[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100658655":3},{"organization":4,"outcomesModule":7,"designInfo":23,"detailedDescription":25,"studyPopulation":26,"armGroups":27,"interventions":33,"overallOfficials":38,"centralContacts":42,"locations":47,"responsibleParty":78,"collaborators":80,"id":83,"slug":84,"hasResults":85,"nctId":86,"briefTitle":87,"officialTitle":88,"acronym":22,"eligibilityCriteria":89,"healthyVolunteers":85,"sex":90,"minAge":91,"maxAge":92,"enrollmentInfo":93,"targetDuration":22,"studyType":96,"phases":22,"briefSummary":97,"conditions":98,"keywords":103,"overallStatus":50,"whyStopped":22,"lastUpdateSubmitDate":111,"lastUpdatePostDateStruct":112,"startDateStruct":115,"completionDateStruct":117,"leadSponsor":119,"locationsCount":120},{"fullName":5,"class":6},"BetaPlus Center for Reproductive Medicine","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":22},[9],{"measure":10,"description":11,"timeFrame":12},"Live birth following ART","Delivery of a live infant at ≥24 weeks of gestation.","From positive pregnancy test until delivery.",[14,18],{"measure":15,"description":16,"timeFrame":17},"Biochemical pregnancy following ART","A pregnancy characterized by a positive serum beta-hCG test followed by bleeding or pregnancy loss without ultrasound confirmation of a clinical pregnancy.","From the first positive serum beta-hCG measurement until biochemical pregnancy resolution.",{"measure":19,"description":20,"timeFrame":21},"Early Pregnancy Loss following ART","Pregnancy loss occurring before 12 weeks of gestation, excluding biochemical pregnancy, ectopic pregnancy, heterotopic pregnancy, and hydatidiform mole.","From clinical pregnancy confirmation until 12 weeks of gestation.",null,{"allocation":22,"interventionModel":22,"interventionModelDescription":22,"primaryPurpose":22,"observationalModel":24,"timePerspective":6,"maskingInfo":22},"COHORT","Infertility affects more than 15% of the global population, and ART are increasingly used to achieve pregnancy. The field continues to evolve rapidly with ongoing technological and clinical advances. Although these advances have improved ART, success rates continue to vary, contributing to significant psychological distress among patients due to uncertainty regarding outcomes.\n\nThe most intense stress typically occurs after the procedure, especially following a positive pregnancy test and before ultrasound confirmation. This period, which lasts at least 3-4 weeks, is characterized by considerable uncertainty, as neither patients nor healthcare providers have reliable tools to determine pregnancy viability, assess early development, or accurately predict pregnancy progression. An individualized assessment of the probability of live birth after a positive pregnancy test could provide clinically meaningful information during this period and potentially improve counseling and management of patient expectations.\n\nSerum beta-hCG is one of the earliest measurable biomarkers following embryo implantation and has long been recognized as an important prognostic marker of early pregnancy outcome. Both the absolute concentration of beta-HCG and its subsequent rate of increase have been associated with pregnancy viability and live birth following embryo transfer.\n\nInvestigators plan to use a machine-learning system to collect and analyze retrospective clinical data. Beta-HCG values from this dataset were collected at very early stages of pregnancy (around aspiration days +14 and 16, and post-FET days 9 and 11).\n\nPatients were advised to have blood drawn for beta-HCG measurement 14 days after oocyte aspiration or insemination, followed by a second measurement two days later. For FET, measurements were taken 9 days after blastocyst transfer (or the corresponding day for cleavage-stage embryos) and two days afterward. Due to scheduling constraints, such as weekends, actual measurements may have occurred one day earlier or later, or several days apart. The dataset included the exact dates of each blood draw, allowing precise calculation of the interval between measurements for analysis.\n\nART outcomes were primarily collected through direct patient reporting of events, including adverse pregnancy outcomes or live birth; for patients who did not report the outcomes, clinic staff conducted follow-up calls to ensure complete follow-up.\n\nThis study is an observational retrospective-prospective cohort study for development and validation of an AI (artificial intelligence) - based prognostic model predicting live birth after ART.\n\nThe retrospective phase will use existing clinical data from patients who underwent ART procedures at the BetaPlus Center between January 2017 and 31.12.2025.\n\nThe prospective phase will include consecutive patients undergoing ART after model development in both BetaPlus Center and Clinical University Hospital in Rijeka. These patients will be followed to assess pregnancy outcomes, enabling external validation of the model.