miscarriage Management Clinical Decision Support Tool Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Miscarriage in First Trimester
Lead sponsor: Imperial College London
Sponsor class: OTHER
First posted: 2024-04-25
Enrollment: 1000
Sex: FEMALE
Minimum age (years): 16
Maximum age (years): 55
Standard ages: CHILD
ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Cases of missed miscarriage and incomplete miscarriage in the first trimester.

Brief 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.

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.

Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

- Final outcome data unavailable

Primary outcomes:
measure: Machine learning predictive model development for miscarriage management outcomes.
timeFrame: Jan 2023- June 2024
Secondary outcomes:
measure: Prospective audit to test and validate predictive model
timeFrame: July 2024-June 2025
Other outcomes:
Locations:
city: London
country: United Kingdom
facility: Imperial College Heatlhcare NHS Trust
Study officials:
References: