High-risk PregnancyCURAteNCT06974188
AI-Powered CURAᵀᴹ Application for Identifying At-Risk Pregnancies in Obstetric Management
A study testing AI-risk Stratification in people with High-risk Pregnancy and Pregnancy, run by National University Hospital, Singapore at 1 site in Singapore.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 21 to 50 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- High-risk Pregnancy, Pregnancy, Antenatal Health
- What is being tested?
- AI-risk Stratification
- Type of treatment
- Pill or oral medicine
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,700 participants
- Where
- 1 siteSingapore
- Study dates
- Aug 2025to Mar 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 21 to 50 years
- Women only
- Healthy volunteers accepted
- Adult
Who may be able to join
- 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
Who cannot join
- Not proficient in the English language (AI intervention is only available in English at this stage)
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Singapore1 site
National University Hospital
Singapore
Sarah Li Dr, +65 97833106, sarah_wl_li@nuhs.edu.sg
About this study
Official title: AI-Powered CURAᵀᴹ Application for Identifying At-Risk Pregnancies in Obstetric Management: A Randomized Controlled Trial (CURAte)
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.
Read the detailed description
High-risk pregnancies refer to pregnancies at risk of an adverse maternal outcomes (e.g., gestational diabetes, pre-eclampsia) or fetal/neonatal (e.g. preterm birth, still birth, hypoxic-ischemic encephalopathy). In most healthcare facilities, antenatal care is delivered through a general obstetric clinic. The initial screening of risk is guided by the patient's past medical history, past obstetric history for multigravida patients, and the individual provider's knowledge, which may vary across years of experience in the field. Therefore, the triaging of patients into appropriate antenatal care pathways is inconsistent and often inaccurate. AI technology, particularly Machine Learning (ML) has potential to develop predictive models that are able to segregate low-risk from high-risk pregnancies using complex interactions and relationships. The investigators propose a novel AI-assisted risk stratification model in pregnancy that can help to overcome the current gaps. The AI model considers maternal history and simple biophysical measurements performed in pregnancy.
The primary objective of the CURAte trial is to compare the composite incidence of maternal and fetal/neonatal adverse outcomes between participants who were randomised to the AI-assisted risk stratification intervention arm and participants who were randomised to the no-AI assisted control arm. The secondary objective is to test the feasibility and acceptability of an AI-assisted antenatal risk stratification approach in a real-life patient-care system.
The study will adopt a parallel arm single-blinded, pragmatic randomised controlled trial design. Women presenting at the subsidised antenatal clinics in the first trimester will be approached and assessed for eligibility. A total of 1444 participants (722 in each arm) will be recruited in this study. All participants will be randomised via block randomisation in a 1:1 ratio into two groups (AI-assisted arm versus non-AI assisted arm (standard of care)) which will be done through an electronic programme prepared by the trial statistician. Enrolled participants will be required to complete a questionnaire about their sociodemographic, obstetric and medical history on the FormSG platform prior to consultation with the clinician. The AI-assisted risk stratification will be deployed twice in each participant's pregnancy- at the first trimester visit before 13 weeks' and 6 days' gestation, followed by after the results of the oral glucose tolerance test and third trimester growth scan are available, usually between 31- and 33-weeks' gestation. The results of the AI-assisted risk stratification will not be disclosed in the no-AI intervention arm, until the end of the study. Other study data (i.e. pre-specified study outcomes) will be extracted from medical records at or after 6 weeks from delivery (or at the end of pregnancy) to assess the primary and secondary outcomes.
