Machine-Learning Prediction of Pregnancy Outcomes After Assisted Reproductive Treatments
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexFemale
Age18-45
SponsorBetaPlus Center for Reproductive Medicine
About this trial
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:
Can 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?
Participants 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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Women who underwent ART procedures (IUI, IVF/ICSI, or FET) using homologous oocytes.
Availability of at least one positive early serum beta-HCG measurement after aspiration, FET, or insemination (beta-HCG >10 mIU/mL).
Availability of the pregnancy outcome.
Availability of relevant patient characteristics and clinical variables required for predictive modeling.
Disqualifiers
Missing beta-HCG measurements critical for model input.
Missing key demographic or clinical variables required for predictive modeling.
Missing ART treatment protocol where required for the model.
Missing pregnancy outcomes.
Trial population
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/ICSI, FET, or IUI.
Trial design
Design model
Cohort
Time perspective
Other
Treatments tested in this trial
Machine-Learning Model for Prediction of Pregnancy Outcomes
Other interventionA 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.
Treatment groups
2,200 Participants
are divided into 1 treatment groupGroup A: ART pregnancies with positive beta-hCG1 intervention
Trial outcomes
Primary outcomes
1
Live birth following ART
Delivery of a live infant at ≥24 weeks of gestation.
Time frame
From positive pregnancy test until delivery.Secondary outcomes
1
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.
Time frame
From the first positive serum beta-hCG measurement until biochemical pregnancy resolution.2
Early Pregnancy Loss following ART
Pregnancy loss occurring before 12 weeks of gestation, excluding biochemical pregnancy, ectopic pregnancy, heterotopic pregnancy, and hydatidiform mole.
Time frame
From clinical pregnancy confirmation until 12 weeks of gestation.Sponsors and contacts
Click on the lead sponsor to view all of their trials.
BetaPlus Center for Reproductive Medicine
Lead sponsor
Clinical Hospital Center Rijeka
Collaborator
Source ClinicalTrials.gov