\n\nThe study is observational in nature. No interventions, treatment modifications, or randomization will be performed as part of this research. Clinical management will follow standard operating procedures (SOP) independent of model predictions.","Participants will be women undergoing ART at the BetaPlus Center and, during the prospective validation phase, at the Clinical University Hospital in Rijeka, Croatia. The study population will include patients undergoing IVF\u002FICSI, FET, or IUI.",[28],{"label":29,"type":22,"description":30,"interventionNames":31},"ART pregnancies with positive beta-hCG","Patients with a positive serum beta-hCG measurement following ART, including IVF, FET, and IUI. Participants will be followed to assess pregnancy outcomes, including live birth, biochemical pregnancy, and early pregnancy loss.",[32],"Other: Machine-Learning Model for Prediction of Pregnancy Outcomes",[34],{"type":6,"name":35,"description":36,"armGroupLabels":37,"otherNames":22},"Machine-Learning Model for Prediction of Pregnancy Outcomes","A machine-learning prognostic model using early serum beta-hCG measurements and patient, embryo, and treatment characteristics to predict live birth and early adverse pregnancy outcomes following assisted reproductive treatment.",[29],[39],{"name":40,"affiliation":5,"role":41},"Romana Dmitrovic","PRINCIPAL_INVESTIGATOR",[43],{"name":40,"role":44,"phone":45,"phoneExt":22,"email":46},"CONTACT","+385981983563","romana@betaplus.hr",[48,65],{"facility":49,"status":50,"city":51,"state":22,"zip":22,"country":52,"countryCode":53,"cosmosGeoPoint":54,"geoPoint":59,"contacts":60},"Clinical Hospital Center","RECRUITING","Rijeka","Croatia","HR",{"type":55,"coordinates":56},"Point",[57,58],14.44241,45.32673,{"lat":58,"lon":57},[61],{"name":62,"role":44,"phone":63,"phoneExt":22,"email":64},"Neda Smiljan Severinski","+385913644239","nedass@medri.uniri.hr",{"facility":5,"status":50,"city":66,"state":22,"zip":67,"country":52,"countryCode":53,"cosmosGeoPoint":68,"geoPoint":72,"contacts":73},"Zagreb","10000",{"type":55,"coordinates":69},[70,71],15.97798,45.81444,{"lat":71,"lon":70},[74],{"name":75,"role":44,"phone":76,"phoneExt":22,"email":77},"Martina Kuzman","+385993765735","martina@betaplus.hr",{"type":79,"investigatorFullName":22,"investigatorTitle":22,"investigatorAffiliation":22,"oldNameTitle":22,"oldOrganization":22},"SPONSOR",[81],{"name":82,"class":6},"Clinical Hospital Center Rijeka","100658655","machine-learning-prediction-of-pregnancy-outcomes-after-assisted-reproductive-treatments-100658655",false,"NCT07855783","Machine-Learning Prediction of Pregnancy Outcomes After Assisted Reproductive Treatments","Development and Validation of a Machine Learning Prognostic Model for Early Prediction of Pregnancy Outcomes Following Assisted Reproductive Treatment: A Retrospective and Prospective Cohort Study","Inclusion Criteria:\n\n* Women who underwent ART procedures (IUI, IVF\u002FICSI, or FET) using homologous oocytes.\n* Availability of at least one positive early serum beta-HCG measurement after aspiration, FET, or insemination (beta-HCG \\>10 mIU\u002FmL).\n* Availability of the pregnancy outcome.\n* Availability of relevant patient characteristics and clinical variables required for predictive modeling.\n* For embryo-transfer cycles, availability of information on embryo developmental stage and number of embryos transferred where applicable.\n* For the prospective validation cohort, written informed consent.\n\nExclusion Criteria\n\n* Missing beta-HCG measurements critical for model input.\n* Missing key demographic or clinical variables required for predictive modeling.\n* Missing ART treatment protocol where required for the model.\n* Missing pregnancy outcomes.\n* Multiple pregnancy, hydatidiform mole, heterotopic pregnancy, and ectopic pregnancy will be excluded from the primary live-birth analysis but will be described separately and, where sample size permits, evaluated as secondary adverse pregnancy outcomes.\n* Non-consent for prospective participation in validation.","FEMALE","18 Years","45 Years",{"count":94,"type":95},2200,"ESTIMATED","OBSERVATIONAL","The goal of this observational study is to develop and validate a machine-learning model to predict pregnancy outcomes in women with a positive pregnancy test following assisted reproductive treatment (ART), with the primary analysis focusing on in vitro fertilization (IVF) and frozen embryo transfer (FET). The study will also include an exploratory analysis of pregnancies following intrauterine insemination (IUI). The main questions addressed by the study are:\n\nCan early blood levels of beta-human chorionic gonadotropin (beta-hCG) predict the likelihood of live birth in an IVF or FET cycle? Can early beta-hCG levels help identify pregnancies at increased risk of biochemical pregnancy or early pregnancy loss?\n\nParticipants will have clinical and treatment information, including beta-hCG measurements and pregnancy outcomes, collected from medical records or during prospective follow-up. No changes to participants' medical treatment will be made as part of the study.",[99,100,101,102],"IVF Outcomes","Prediction Models","Infertility Assisted Reproductive Technology","Live Birth",[104,105,106,107,108,109,110],"Prediction model","Machine learning","Biochemical pregnancy","Early pregnancy loss","Pregnancy outcome","Beta-hCG","Assisted reproductive technology","2026-10-03",{"date":113,"type":114},"2026-10-07","ACTUAL",{"date":116,"type":114},"2026-01-01",{"date":118,"type":95},"2027-09-30",{"name":5,"class":6},2]