Development and external validation of a three-stage model to predict live birth after fresh IVF/ICSI embryo transfer

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Background Predicting live birth outcomes following in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) remains challenging. Most existing models are typically limited to single time-point data and lack integration of sequential clinical information, constraining their utility for personalized decision-making. Methods This retrospective multicenter study included 8,389 fresh IVF/ICSI cycles for model development and internal validation, with 2,058 cycles for independent external validation. We constructed prediction models at three sequential clinical stages: Stage 1 (baseline), Stage 2 (ovarian stimulation), and Stage 3 (embryo transfer). We compared 10 base machine learning models and 3 ensemble methods (SES, Stacking, DEP). Performance was evaluated using area under the receiver operating characteristic curve (AUC-ROC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration, and decision curve analysis (DCA). Model interpretability was assessed via SHapley Additive exPlanations (SHAP) analysis and restricted cubic splines (RCS). Results The three-stage sequential model showed stepwise improved predictive performance. In internal validation, the SES ensemble achieved AUC increases from 0.699 (Stage 1) to 0.754 (Stage 3). In the test cohort, AUC improved significantly from 0.678 at baseline to 0.725 after ovarian stimulation (NRI = 0.630, IDI = 0.072, P<0.001), with a modest further increase to 0.731 at the embryo development stage. External validation yielded moderate discriminative performance (Stage 3 AUC = 0.719). DCA showed favorable clinical net benefit. SHAP analysis identified female age and high-quality embryos transferred as the dominant predictors. RCS confirmed nonlinear associations of female age, ovarian reserve, endometrial thickness, oocyte yield and embryo quantity metrics with live birth. Conclusions This three-stage sequential, externally validated model provides reliable and interpretable live birth prediction at key decision points during fresh IVF/ICSI treatment. It may support personalized pretreatment counseling. However, prospective validation and predefined clinical thresholds are required prior to routine clinical decision-making.

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