Ignite Creation Date:
2026-03-26 @ 3:18 PM
Ignite Modification Date:
2026-03-31 @ 7:35 AM
Study NCT ID:
NCT07430358
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-02-24
First Post:
2025-12-16
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Obstetric Risk Assessment & Cesarean-delivery in Labor Estimation Using Artificial Intelligence
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'DOUBLE', 'whoMasked': ['PARTICIPANT', 'OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'SUPPORTIVE_CARE', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 400}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-03-01', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-02', 'completionDateStruct': {'date': '2027-04-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-02-22', 'studyFirstSubmitDate': '2025-12-16', 'studyFirstSubmitQcDate': '2026-02-22', 'lastUpdatePostDateStruct': {'date': '2026-02-24', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-02-24', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-12-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'otherOutcomes': [{'measure': 'Decision Latency to Unplanned Cesarean Delivery', 'timeFrame': 'From the first documented intrapartum triggering event during labor to surgical skin incision for unplanned cesarean delivery, occurring during the index hospitalization (up to 7 days after randomization).', 'description': 'Time interval, in minutes, from the first documented intrapartum triggering event (e.g., vaginal bleeding or non-reassuring cardiotocography) to skin incision for unplanned cesarean delivery, derived from electronic health record timestamps.'}], 'primaryOutcomes': [{'measure': 'Primary Endpoint: unplanned cesarean delivery rates.', 'timeFrame': 'From randomization at labor admission to delivery (time of birth), up to 7 days.', 'description': 'Unplanned cesarean delivery is defined as any cesarean delivery performed after the onset of labor or during induction of labor, in participants randomized to the study, excluding scheduled or elective cesarean deliveries. The outcome is assessed from the time of randomization at labor admission through delivery and is recorded as a binary variable (yes/no) per participant, based on electronic health record documentation.'}], 'secondaryOutcomes': [{'measure': 'Postpartum Hemorrhage', 'timeFrame': 'From delivery (time of birth) through maternal hospital discharge, up to 30 days.', 'description': 'Occurrence of postpartum hemorrhage as documented in the electronic health record, defined by \\>500ml in vaginal delivery and \\>1000ml in cesarean delivery and/or hemodynamic instability requiring clinical intervention, and/or need for blood transfusion.'}, {'measure': 'Maternal ICU Admission', 'timeFrame': 'From delivery (time of birth) through maternal hospital discharge, up to 30 days.', 'description': 'Admission to a maternal intensive care unit following delivery.'}, {'measure': 'Chorioamnionitis', 'timeFrame': 'From randomization at labor admission through maternal hospital discharge, up to 30 days.', 'description': 'Clinical or histologic diagnosis of chorioamnionitis documented in the electronic health record.'}, {'measure': 'Advanced Perineal Tear', 'timeFrame': 'At delivery (time of birth), within 7 days of randomization.', 'description': 'Third- or fourth-degree perineal laceration documented at delivery.'}, {'measure': 'Length of Maternal Hospitalization', 'timeFrame': 'From delivery (time of birth) through maternal hospital discharge, up to 30 days.', 'description': 'Total length of maternal hospital stay in days, calculated from delivery (time of birth) to maternal hospital discharge.'}, {'measure': 'Maternal mortality', 'timeFrame': 'From delivery (time of birth) through maternal hospital discharge, up to 30 days.', 'description': 'Death of the mother prior to hospital discharge.'