Stage-structured, distributional prediction of IVF outcomes with conditional updating

Area of Science:

• Reproductive medicine
• Biostatistics
• In vitro fertilization (IVF) modeling

Background:

• In vitro fertilization (IVF) involves multiple sequential stages, each with inherent uncertainties affecting overall success rates.
• Accurate prediction of outcomes at each stage is crucial for effective patient counseling and treatment planning.

Purpose of the Study:

• To develop a novel stage-structured, distribution-based prediction framework for IVF.
• To enable conditional updating of downstream predictions based on observed outcomes at preceding stages.

Main Methods:

• An observational modeling study utilized UK Human Fertilisation and Embryology Authority (HFEA) registry data (2017-2018).
• Modeled egg retrieval, maturation, and fertilization using zero-inflated negative binomial and logistic regressions.
• Calibrated downstream transitions (blastocyst formation, euploidy, etc.) using published cohorts and registry summaries.

Main Results:

• The framework generates full probability distributions across sequential IVF stages, outperforming point estimates.
• Held-out validation demonstrated minimal performance degradation and accurate prediction intervals for egg retrieval.
• Downstream predictions dynamically updated with observed outcomes, reducing uncertainty and preserving patient-specific parameters.

Conclusions:

• A sequential, distribution-based IVF prediction model offers uncertainty-quantified, stage-aware predictions.
• The model dynamically adapts to patient-specific outcomes, improving individualized counseling.
• This approach supports more informed treatment planning in assisted reproductive technologies.