Ignite Creation Date:
2024-05-06 @ 8:26 PM
Last Modification Date:
2024-10-26 @ 3:27 PM
Study NCT ID:
NCT06384144
Status:
RECRUITING
Last Update Posted:
2024-04-25
First Post:
2024-04-22
Brief Title:
Machine Learning Miscarriage Management Clinical Decision Support Tool Study
Sponsor:
Imperial College London
Organization:
Imperial College London
Study Overview
Official Title:
Machine Learning Miscarriage Management Clinical Decision Support Tool Study
Status:
RECRUITING
Status Verified Date:
2024-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
MLMM
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 SMMMVA 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
Demographics Maternal Age Parity
History Previous CS Previous SMMMVA 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 Charlottes 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 SMMMVA 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
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 Charlottes 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 SMMMVA 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None