AI-Powered CURAᵀᴹ Application for Identifying At-Risk Pregnancies in Obstetric Management
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexFemale
Age21-50
SponsorNational University Hospital, Singapore
About this trial
It is important to identify high pregnancies early through screening so that appropriate care and intervention may be instituted. An AI-assisted risk categorisation approach may be advantageous compared with traditional means of screening. The purpose of this study is to determine if the adoption of an AI-assisted approach in general pregnancy risk screening will improve the accuracy of antenatal risk categorization into high- and low- risk pregnancy groups, ultimately resulting in fewer poor maternal and fetal/neonatal outcomes.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
Age: 21 years old to 50 years old
Singleton Pregnancy
No more than 13 weeks' and 6 days' gestation at recruitment
Able to provide written, informed consent
Disqualifiers
Not proficient in the English language (AI intervention is only available in English at this stage)
Trial design
Design model
Parallel
Treatments tested in this trial
AI-risk Stratification
Other interventionWith the results being disclosed as 'high-risk' or 'low-risk' in the experimental arm, clinicians have to adhere to a specific 'high-risk' and 'low-risk' management protocol for participants.
Treatment groups
1,700 Participants
are divided into 2 treatment groupsGroup A: AI-risk stratification armExperimental treatment 1 intervention
Group B: Non-AI arm (Current Risk Stratification)No intervention 0 interventions
Trial outcomes
Primary outcomes
1
The number and proportion of cases displaying any one of the following outcomes listed below (composite):
Presented as a relative risk (RR) with 95% CI, with and without stratification for parity, ethnicity and BMI. (i) Maternal * Postpartum haemorrhage more than/equal to 1000ml or requiring blood product transfusion * Eclampsia/severe eclampsia (blood pressure SBP\>160mmHg or diastolic \>110mmHg and/or significant end-organ damage which can include signs of liver or kidney impairment, low platelet count, fluid in the lungs or new-onset headache/visual disturbances) * ICU or HDU admission (excluding those who are admitted for precautionary monitoring) * Unplanned hysterectomy * Uterine rupture (symptomatic full thickness rupture) * Venous thromboembolism proven radiologically * Maternal mortality (ii) Neonatal: * Apgar scores \<7 at 5 minutes or less than/equal to 4 at 10 minutes * Respiratory distress requiring mechanical ventilation or surfactant treatment * Hypoxic ischaemic encephalopathy (HIE, any grade) * neonatal death (within 28 days of birth)/ * intrauterine demise
Time frame
At Delivery (Birth)2
In addition, these outcomes will be reported individually:
Number and proportion, RR and 95% CI for each of the following: * Postpartum haemorrhage more than/equal to 1000ml or requiring blood product transfusion as well as mean difference in blood loss * Eclampsia/severe eclampsia (blood pressure SBP\>160mmHg or diastolic \>110mmHg and/or significant end-organ damage which can include signs of liver or kidney impairment, low platelet count, fluid in the lungs or new-onset headache/visual disturbances) as well as difference in gestational age of onset or diagnosis with and without adjustment for important covariates * ICU or HDU admission (excluding those who are admitted for precautionary monitoring) * Maternal mortality as well as cause of death * Respiratory distress requiring mechanical ventilation or surfactant treatment as well as aetiology for RDS * Hypoxic ischaemic encephalopathy (HIE, any grade) * Neonatal death (within 28 days of birth) * Intrauterine demise
Time frame
At Delivery (Birth)Secondary outcomes
1
To test the feasibility of an AI-assisted antenatal risk stratification approach in a real-life patient-care system - Quantitative; Mean Differences
* Mean differences or categorically-determined outcomes of time taken for each participant to complete the questionnaire * Mean differences or categorically-determined outcomes of time taken for each consultation with the clinician to complete the questionnaire and launch AI
Time frame
At baseline visit (first trimester antenatal visit)2
To test the feasibility of an AI-assisted antenatal risk stratification approach in a real-life patient-care system - Qualitative
* Qualitative Feedback to determine acceptability by participants and clinicians * Clinicians' actions in response to the AI results for all cases: low, high, undisclosed to assess mechanisms in which AI influences clinicians' decision- making
Time frame
At baseline visit (first trimester antenatal visit)3
To test the feasibility of an AI-assisted antenatal risk stratification approach in a real-life patient-care system - Quantitative; Number
• Number of (potential) participants screened and recruited per antenatal clinic session
Time frame
At baseline visit (first trimester antenatal visit)Sponsors and contacts
Click on the lead sponsor to view all of their trials.
National University Hospital, Singapore
Lead sponsor
National University of Singapore
Collaborator
National University of Singapore, Saw Swee Hock School of Public Health
Collaborator
Agency for Science, Technology and Research (A*STAR)
Collaborator
Source ClinicalTrials.gov
This trial is not recruiting at the moment. You can still explore other options: