Clinical Trial Details
— Status: Recruiting
Administrative data
NCT number |
NCT06384144 |
Other study ID # |
23QC8155 |
Secondary ID |
|
Status |
Recruiting |
Phase |
|
First received |
|
Last updated |
|
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 |
Health authority |
|
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.