The primary analysis will be conducted on an intention-to-treat basis, for the binary primary composite outcome of maternal/fetal and neonatal morbidity and mortality. For improved precision, a further multiple regression adjusting for factors known to be prognostic of pregnancy and neonatal outcomes including maternal age, BMI, parity, ethnicity will also be conducted. The investigators' proposed new AI-assisted screening model will address the current gaps in the stratification approach and improve the clinical relevance of antenatal screening in the long run, with downstream positive impact on maternal and neonatal well-being, as well as potential cost savings to the healthcare system. By testing this AI-assisted model in an actual clinical setting in a public healthcare institution, the investigators will be able to identify challenges relating to real-world logistics and enablers for translating this digital innovation into clinical practice. The investigators can use the findings to elicit specific modifications to both the AI-assisted model workflow and CuraTM application, ultimately optimising the future implementation as well as acceptability and uptake amongst healthcare providers and pregnant women.
- Conditions
- High-risk Pregnancy, Pregnancy, Antenatal Health
- Being tested
- AI-risk Stratification
- Keywords
- Machine-learning, Artificial Intelligence, Antenatal Risk Stratification, Software as a Medical Device, Machine Learning Algorithm, Pregnancy Complications, Neonatal Complications, Pregnancy outcomes, Obstetric complications, Antenatal care
- Medical subject headings
- Pregnancy Complications
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-risk StratificationOther
With 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.
| Study group | Role | What they receive |
|---|---|---|
| AI-risk stratification arm | Experimental | During their first trimester antenatal visit, participants will be risk stratified by clinicians with the assistance of AI. The risk prediction of 'high risk' or 'low risk' will be immediately made known to the clinician during the visit.
Similarly, during the 31 -34 weeks visit following release of oral glucose tolerance test and growth scan results, participants will be risk stratified by clinicians again with the assistance of AI. Receives: Other: AI-risk Stratification |
| Non-AI arm (Current Risk Stratification) | No intervention | During their first trimester antenatal visit, participants will be risk stratified by both clinicians alone and with the assistance of AI. However, the risk prediction of 'high risk' or 'low risk' will not be disclosed to the clinician at all. It will only be revealed to the study investigators at the end of the study. Similarly, during the 31 -34 weeks visit following release of oral glucose tolerance test and growth scan results, participants will be risk stratified by clinicians again with and without the assistance of AI. However, the risk prediction of 'high risk' or 'low risk' will not be disclosed to the clinician at all. It will only be revealed to the study investigators at the end of the study. Clinicians will use their own judgement as to the risk of the participants' pregnancies. They have to adhere to the same specific 'high-risk' and 'low-risk' management protocol. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: At Delivery (Birth)
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
Measured: At Delivery (Birth)
Secondary outcomes (3)
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
Measured: At baseline visit (first trimester antenatal visit)
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
Measured: At baseline visit (first trimester antenatal visit)
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
Measured: At baseline visit (first trimester antenatal visit)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 May 2025
- Study startAug 2025 (estimated)
- Primary completionSep 2026 (estimated)
- Study completionMar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (10)Papers the sponsor has linked to this study.
- Other trials for High-risk PregnancyStudies of the same condition, open and under way.
- More trials run by National University Hospital, SingaporeThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- The study employs a two parallel arm, single-blinded, pragmatic randomised controlled trial design. In this design, pregnant women will be randomly assigned in a 1:1 ratio to an AI-assisted risk categorization approach or the current manual risk stratification method.
- Masking
- Triple blind
- Who is masked
- Participant, Investigator, Outcomes assessor
- Enrolment
- 1,700 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National University Hospital, Singapore
- Sponsor type
- University, hospital or other
- Collaborators
- National University of Singapore, National University of Singapore, Saw Swee Hock School of Public Health, Agency for Science, Technology and Research (A*STAR)
- Responsible party
- Sponsor
- Investigators
- Sarah Li, MRCOG, MPH, Principal investigator, National University Hospital, Singapore
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06974188
- Sponsor's protocol ID
- 2024-3832
- Acronym
- CURAte
- Status last verified
- Nov 2024
- First submitted
- 29 Apr 2025
- Last updated
- 15 Jul 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ. 2020 Sep 9;370:m3210. doi: 10.1136/bmj.m3210.
