Journal of assisted reproduction and genetics · 2026
Stage-structured, distributional prediction of IVF outcomes with conditional updating.
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Craig Alexander, Wartschinski Laura, Eyre Mathew, Davidson Ivan, Christensen Michael, Wolfram Tobias
Аннотация
PURPOSE: To 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.
METHODS: We 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.
RESULTS: The framework propagates full probability distributions across sequential IVF stages rather than point estimates. Held-out HFEA validation showed minimal train-test degradation ( R 2 gaps under 0.007), with modest expected-count accuracy for egg retrieval and stronger expected-count accuracy for maturity and fertilization. Egg-retrieval prediction intervals showed near-nominal coverage (50%: 50.2%; 80%: 79.2%; 95%: 94.9%), and observed mean outcomes were close to predicted means across predicted-yield/rate strata. When observed stage outcomes were entered, downstream distributions updated appropriately, reducing uncertainty and preserving cycle-specific biological parameters in both single- and multi-cycle scenarios.
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.
METHODS: We 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.
RESULTS: The framework propagates full probability distributions across sequential IVF stages rather than point estimates. Held-out HFEA validation showed minimal train-test degradation ( R 2 gaps under 0.007), with modest expected-count accuracy for egg retrieval and stronger expected-count accuracy for maturity and fertilization. Egg-retrieval prediction intervals showed near-nominal coverage (50%: 50.2%; 80%: 79.2%; 95%: 94.9%), and observed mean outcomes were close to predicted means across predicted-yield/rate strata. When observed stage outcomes were entered, downstream distributions updated appropriately, reducing uncertainty and preserving cycle-specific biological parameters in both single- and multi-cycle scenarios.
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.