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

purposeTo develop a stage-structured, distribution-based prediction framework for in vitro fertilization (IVF) that generates full probability distributions at each treatment stage and enables conditional updating of downstream predictions when observed outcomes become known.

methodsWe conducted an observational modeling study using de-identified UK Human Fertilisation and Embryology Authority (HFEA) registry data (2017-2018; up to 101,217 model-ready cycles after stage-specific filtering) to model egg retrieval, maturation, and fertilization. Egg retrieval was modeled using a zero-inflated negative binomial specification. Downstream transitions such as blastocyst formation, euploidy, vitrification survival, and live birth after euploid transfer were modeled using stage-specific logistic regressions calibrated to published cohorts and national registry summaries (429,507 additional observations). Predictive performance of HFEA-derived models was evaluated on held-out HFEA test sets using point-prediction accuracy, train-test gaps, prediction-interval coverage, and calibration across predicted outcome strata.

resultsThe framework propagates full probability distributions across sequential IVF stages rather than point estimates. Held-out HFEA validation showed minimal train-test degradation ( CONCLUSION(S): A sequential, distribution-based IVF prediction model with conditional updating provides uncertainty-quantified, stage-aware predictions that dynamically adapt to patient-specific outcomes, supporting more individualized counseling and treatment planning.