PubMed 32907797Malacova E, Tippaya S, Bailey HD, Chai K, Farrant BM, Gebremedhin AT, Leonard H, Marinovich ML, Nassar N, Phatak A, Raynes-Greenow C, Regan AK, Shand AW, Shepherd CCJ, Srinivasjois R, Tessema GA, Pereira G. Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980-2015. Sci Rep. 2020 Mar 24;10(1):5354. doi: 10.1038/s41598-020-62210-9.
PubMed 32210300Jhee JH, Lee S, Park Y, Lee SE, Kim YA, Kang SW, Kwon JY, Park JT. Prediction model development of late-onset preeclampsia using machine learning-based methods. PLoS One. 2019 Aug 23;14(8):e0221202. doi: 10.1371/journal.pone.0221202. eCollection 2019.
PubMed 31442238Arabi Belaghi R, Beyene J, McDonald SD. Prediction of preterm birth in nulliparous women using logistic regression and machine learning. PLoS One. 2021 Jun 30;16(6):e0252025. doi: 10.1371/journal.pone.0252025. eCollection 2021.
PubMed 34191801Bhutta ZA, Das JK, Bahl R, Lawn JE, Salam RA, Paul VK, Sankar MJ, Blencowe H, Rizvi A, Chou VB, Walker N; Lancet Newborn Interventions Review Group; Lancet Every Newborn Study Group. Can available interventions end preventable deaths in mothers, newborn babies, and stillbirths, and at what cost? Lancet. 2014 Jul 26;384(9940):347-70. doi: 10.1016/S0140-6736(14)60792-3. Epub 2014 May 19.
PubMed 24853604Gosavi A, Amin Z, Carter SWD, Choolani MA, Fee EL, Milad MA, Jobe AH, Kemp MW. Antenatal corticosteroids in Singapore: a clinical and scientific assessment. Singapore Med J. 2024 Sep 1;65(9):479-487. doi: 10.4103/SINGAPOREMEDJ.SMJ-2022-014. Epub 2022 Oct 6.
PubMed 36254928Hewage S, Audimulam J, Sullivan E, Chi C, Yew TW, Yoong J. Barriers to Gestational Diabetes Management and Preferred Interventions for Women With Gestational Diabetes in Singapore: Mixed Methods Study. JMIR Form Res. 2020 Jun 30;4(6):e14486. doi: 10.2196/14486.
PubMed 32602845Phibbs CM, Kozhimannil KB, Leonard SA, Lorch SA, Main EK, Schmitt SK, Phibbs CS. A Comprehensive Analysis of the Costs of Severe Maternal Morbidity. Womens Health Issues. 2022 Jul-Aug;32(4):362-368. doi: 10.1016/j.whi.2021.12.006. Epub 2022 Jan 12.
PubMed 35031196Chi C, Pang D, Aris IM, Teo WT, Li SW, Biswas A, Yong EL, Chong YS, Tan K, Kramer MS. Trends and predictors of cesarean birth in Singapore, 2005-2014: A population-based cohort study. Birth. 2018 Dec;45(4):399-408. doi: 10.1111/birt.12341. Epub 2018 Feb 17.
PubMed 29453821Fink DA, Kilday D, Cao Z, Larson K, Smith A, Lipkin C, Perigard R, Marshall R, Deirmenjian T, Finke A, Tatum D, Rosenthal N. Trends in Maternal Mortality and Severe Maternal Morbidity During Delivery-Related Hospitalizations in the United States, 2008 to 2021. JAMA Netw Open. 2023 Jun 1;6(6):e2317641. doi: 10.1001/jamanetworkopen.2023.17641.