}, {'measure': 'Neonatal Mortality', 'timeFrame': 'From birth through neonatal hospital discharge, up to 30 days.', 'description': 'Death of the neonate prior to hospital discharge.'}, {'measure': 'Low Apgar Score', 'timeFrame': 'At 1 minute and 5 minutes after birth.', 'description': 'Apgar score assessed at 1 minute and 5 minutes after birth. The Apgar score ranges from 0 to 10, with higher scores indicating better neonatal condition. The proportion of neonates with Apgar score ≤7 will be reported.'}, {'measure': 'Umbilical Cord Arterial pH < 7.10', 'timeFrame': 'At birth.', 'description': 'Umbilical arterial blood pH less than 7.10 measured at birth.'}, {'measure': 'Neonatal Intensive Care Unit Admission', 'timeFrame': 'From birth through neonatal hospital discharge, up to 30 days.', 'description': 'Admission of the neonate to a neonatal intensive care unit.'}, {'measure': 'Neonatal Mechanical Ventilation', 'timeFrame': 'From birth through neonatal hospital discharge, up to 30 days.', 'description': 'Requirement for invasive mechanical ventilation during the neonatal hospitalization.'}, {'measure': 'Length of Neonatal Hospitalization', 'timeFrame': 'From birth through neonatal hospital discharge, up to 30 days.', 'description': 'Total length of neonatal hospital stay in days, calculated from birth to neonatal hospital discharge.'}]}, 'oversightModule': {'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Labor, Obstetric', 'Pregnancy', 'Cesarean Section Rate']}, 'referencesModule': {'references': [{'pmid': '38517902', 'type': 'BACKGROUND', 'citation': 'Huurnink JME, Blix E, Hals E, Kaasen A, Bernitz S, Lavender T, Ahlberg M, Oian P, Hoifodt AI, Miltenburg AS, Pay ASD. Labor curves based on cervical dilatation over time and their accuracy and effectiveness: A systematic scoping review. PLoS One. 2024 Mar 22;19(3):e0298046. doi: 10.1371/journal.pone.0298046. eCollection 2024.'}, {'pmid': '28157275', 'type': 'BACKGROUND', 'citation': 'Alfirevic Z, Devane D, Gyte GM, Cuthbert A. Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database Syst Rev. 2017 Feb 3;2(2):CD006066. doi: 10.1002/14651858.CD006066.pub3.'}, {'pmid': '32434000', 'type': 'BACKGROUND', 'citation': 'Guedalia J, Lipschuetz M, Novoselsky-Persky M, Cohen SM, Rottenstreich A, Levin G, Yagel S, Unger R, Sompolinsky Y. Real-time data analysis using a machine learning model significantly improves prediction of successful vaginal deliveries. Am J Obstet Gynecol. 2020 Sep;223(3):437.e1-437.e15. doi: 10.1016/j.ajog.2020.05.025. Epub 2020 May 17.'}, {'pmid': '35752169', 'type': 'BACKGROUND', 'citation': 'Wong MS, Wells M, Zamanzadeh D, Akre S, Pevnick JM, Bui AAT, Gregory KD. Applying Automated Machine Learning to Predict Mode of Delivery Using Ongoing Intrapartum Data in Laboring Patients. Am J Perinatol. 2024 May;41(S 01):e412-e419. doi: 10.1055/a-1885-1697. Epub 2022 Jun 25.'}, {'pmid': '28213060', 'type': 'BACKGROUND', 'citation': 'Burke N, Burke G, Breathnach F, McAuliffe F, Morrison JJ, Turner M, Dornan S, Higgins JR, Cotter A, Geary M, McParland P, Daly S, Cody F, Dicker P, Tully E, Malone FD; Perinatal Ireland Research Consortium. Prediction of cesarean delivery in the term nulliparous woman: results from the prospective, multicenter Genesis study. Am J Obstet Gynecol. 2017 Jun;216(6):598.e1-598.e11. doi: 10.1016/j.ajog.2017.02.017. Epub 2017 Feb 16.'}, {'pmid': '40527278', 'type': 'BACKGROUND', 'citation': 'Wakefield BM, Zapf MA, Ende HB. Artificial intelligence in prediction of postpartum hemorrhage: a primer and review. Int J Obstet Anesth. 2025 Aug;63:104694. doi: 10.1016/j.ijoa.2025.104694. Epub 2025 Jun 2.'}, {'pmid': '31587401', 'type': 'BACKGROUND', 'citation': 'Tsur A, Batsry L, Toussia-Cohen S, Rosenstein MG, Barak O, Brezinov Y, Yoeli-Ullman R, Sivan E, Sirota M, Druzin ML, Stevenson DK, Blumenfeld YJ, Aran D. Development and validation of a machine-learning model for prediction of shoulder dystocia. Ultrasound Obstet Gynecol. 