Miscarriage in First Trimester
Conditions
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.
Interventions
OTHERExpectant 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.
OTHERMedical 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.
Sponsors
Imperial College London
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
16 Years to 55 Years
Healthy volunteers
Yes
Inclusion criteria
* Missed miscarriage and incomplete miscarriage less than 14weeks gestation * Follow-up recorded at 2 weeks
Exclusion criteria
\- Final outcome data unavailable
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Machine learning predictive model development for miscarriage management outcomes. | Jan 2023- June 2024 | Machine learning predictive model development based on a retrospective audit of approximately 1000 cases of miscarriage. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Prospective audit to test and validate predictive model | July 2024-June 2025 | 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. |
Countries
United Kingdom
Outcome results
None listed