PubMed 37347486
Study at a glance
From the registry record, last updated by the sponsor on 15 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-Powered CURAᵀᴹ Application for Identifying At-Risk Pregnancies in Obstetric Management: A Randomized Controlled Trial (CURAte)
- ClinicalTrials.gov ID
- NCT06974188
- Acronym / sponsor ID
- CURAte / 2024-3832
- Conditions
- High-risk Pregnancy, Pregnancy, Antenatal Health
- Keywords
- Machine-learning, Artificial Intelligence, Antenatal Risk Stratification, Software as a Medical Device, Machine Learning Algorithm, Pregnancy Complications, Neonatal Complications, Pregnancy outcomes
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,700 participants (estimated)
- Lead sponsor
- National University Hospital, SingaporeUniversity, hospital or other
- Collaborators
- National University of Singapore, National University of Singapore, Saw Swee Hock School of Public Health, Agency for Science, Technology and Research (A*STAR)
- Study start
- Aug 2025 (estimated)
- Primary completion
- Sep 2026 (estimated)
- Study completion
- Mar 2027 (estimated)
- First posted
- 15 May 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
High-risk pregnancies refer to pregnancies at risk of an adverse maternal outcomes (e.g., gestational diabetes, pre-eclampsia) or fetal/neonatal (e.g. preterm birth, still birth, hypoxic-ischemic encephalopathy). In most healthcare facilities, antenatal care is delivered through a general obstetric clinic. The initial screening of risk is guided by the patient's past medical history, past obstetric history for multigravida patients, and the individual provider's knowledge, which may vary across years of experience in the field. Therefore, the triaging of patients into appropriate antenatal care pathways is inconsistent and often inaccurate. AI technology, particularly Machine Learning (ML) has potential to develop predictive models that are able to segregate low-risk from high-risk pregnancies using complex interactions and relationships. The investigators propose a novel AI-assisted risk stratification model in pregnancy that can help to overcome the current gaps. The AI model considers maternal history and simple biophysical measurements performed in pregnancy.
The primary objective of the CURAte trial is to compare the composite incidence of maternal and fetal/neonatal adverse outcomes between participants who were randomised to the AI-assisted risk stratification intervention arm and participants who were randomised to the no-AI assisted control arm. The secondary objective is to test the feasibility and acceptability of an AI-assisted antenatal risk stratification approach in a real-life patient-care system.
The study will adopt a parallel arm single-blinded, pragmatic randomised controlled trial design. Women presenting at the subsidised antenatal clinics in the first trimester will be approached and assessed for eligibility. A total of 1444 participants (722 in each arm) will be recruited in this study. All participants will be randomised via block randomisation in a 1:1 ratio into two groups (AI-assisted arm versus non-AI assisted arm (standard of care)) which will be done through an electronic programme prepared by the trial statistician. Enrolled participants will be required to complete a questionnaire about their sociodemographic, obstetric and medical history on the FormSG platform prior to consultation with the clinician. The AI-assisted risk stratification will be deployed twice in each participant's pregnancy- at the first trimester visit before 13 weeks' and 6 days' gestation, followed by after the results of the oral glucose tolerance test and third trimester growth scan are available, usually between 31- and 33-weeks' gestation. The results of the AI-assisted risk stratification will not be disclosed in the no-AI intervention arm, until the end of the study. Other study data (i.e. pre-specified study outcomes) will be extracted from medical records at or after 6 weeks from delivery (or at the end of pregnancy) to assess the primary and secondary outcomes.
The primary analysis will be conducted on an intention-to-treat basis, for the binary primary composite outcome of maternal/fetal and neonatal morbidity and mortality. For improved precision, a further multiple regression adjusting for factors known to be prognostic of pregnancy and neonatal outcomes including maternal age, BMI, parity, ethnicity will also be conducted. The investigators' proposed new AI-assisted screening model will address the current gaps in the stratification approach and improve the clinical relevance of antenatal screening in the long run, with downstream positive impact on maternal and neonatal well-being, as well as potential cost savings to the healthcare system. By testing this AI-assisted model in an actual clinical setting in a public healthcare institution, the investigators will be able to identify challenges relating to real-world logistics and enablers for translating this digital innovation into clinical practice. The investigators can use the findings to elicit specific modifications to both the AI-assisted model workflow and CuraTM application, ultimately optimising the future implementation as well as acceptability and uptake amongst healthcare providers and pregnant women.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind (participant, investigator, outcomes assessor)
- Number of arms
- 2
- Model description
- The study employs a two parallel arm, single-blinded, pragmatic randomised controlled trial design. In this design, pregnant women will be randomly assigned in a 1:1 ratio to an AI-assisted risk categorization approach or the current manual risk stratification method.
