AI-Powered CURAᵀᴹ Application for Identifying At-Risk Pregnancies in Obstetric Management
NCT ID: NCT06974188
Last Updated: 2025-07-15
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
NA
1700 participants
INTERVENTIONAL
2025-08-31
2027-03-31
Brief Summary
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Detailed Description
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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
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Study Design
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RANDOMIZED
PARALLEL
SCREENING
TRIPLE
Study Groups
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AI-risk stratification arm
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.
AI-risk Stratification
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.
Non-AI arm (Current Risk Stratification)
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.
No interventions assigned to this group
Interventions
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AI-risk Stratification
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.
Eligibility Criteria
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Inclusion Criteria
* Singleton Pregnancy
* No more than 13 weeks' and 6 days' gestation at recruitment
* Able to provide written, informed consent
Exclusion Criteria
21 Years
50 Years
FEMALE
Yes
Sponsors
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National University of Singapore
OTHER
National University of Singapore, Saw Swee Hock School of Public Health
UNKNOWN
Agency for Science, Technology and Research (A*STAR)
OTHER_GOV
National University Hospital, Singapore
OTHER
Responsible Party
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Principal Investigators
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Sarah Li, MRCOG, MPH
Role: PRINCIPAL_INVESTIGATOR
National University Hospital, Singapore
Locations
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National University Hospital
Singapore, , Singapore
Countries
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Central Contacts
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Facility Contacts
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References
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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.
Malacova 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.
Jhee 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.
Arabi 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.
Bhutta 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.
Gosavi 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.
Hewage 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.
Phibbs 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.
Chi 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.
Fink 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.
Other Identifiers
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2024-3832
Identifier Type: -
Identifier Source: org_study_id
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