2020 Oct;56(4):588-596. doi: 10.1002/uog.21878.'}, {'pmid': '33713380', 'type': 'BACKGROUND', 'citation': 'Guedalia J, Sompolinsky Y, Novoselsky Persky M, Cohen SM, Kabiri D, Yagel S, Unger R, Lipschuetz M. Prediction of severe adverse neonatal outcomes at the second stage of labour using machine learning: a retrospective cohort study. BJOG. 2021 Oct;128(11):1824-1832. doi: 10.1111/1471-0528.16700. Epub 2021 Apr 15.'}, {'pmid': '37164488', 'type': 'BACKGROUND', 'citation': 'Hamilton EF, Romero R, Tarca AL, Warrick PA. The evolution of the labor curve and its implications for clinical practice: the relationship between cervical dilation, station, and time during labor. Am J Obstet Gynecol. 2023 May;228(5S):S1050-S1062. doi: 10.1016/j.ajog.2022.12.005. Epub 2023 Mar 16.'}, {'pmid': '34235291', 'type': 'BACKGROUND', 'citation': 'Schepman A, Rodway P. Initial validation of the general attitudes towards Artificial Intelligence Scale. Comput Hum Behav Rep. 2020 Jan-Jul;1:100014. doi: 10.1016/j.chbr.2020.100014. Epub 2020 May 18.'}, {'pmid': '24252712', 'type': 'BACKGROUND', 'citation': 'Hollins Martin CJ, Martin CR. Development and psychometric properties of the Birth Satisfaction Scale-Revised (BSS-R). Midwifery. 2014 Jun;30(6):610-9. doi: 10.1016/j.midw.2013.10.006. Epub 2013 Oct 24.'}, {'pmid': '30983383', 'type': 'BACKGROUND', 'citation': 'Skvirsky V, Taubman-Ben-Ari O, Hollins Martin CJ, Martin CR. Validation of the Hebrew Birth Satisfaction Scale - Revised (BSS-R) and its relationship to perceived traumatic labour. J Reprod Infant Psychol. 2020 Apr;38(2):214-220. doi: 10.1080/02646838.2019.1600666. Epub 2019 Apr 13.'}], 'seeAlsoLinks': [{'url': 'https://www.ncbi.nlm.nih.gov/pubmed/38517902', 'label': 'link to pubmed abstract for this pmid 38517902'}, {'url': 'https://www.ncbi.nlm.nih.gov/pubmed/28157275', 'label': 'link to pubmed abstract for this pmid 28157275'}, {'url': 'https://www.ncbi.nlm.nih.gov/pubmed/32434000', 'label': 'link to pubmed abstract for this pmid 32434000'}, {'url': 'https://www.ncbi.nlm.nih.gov/pubmed/35752169', 'label': 'link to pubmed abstract for this pmid 35752169'}, {'url': 'https://www.ncbi.nlm.nih.gov/pubmed/28213060', 'label': 'link to pubmed abstract for this pmid 28213060'}, {'url': 'https://www.ncbi.nlm.nih.gov/pubmed/40527278', 'label': 'link to pubmed abstract for this pmid 40527278'}, {'url': 'https://www.ncbi.nlm.nih.gov/pubmed/31587401', 'label': 'link to pubmed abstract for this pmid 31587401'}, {'url': 'https://www.ncbi.nlm.nih.gov/pubmed/33713380', 'label': 'link to pubmed abstract for this pmid 33713380'}, {'url': 'https://www.ncbi.nlm.nih.gov/pubmed/37164488', 'label': 'link to pubmed abstract for this pmid 37164488'}, {'url': 'https://www.ncbi.nlm.nih.gov/pubmed/34235291', 'label': 'link to pubmed abstract for this pmid 34235291'}, {'url': 'https://www.ncbi.nlm.nih.gov/pubmed/24252712', 'label': 'link to pubmed abstract for this pmid 24252712'}, {'url': 'https://www.ncbi.nlm.nih.gov/pubmed/30983383', 'label': 'link to pubmed abstract for this pmid 30983383'}]}, 'descriptionModule': {'briefSummary': 'ORACLE-AI is a single-center, open-label, randomized clinical trial comparing primiparous women managed with a real-time machine-learning dashboard against a concurrent control group receiving standard intrapartum care. Participants are randomized 1:1 at the onset of labor. The intervention group has the AI dashboard visible in their electronic health record, while the control group does not. The primary hypothesis is that the use of continuous AI-based risk estimates will be non-inferior to standard care in terms of unplanned cesarean\\–delivery rates (uCD), with potential secondary benefits in maternal and neonatal outcomes.'