| Arm | Type | Receives |
|---|---|---|
| AI-risk stratification arm | Experimental | Other: AI-risk StratificationDuring their first trimester antenatal visit, participants will be risk stratified by clinicians with the assistance of AI. The risk prediction of 'high risk' or 'low risk' will be immediately made known to the clinician during the visit. Similarly, during the 31 -34 weeks visit following release of oral glucose tolerance test and growth scan results, participants will be risk stratified by clinicians again with the assistance of AI. |
| Non-AI arm (Current Risk Stratification) | No intervention | –During their first trimester antenatal visit, participants will be risk stratified by both clinicians alone and with the assistance of AI. However, the risk prediction of 'high risk' or 'low risk' will not be disclosed to the clinician at all. It will only be revealed to the study investigators at the end of the study. Similarly, during the 31 -34 weeks visit following release of oral glucose tolerance test and growth scan results, participants will be risk stratified by clinicians again with and without the assistance of AI. However, the risk prediction of 'high risk' or 'low risk' will not be disclosed to the clinician at all. It will only be revealed to the study investigators at the end of the study. Clinicians will use their own judgement as to the risk of the participants' pregnancies. They have to adhere to the same specific 'high-risk' and 'low-risk' management protocol. |
Who may qualify
- Ages
- 21 to 50 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (1 listed)
- Not proficient in the English language (AI intervention is only available in English at this stage)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-risk StratificationWith 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. | Other | AI-risk stratification arm |
Primary outcome
The number and proportion of cases displaying any one of the following outcomes listed below (composite):
Time frame: At Delivery (Birth)
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
In addition, these outcomes will be reported individually:
Time frame: At Delivery (Birth)
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
3 secondary outcomes
To test the feasibility of an AI-assisted antenatal risk stratification approach in a real-life patient-care system - Quantitative; Mean Differences
Time frame: At baseline visit (first trimester antenatal visit)
To test the feasibility of an AI-assisted antenatal risk stratification approach in a real-life patient-care system - Qualitative
Time frame: At baseline visit (first trimester antenatal visit)
To test the feasibility of an AI-assisted antenatal risk stratification approach in a real-life patient-care system - Quantitative; Number
Time frame: At baseline visit (first trimester antenatal visit)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Jul 2025.
- Background: PubMed 32907797Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ. 2020 Sep 9;370:m3210. doi: 10.1136/bmj.m3210.
- Background: PubMed 32210300Malacova E, Tippaya S, Bailey HD, Chai K, Farrant BM, Gebremedhin AT, Leonard H, Marinovich ML, Nassar N, Phatak A, Raynes-Greenow C, Regan AK, Shand AW, Shepherd CCJ, Srinivasjois R, Tessema GA, Pereira G. Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980-2015. Sci Rep. 2020 Mar 24;10(1):5354. doi: 10.1038/s41598-020-62210-9.
- Background: PubMed 31442238Jhee JH, Lee S, Park Y, Lee SE, Kim YA, Kang SW, Kwon JY, Park JT. Prediction model development of late-onset preeclampsia using machine learning-based methods. PLoS One. 2019 Aug 23;14(8):e0221202. doi: 10.1371/journal.pone.0221202. eCollection 2019.
- Background: PubMed 34191801Arabi Belaghi R, Beyene J, McDonald SD. Prediction of preterm birth in nulliparous women using logistic regression and machine learning. PLoS One. 2021 Jun 30;16(6):e0252025. doi: 10.1371/journal.pone.0252025. eCollection 2021.
