Overview
The goal of this observational study is to develop and validate a machine-learning model to predict pregnancy outcomes in women who have 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 it aims to answer 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.
Description
Infertility affects more than 15% of the global population, and assisted reproductive technologies (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.
The 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.
Serum beta - human chorionic gonadotropin (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.
We 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).
Patients 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 frozen embryo transfer (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.
ART outcomes were primarily collected through direct patient reporting of events, including adverse pregnancy outcomes or live birth; for patients who did not report their outcomes, clinic staff conducted follow-up calls to ensure complete follow-up.
This study is an observational retrospective-prospective cohort study for development and validation of an AI-based prognostic model predicting live birth after ART.
The retrospective phase will use existing clinical data from patients who underwent assisted reproductive technology (ART) procedures at the BetaPlus Center between January 2017 and 31.12.2025.
The 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.
The 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.
Eligibility
Inclusion Criteria:
- 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.
- For embryo-transfer cycles, availability of information on embryo developmental stage and number of embryos transferred where applicable.
- For the prospective validation cohort, written informed consent.
Exclusion Criteria
- 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.
- 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.
- Non-consent for prospective participation in validation.