}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': "Inclusion Criteria:\n\n* Age ≥ 18 years at the time of consent\n* Able and willing to provide written informed consent\n* Nulliparous (no prior birth ≥ 24 weeks' gestation)\n* Singleton live pregnancy\n* Cephalic (vertex) fetal presentation\n* Gestational age ≥ 37+0 weeks\n* Admitted to the labor ward in labor (cervical dilation ≥ 3 cm with regular contractions) or undergoing induction or augmentation of labor with intent to proceed to vaginal delivery\n* Planned trial of labor (no scheduled or elective cesarean delivery)\n* Receiving intrapartum care at Hadassah-Hebrew University Medical Center, Mount Scopus campus\n\nExclusion Criteria:\n\n* Planned or elective cesarean delivery prior to labor admission\n* Multifetal gestation\n* Non-cephalic fetal presentation\n* Gestational age \\< 37+0 weeks\n* Major fetal anomaly expected to affect labor or neonatal management\n* Contraindication to vaginal delivery (e.g., placenta previa, invasive placentation, prior uterine surgery precluding labor)\n* Category III fetal heart rate tracing on admission requiring immediate delivery\n* Maternal hemodynamic instability or other life-threatening condition necessitating urgent surgical or critical-care intervention\n* Inability to provide informed consent due to cognitive impairment, intoxication, or other incapacity\n* Concurrent participation in another interventional obstetric study that could confound outcomes or increase risk"}, 'identificationModule': {'nctId': 'NCT07430358', 'acronym': 'ORACLE-AI', 'briefTitle': 'Obstetric Risk Assessment & Cesarean-delivery in Labor Estimation Using Artificial Intelligence', 'organization': {'class': 'OTHER', 'fullName': 'Hadassah Medical Organization'}, 'officialTitle': 'Obstetric Risk Assessment & Cesarean-delivery in Labor Estimation Using Artificial Intelligence Trial (ORACLE-AI)', 'orgStudyIdInfo': {'id': '0335-25- HMO-CTIL'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Dashboard Group', 'description': 'Participants randomized to the intervention arm will receive standard intrapartum obstetric care with the addition of the ORACLE-AI real-time clinical decision-support dashboard.', 'interventionNames': ['Device: Software-based, real-time AI dashboard providing continuous risk estimates for unplanned cesarean delivery during labor.']}, {'type': 'NO_INTERVENTION', 'label': 'Control group', 'description': 'Participants randomized to the control arm will receive standard intrapartum obstetric care'}], 'interventions': [{'name': 'Software-based, real-time AI dashboard providing continuous risk estimates for unplanned cesarean delivery during labor.', 'type': 'DEVICE', 'description': 'The intervention is a software-based, real-time clinical decision-support dashboard (ORACLE-AI) integrated into the electronic health record and used during intrapartum care. The system continuously analyzes admission characteristics and dynamic labor data, including serial cervical examinations, uterine activity, and cardiotocography (CTG) annotations, to generate individualized estimates of the probability of unplanned cesarean delivery. Risk estimates are updated automatically every 5-7 minutes and displayed as a continuous numeric percentage with a graphical time trend and 95% confidence intervals. The dashboard is visible only to the clinical care team and is advisory in nature; it does not provide prescriptive recommendations or automated alerts, and it does not replace clinical judgment. All obstetric management decisions, medications, and procedures follow standard institutional protocols at the discretion of the treating clinicians. No drugs, implants, or additional procedures', 'armGroupLabels': ['Dashboard Group']}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Hadassah Medical Organization', 'class': 'OTHER'}, 'collaborators': [{'name': 'Israel Innovation Authority', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Dr.', 'investigatorFullName': 'Yishai Sompolinsky', 'investigatorAffiliation': 'Hadassah Medical Organization'}}}}