- Background: PubMed 24853604Bhutta ZA, Das JK, Bahl R, Lawn JE, Salam RA, Paul VK, Sankar MJ, Blencowe H, Rizvi A, Chou VB, Walker N; Lancet Newborn Interventions Review Group; Lancet Every Newborn Study Group. Can available interventions end preventable deaths in mothers, newborn babies, and stillbirths, and at what cost? Lancet. 2014 Jul 26;384(9940):347-70. doi: 10.1016/S0140-6736(14)60792-3. Epub 2014 May 19.
- Background: PubMed 36254928Gosavi A, Amin Z, Carter SWD, Choolani MA, Fee EL, Milad MA, Jobe AH, Kemp MW. Antenatal corticosteroids in Singapore: a clinical and scientific assessment. Singapore Med J. 2024 Sep 1;65(9):479-487. doi: 10.4103/SINGAPOREMEDJ.SMJ-2022-014. Epub 2022 Oct 6.
- Background: PubMed 32602845Hewage S, Audimulam J, Sullivan E, Chi C, Yew TW, Yoong J. Barriers to Gestational Diabetes Management and Preferred Interventions for Women With Gestational Diabetes in Singapore: Mixed Methods Study. JMIR Form Res. 2020 Jun 30;4(6):e14486. doi: 10.2196/14486.
- Background: PubMed 35031196Phibbs CM, Kozhimannil KB, Leonard SA, Lorch SA, Main EK, Schmitt SK, Phibbs CS. A Comprehensive Analysis of the Costs of Severe Maternal Morbidity. Womens Health Issues. 2022 Jul-Aug;32(4):362-368. doi: 10.1016/j.whi.2021.12.006. Epub 2022 Jan 12.
- Background: PubMed 29453821Chi C, Pang D, Aris IM, Teo WT, Li SW, Biswas A, Yong EL, Chong YS, Tan K, Kramer MS. Trends and predictors of cesarean birth in Singapore, 2005-2014: A population-based cohort study. Birth. 2018 Dec;45(4):399-408. doi: 10.1111/birt.12341. Epub 2018 Feb 17.
- Background: PubMed 37347486Fink DA, Kilday D, Cao Z, Larson K, Smith A, Lipkin C, Perigard R, Marshall R, Deirmenjian T, Finke A, Tatum D, Rosenthal N. Trends in Maternal Mortality and Severe Maternal Morbidity During Delivery-Related Hospitalizations in the United States, 2008 to 2021. JAMA Netw Open. 2023 Jun 1;6(6):e2317641. doi: 10.1001/jamanetworkopen.2023.17641.
- Individual participant dataSharing planned: Undecided
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Pucotenlimab, Lenvatinib, and SBRT Before Surgery for Localized Clear Cell Renal Cell Carcinoma
Localized Clear Cell Renal Cell Carcinoma
Recruiting
Unravelling Tumour Biology In Ovarian Cancer With Precision Imaging
High Grade Serous Ovarian Cancer
RecruitingPhase 2
Immunotherapy Platform Study in Platinum Resistant High Grade Serous Ovarian Cancer
Ovarian Cancer
RecruitingPhase 3
Tranexamic Acid to Prevent Heavy Bleeding After Childbirth in Women at Higher Risk
Postpartum Hemorrhage
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Sarah Li, MRCOG, MPH principal investigator
National University Hospital, Singapore
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-risk Stratification in people with High-risk Pregnancy and Pregnancy, run by National University Hospital, Singapore at 1 site in Singapore.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 15 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Singapore. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Sarah Li, MRCOG, MPH ((+65)97833106, sarah_wl_li@nuhs.edu.sg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The number and proportion of cases displaying any one of the following outcomes listed below (composite): (time frame: At Delivery (Birth)). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06974188, which also lists 10 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Jul 2025.